phangorn/0000755000175100001440000000000013020771374012110 5ustar hornikusersphangorn/inst/0000755000175100001440000000000013020643153013056 5ustar hornikusersphangorn/inst/CITATION0000644000175100001440000000114612761653173014233 0ustar hornikuserscitHeader("To cite phangorn in a publication use:") citEntry(entry="Article", title = "phangorn: phylogenetic analysis in R", author = personList(as.person("K.P. Schliep")), journal = "Bioinformatics", year = "2011", volume = "27", number = "4", pages = "592--593", textVersion = "Schliep K.P. 2011. phangorn: phylogenetic analysis in R. Bioinformatics, 27(4) 592-593") citFooter("As phangorn is evolving quickly, you may want to cite also its version number (found with 'library(help = phangorn)').") if(!exists("meta") || is.null(meta)) meta <- packageDescription("phangorn") phangorn/inst/README0000644000175100001440000000123212761653173013752 0ustar hornikusersThe following persons and institutions helped in the development of phangorn at one stage or another. Emmanuel Paradis and all the other authors of the APE package. Tim White for provinding some C-code to compute the Hadamard distances. Bennet McComish for providing the allTrees function. Francois-Joiseph Lapointe for feedback on clans and clips. Further thanks to all the user that send bug reports/fixes and have helped to improve this package. Financial support was provided by the Alan Wilson Centre of Molecular Ecology and Evolution and the Muséum national d'Histoire naturelle and Universidade de Vigo. phangorn/inst/extdata/0000755000175100001440000000000012761653173014526 5ustar hornikusersphangorn/inst/extdata/Blosum62.dat0000644000175100001440000000561012761653173016633 0ustar hornikusers 0.735790389698 0.485391055466 1.297446705134 0.543161820899 0.500964408555 3.180100048216 1.45999531047 0.227826574209 0.397358949897 0.240836614802 1.199705704602 3.020833610064 1.839216146992 1.190945703396 0.32980150463 1.1709490428 1.36057419042 1.24048850864 3.761625208368 0.140748891814 5.528919177928 1.95588357496 0.418763308518 1.355872344485 0.798473248968 0.418203192284 0.609846305383 0.423579992176 0.716241444998 1.456141166336 2.414501434208 0.778142664022 0.354058109831 2.43534113114 1.626891056982 0.539859124954 0.605899003687 0.232036445142 0.283017326278 0.418555732462 0.774894022794 0.236202451204 0.186848046932 0.189296292376 0.252718447885 0.800016530518 0.622711669692 0.211888159615 0.218131577594 0.831842640142 0.580737093181 0.372625175087 0.217721159236 0.348072209797 3.890963773304 1.295201266783 5.411115141489 1.593137043457 1.032447924952 0.285078800906 3.945277674515 2.802427151679 0.752042440303 1.022507035889 0.406193586642 0.445570274261 1.253758266664 0.983692987457 0.648441278787 0.222621897958 0.76768882348 2.494896077113 0.55541539747 0.459436173579 0.984311525359 3.364797763104 6.030559379572 1.073061184332 0.492964679748 0.371644693209 0.354861249223 0.281730694207 0.441337471187 0.14435695975 0.291409084165 0.368166464453 0.714533703928 1.517359325954 2.064839703237 0.266924750511 1.77385516883 1.173275900924 0.448133661718 0.494887043702 0.730628272998 0.356008498769 0.858570575674 0.926563934846 0.504086599527 0.527007339151 0.388355409206 0.374555687471 1.047383450722 0.454123625103 0.233597909629 4.325092687057 1.12278310421 2.904101656456 1.582754142065 1.197188415094 1.934870924596 1.769893238937 1.509326253224 1.11702976291 0.35754441246 0.352969184527 1.752165917819 0.918723415746 0.540027644824 1.169129577716 1.729178019485 0.914665954563 1.898173634533 0.934187509431 1.119831358516 1.277480294596 1.071097236007 0.641436011405 0.585407090225 1.17909119726 0.915259857694 1.303875200799 1.488548053722 0.488206118793 1.005451683149 5.15155629227 0.465839367725 0.426382310122 0.191482046247 0.145345046279 0.527664418872 0.758653808642 0.407635648938 0.508358924638 0.30124860078 0.34198578754 0.6914746346 0.332243040634 0.888101098152 2.074324893497 0.252214830027 0.387925622098 0.513128126891 0.718206697586 0.720517441216 0.538222519037 0.261422208965 0.470237733696 0.95898974285 0.596719300346 0.308055737035 4.218953969389 0.674617093228 0.811245856323 0.7179934869 0.951682162246 6.747260430801 0.369405319355 0.796751520761 0.801010243199 4.054419006558 2.187774522005 0.438388343772 0.312858797993 0.258129289418 1.116352478606 0.530785790125 0.524253846338 0.25334079019 0.20155597175 8.311839405458 2.231405688913 0.498138475304 2.575850755315 0.838119610178 0.496908410676 0.561925457442 2.253074051176 0.266508731426 1 0.074 0.052 0.045 0.054 0.025 0.034 0.054 0.074 0.026 0.068 0.099 0.058 0.025 0.047 0.039 0.057 0.051 0.013 0.032 0.073 phangorn/inst/extdata/RtREV.dat0000644000175100001440000000136212761653173016164 0ustar hornikusers 34 51 35 10 30 384 439 92 128 1 32 221 236 78 70 81 10 79 542 1 372 135 41 94 61 48 18 70 30 90 320 91 124 387 34 68 1 24 35 1 104 33 1 1 34 45 18 15 5 110 54 21 3 51 385 38 593 123 20 16 309 141 30 76 34 23 235 57 1 1 156 158 1 37 116 375 581 134 1 7 49 1 70 1 1 7 141 64 179 14 247 97 24 33 55 1 68 52 17 44 10 22 43 1 11 460 102 294 136 75 225 95 152 183 4 24 77 1 20 134 258 64 148 55 117 146 82 7 49 72 25 110 131 69 62 671 5 13 16 1 55 10 17 23 48 39 47 6 111 182 9 14 1 55 47 28 1 131 45 1 21 307 26 64 1 74 1017 14 31 34 176 197 29 21 6 295 36 35 3 1 1048 112 19 236 92 25 39 196 26 59 0.0646 0.0453 0.0376 0.0422 0.0114 0.0606 0.0607 0.0639 0.0273 0.0679 0.1018 0.0751 0.015 0.0287 0.0681 0.0488 0.0622 0.0251 0.0318 0.0619 phangorn/inst/extdata/MtZoa.dat0000644000175100001440000000261312761653173016254 0ustar hornikusers 3.3 1.7 33.6 16.1 3.2 617.0 272.5 61.1 94.6 9.5 7.3 231.0 190.3 19.3 49.1 17.1 6.4 174.0 883.6 3.4 349.4 289.3 7.2 99.3 26.0 82.4 8.9 43.1 2.3 61.7 228.9 55.6 37.5 421.8 14.9 7.4 33.2 0.2 24.3 1.5 48.8 0.2 7.3 3.4 1.6 15.6 4.1 7.9 0.5 59.7 23.0 1.0 3.5 6.6 425.2 0.2 292.3 413.4 0.2 0.2 334.0 163.2 10.1 23.9 8.4 6.7 136.5 3.8 73.7 0.2 264.8 83.9 0.2 52.2 7.1 449.7 636.3 83.0 26.5 0.2 12.9 2.0 167.8 9.5 0.2 5.8 13.1 90.3 234.2 16.3 215.6 61.8 7.5 22.6 0.2 8.1 52.2 20.6 1.3 15.6 2.6 11.4 24.3 5.4 10.5 644.9 11.8 420.2 51.4 656.3 96.4 38.4 257.1 23.1 7.2 15.2 144.9 95.3 32.2 79.7 378.1 3.2 184.6 2.3 199.0 39.4 34.5 5.2 19.4 222.3 50.0 75.5 305.1 19.3 56.9 666.3 3.1 16.9 6.4 0.2 36.1 6.1 3.5 12.3 4.5 9.7 27.2 6.6 48.7 58.2 1.3 10.3 3.6 2.1 13.8 141.6 13.9 76.7 52.3 10.0 4.3 266.5 13.1 5.7 45.0 41.4 590.5 4.2 29.7 29.0 79.8 321.9 5.1 7.1 3.7 243.8 9.0 16.3 23.7 0.3 1710.6 126.1 11.1 279.6 59.6 17.9 49.5 396.4 13.7 15.6 0.068880 0.021037 0.030390 0.020696 0.009966 0.018623 0.024989 0.071968 0.026814 0.085072 0.156717 0.019276 0.050652 0.081712 0.044803 0.080535 0.056386 0.027998 0.037404 0.066083 Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val // end of file Rota-Stabelli, O., Z. Yang, and M. Telford. 2009. MtZoa: a general mitochondrial amino acid substitutions model for animal evolutionary studies. Mol. Phyl. Evol. phangorn/inst/extdata/wag.dat0000644000175100001440000000577712761653173016016 0ustar hornikusers0.551571 0.509848 0.635346 0.738998 0.147304 5.429420 1.027040 0.528191 0.265256 0.0302949 0.908598 3.035500 1.543640 0.616783 0.0988179 1.582850 0.439157 0.947198 6.174160 0.021352 5.469470 1.416720 0.584665 1.125560 0.865584 0.306674 0.330052 0.567717 0.316954 2.137150 3.956290 0.930676 0.248972 4.294110 0.570025 0.249410 0.193335 0.186979 0.554236 0.039437 0.170135 0.113917 0.127395 0.0304501 0.138190 0.397915 0.497671 0.131528 0.0848047 0.384287 0.869489 0.154263 0.0613037 0.499462 3.170970 0.906265 5.351420 3.012010 0.479855 0.0740339 3.894900 2.584430 0.373558 0.890432 0.323832 0.257555 0.893496 0.683162 0.198221 0.103754 0.390482 1.545260 0.315124 0.174100 0.404141 4.257460 4.854020 0.934276 0.210494 0.102711 0.0961621 0.0467304 0.398020 0.0999208 0.0811339 0.049931 0.679371 1.059470 2.115170 0.088836 1.190630 1.438550 0.679489 0.195081 0.423984 0.109404 0.933372 0.682355 0.243570 0.696198 0.0999288 0.415844 0.556896 0.171329 0.161444 3.370790 1.224190 3.974230 1.071760 1.407660 1.028870 0.704939 1.341820 0.740169 0.319440 0.344739 0.967130 0.493905 0.545931 1.613280 2.121110 0.554413 2.030060 0.374866 0.512984 0.857928 0.822765 0.225833 0.473307 1.458160 0.326622 1.386980 1.516120 0.171903 0.795384 4.378020 0.113133 1.163920 0.0719167 0.129767 0.717070 0.215737 0.156557 0.336983 0.262569 0.212483 0.665309 0.137505 0.515706 1.529640 0.139405 0.523742 0.110864 0.240735 0.381533 1.086000 0.325711 0.543833 0.227710 0.196303 0.103604 3.873440 0.420170 0.398618 0.133264 0.428437 6.454280 0.216046 0.786993 0.291148 2.485390 2.006010 0.251849 0.196246 0.152335 1.002140 0.301281 0.588731 0.187247 0.118358 7.821300 1.800340 0.305434 2.058450 0.649892 0.314887 0.232739 1.388230 0.365369 0.314730 0.0866279 0.043972 0.0390894 0.0570451 0.0193078 0.0367281 0.0580589 0.0832518 0.0244313 0.048466 0.086209 0.0620286 0.0195027 0.0384319 0.0457631 0.0695179 0.0610127 0.0143859 0.0352742 0.0708956 A R N D C Q E G H I L K M F P S T W Y V Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val #Symmetrical part of the WAG rate matrix and aa frequencies, #estimated from 3905 globular protein amino acid sequences forming 182 #protein families. #The first part above indicates the symmetric 'exchangeability' #parameters, where s_ij = s_ji. The s_ij above are not scaled, but the #PAML package will perform this scaling. #The second part gives the amino acid frequencies (pi_i) #estimated from the 3905 sequences. The net replacement rate from i to #j is Q_ij = s_ij*pi_j. #Prepared by Simon Whelan and Nick Goldman, December 2000. # #Citation: #Whelan, S. and N. Goldman. 2001. A general empirical model of #protein evolution derived from multiple protein families using #a maximum likelihood approach. Molecular Biology and #Evolution 18:691-699. phangorn/inst/extdata/jtt-dcmut.dat0000644000175100001440000000564612761653173017146 0ustar hornikusers0.531678 0.557967 0.451095 0.827445 0.154899 5.549530 0.574478 1.019843 0.313311 0.105625 0.556725 3.021995 0.768834 0.521646 0.091304 1.066681 0.318483 0.578115 7.766557 0.053907 3.417706 1.740159 1.359652 0.773313 1.272434 0.546389 0.231294 1.115632 0.219970 3.210671 4.025778 1.032342 0.724998 5.684080 0.243768 0.201696 0.361684 0.239195 0.491003 0.115968 0.150559 0.078270 0.111773 0.053769 0.181788 0.310007 0.372261 0.137289 0.061486 0.164593 0.709004 0.097485 0.069492 0.540571 2.335139 0.369437 6.529255 2.529517 0.282466 0.049009 2.966732 1.731684 0.269840 0.525096 0.202562 0.146481 0.469395 0.431045 0.330720 0.190001 0.409202 0.456901 0.175084 0.130379 0.329660 4.831666 3.856906 0.624581 0.138293 0.065314 0.073481 0.032522 0.678335 0.045683 0.043829 0.050212 0.453428 0.777090 2.500294 0.024521 0.436181 1.959599 0.710489 0.121804 0.127164 0.123653 1.608126 0.191994 0.208081 1.141961 0.098580 1.060504 0.216345 0.164215 0.148483 3.887095 1.001551 5.057964 0.589268 2.155331 0.548807 0.312449 1.874296 0.743458 0.405119 0.592511 0.474478 0.285564 0.943971 2.788406 4.582565 0.650282 2.351311 0.425159 0.469823 0.523825 0.331584 0.316862 0.477355 2.553806 0.272514 0.965641 2.114728 0.138904 1.176961 4.777647 0.084329 1.257961 0.027700 0.057466 1.104181 0.172206 0.114381 0.544180 0.128193 0.134510 0.530324 0.089134 0.201334 0.537922 0.069965 0.310927 0.080556 0.139492 0.235601 0.700693 0.453952 2.114852 0.254745 0.063452 0.052500 5.848400 0.303445 0.241094 0.087904 0.189870 5.484236 0.113850 0.628608 0.201094 0.747889 2.924161 0.171995 0.164525 0.315261 0.621323 0.179771 0.465271 0.470140 0.121827 9.533943 1.761439 0.124066 3.038533 0.593478 0.211561 0.408532 1.143980 0.239697 0.165473 0.076862 0.051057 0.042546 0.051269 0.020279 0.041061 0.061820 0.074714 0.022983 0.052569 0.091111 0.059498 0.023414 0.040530 0.050532 0.068225 0.058518 0.014336 0.032303 0.066374 A R N D C Q E G H I L K M F P S T W Y V Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val #JTT rate matrix prepared using the DCMut method* #------------------------------------------------ # #The first part above indicates the symmetric 'exchangeability' parameters s_ij, #where s_ij = s_ji. #The second part gives the amino acid equilibrium frequencies pi_i. #The net replacement rate from i to j is q_ij = pi_j*s_ij. # #This model is usually scaled so that the mean rate of change at #equilibrium, Sum_i Sum_j!=i pi_i*q_ij, equals 1. You should check this #scaling before using the matrix above. The PAML package will perform #this scaling. # #// # #*Prepared by Carolin Kosiol and Nick Goldman, December 2003. # #See the following paper for more details: #Kosiol, C., and Goldman, N. 2005. Different versions of the Dayhoff rate matrix. #Molecular Biology and Evolution 22:193-199. # #See also http://www.ebi.ac.uk/goldman/dayhoff phangorn/inst/extdata/HIVw.dat0000644000175100001440000000317512761653173016043 0ustar hornikusers 0.0744808 0.617509 0.16024 4.43521 0.0674539 29.4087 0.167653 2.86364 0.0604932 0.005 0.005 10.6746 0.342068 0.005 0.005 5.56325 0.0251632 0.201526 12.1233 0.005 3.20656 1.8685 13.4379 0.0604932 10.3969 0.0489798 0.0604932 14.7801 0.005 6.84405 8.59876 2.31779 0.005 18.5465 0.005 0.005 0.005 1.34069 0.987028 0.145124 0.005 0.0342252 0.0390512 0.005 0.005 0.16024 0.586757 0.005 0.005 0.005 2.89048 0.129839 0.0489798 1.76382 9.10246 0.592784 39.8897 10.6655 0.894313 0.005 13.0705 23.9626 0.279425 0.22406 0.817481 0.005 0.005 3.28652 0.201526 0.005 0.005 0.005 0.005 0.0489798 0.005 17.3064 11.3839 4.09564 0.597923 0.005 0.005 0.005 0.362959 0.005 0.005 0.005 0.005 1.48288 7.48781 0.005 0.005 1.00981 0.404723 0.344848 0.005 0.005 3.04502 0.005 0.005 13.9444 0.005 9.83095 0.111928 0.005 0.0342252 8.5942 8.35024 14.5699 0.427881 1.12195 0.16024 0.005 6.27966 0.725157 0.740091 6.14396 0.005 0.392575 4.27939 14.249 24.1422 0.928203 4.54206 0.630395 0.005 0.203091 0.458743 0.0489798 0.95956 9.36345 0.005 4.04802 7.41313 0.114512 4.33701 6.34079 0.005 5.96564 0.005 0.005 5.49894 0.0443298 0.005 2.8258 0.005 0.005 1.37031 0.005 0.005 0.005 0.005 1.10156 0.005 0.005 0.005 5.06475 2.28154 8.34835 0.005 0.005 0.005 47.4889 0.114512 0.005 0.005 0.579198 4.12728 0.005 0.933142 0.490608 0.005 24.8094 0.279425 0.0744808 2.91786 0.005 0.005 2.19952 2.79622 0.827479 24.8231 2.95344 0.128065 14.7683 2.28 0.005 0.862637 0.005 0.005 1.35482 0.0377494 0.057321 0.0891129 0.0342034 0.0240105 0.0437824 0.0618606 0.0838496 0.0156076 0.0983641 0.0577867 0.0641682 0.0158419 0.0422741 0.0458601 0.0550846 0.0813774 0.019597 0.0205847 0.0515639 phangorn/inst/extdata/VT.dat0000644000175100001440000000353212761653173015554 0ustar hornikusers 0.233108 0.199097 0.210797 0.265145 0.105191 0.883422 0.227333 0.031726 0.027495 0.010313 0.310084 0.493763 0.2757 0.205842 0.004315 0.567957 0.25524 0.270417 1.599461 0.005321 0.960976 0.876213 0.156945 0.362028 0.311718 0.050876 0.12866 0.250447 0.078692 0.213164 0.290006 0.134252 0.016695 0.315521 0.104458 0.058131 0.222972 0.08151 0.087225 0.01172 0.046398 0.054602 0.046589 0.051089 0.020039 0.42463 0.192364 0.069245 0.060863 0.091709 0.24353 0.151924 0.087056 0.103552 2.08989 0.393245 1.755838 0.50306 0.261101 0.004067 0.738208 0.88863 0.193243 0.153323 0.093181 0.201204 0.21155 0.08793 0.05742 0.012182 0.02369 0.120801 0.058643 0.04656 0.021157 0.493845 1.105667 0.096474 0.116646 0.042569 0.039769 0.016577 0.051127 0.026235 0.028168 0.050143 0.079807 0.32102 0.946499 0.038261 0.173052 0.399143 0.12848 0.083956 0.160063 0.011137 0.15657 0.205134 0.124492 0.078892 0.054797 0.169784 0.212302 0.010363 0.042564 1.817198 0.292327 0.847049 0.461519 0.17527 0.358017 0.406035 0.612843 0.167406 0.081567 0.214977 0.400072 0.090515 0.138119 0.430431 0.877877 0.204109 0.471268 0.178197 0.079511 0.248992 0.321028 0.136266 0.101117 0.376588 0.243227 0.446646 0.184609 0.08587 0.207143 1.767766 0.030309 0.046417 0.010459 0.011393 0.007732 0.021248 0.018844 0.02399 0.020009 0.034954 0.083439 0.023321 0.022019 0.12805 0.014584 0.035933 0.020437 0.087061 0.09701 0.093268 0.051664 0.042823 0.062544 0.0552 0.037568 0.286027 0.086237 0.189842 0.068689 0.073223 0.898663 0.032043 0.121979 0.094617 0.124746 1.230985 0.113146 0.049824 0.048769 0.163831 0.112027 0.205868 0.082579 0.068575 3.65443 1.337571 0.144587 0.307309 0.247329 0.129315 0.1277 0.740372 0.022134 0.125733 0.078837 0.051238 0.042313 0.053066 0.015175 0.036713 0.061924 0.070852 0.023082 0.062056 0.096371 0.057324 0.023771 0.043296 0.043911 0.063403 0.055897 0.013272 0.034399 0.073101 phangorn/inst/extdata/mtArt.dat0000644000175100001440000001142412761653173016311 0ustar hornikusers 0.2 0.2 0.2 1 4 500 254 36 98 11 0.2 154 262 0.2 0.2 0.2 0.2 183 862 0.2 262 200 0.2 121 12 81 3 44 0.2 41 180 0.2 12 314 15 0.2 26 2 21 7 63 11 7 3 0.2 4 2 13 1 79 16 2 1 6 515 0.2 209 467 2 0.2 349 106 0.2 0.2 3 4 121 5 79 0.2 312 67 0.2 56 0.2 515 885 106 13 5 20 0.2 184 0.2 0.2 1 14 118 263 11 322 49 0.2 17 0.2 0.2 39 8 0.2 1 0.2 12 17 5 15 673 3 398 44 664 52 31 226 11 7 8 144 112 36 87 244 0.2 166 0.2 183 44 43 0.2 19 204 48 70 289 14 47 660 0.2 0.2 8 0.2 22 7 11 2 0.2 0.2 21 16 71 54 0.2 2 0.2 1 4 251 0.2 72 87 8 9 191 12 20 117 71 792 18 30 46 38 340 0.2 23 0.2 350 0.2 14 3 0.2 1855 85 26 281 52 32 61 544 0.2 2 0.054116 0.018227 0.039903 0.020160 0.009709 0.018781 0.024289 0.068183 0.024518 0.092638 0.148658 0.021718 0.061453 0.088668 0.041826 0.091030 0.049194 0.029786 0.039443 0.057700 // this is the end of the file. The rest are notes. Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val This model has been derived from 36 artropoda mitochondrial genomes. Each gene of the given species was aligned individually. Then, alignments of the whole set of 13 genes where concatenated and passed through GBlocks (Castresana, 2000, in JME) with parameters and output: Minimum Number Of Sequences For A Conserved Position: 20 Minimum Number Of Sequences For A Flanking Position: 32 Maximum Number Of Contiguous Nonconserved Positions: 8 Minimum Length Of A Block: 10 Allowed Gap Positions: With Half Use Similarity Matrices: Yes Flank positions of the 40 selected block(s) Flanks: [6 22] [26 44] [61 70] [77 143] [145 185] [208 236] [309 640] [644 802] [831 941] [956 966] [973 1062] [1085 1339] [1343 1702] [1754 1831] [1840 1911] [1916 1987] [2011 2038] [2097 2118] [2125 2143] [2179 2215] [2243 2268] [2277 2288] [2333 2347] [2476 2518] [2539 2558] [2600 2613] [2637 2672] [2738 2759] [2784 2839] [2882 2924] [2948 3097] [3113 3123] [3210 3235] [3239 3322] [3348 3392] [3406 3526] [3588 3617] [3660 3692] [3803 3830] [3909 3928] New number of positions in MtArt-strict.phy.fasta-gb: 2664 (67% of the original 3933 positions) The species included in the analysis were: Harpiosquilla harpax [NCBI_TaxID 287944] Ixodes uriae [NCBI_TaxID 59655] Heptathela hangzhouensis [NCBI_TaxID 216259] Triops longicaudatus [NCBI_TaxID 58777] Gryllotalpa orientalis [NCBI_TaxID 213494] lepidopsocid RS-2001 [NCBI_TaxID 159971] Locusta migratoria [NCBI_TaxID 7004] Drosophila yakuba [NCBI_TaxID 7245] Ostrinia furnacalis [NCBI_TaxID 93504] Megabalanus volcano [NCBI_TaxID 266495] Periplaneta fuliginosa [NCBI_TaxID 36977] Thermobia domestica [NCBI_TaxID 89055] Aleurochiton aceris [NCBI_TaxID 266942] Schizaphis graminum [NCBI_TaxID 13262] Pteronarcys princeps [NCBI_TaxID 285953] Aleurodicus dugesii [NCBI_TaxID 30099] Pollicipes polymerus [NCBI_TaxID 36137] Gomphiocephalus hodgsoni [NCBI_TaxID 221270] Habronattus oregonensis [NCBI_TaxID 130930] Speleonectes tulumensis [NCBI_TaxID 84346] Hutchinsoniella macracantha [NCBI_TaxID 84335] Haemaphysalis flava [NCBI_TaxID 181088] Scutigera coleoptrata [NCBI_TaxID 29022] Vargula hilgendorfii [NCBI_TaxID 6674] Tricholepidion gertschi [NCBI_TaxID 89825] Varroa destructor [NCBI_TaxID 109461] Bombyx mandarina [NCBI_TaxID 7092] Thyropygus sp. [NCBI_TaxID 174155] Tribolium castaneum [NCBI_TaxID 7070] Pagurus longicarpus [NCBI_TaxID 111067] Limulus polyphemus [NCBI_TaxID 6850] Tetrodontophora bielanensis [NCBI_TaxID 48717] Penaeus monodon [NCBI_TaxID 6687] Daphnia pulex [NCBI_TaxID 6669] Apis mellifera [NCBI_TaxID 7469] Anopheles gambiae [NCBI_TaxID 7165] The topology used for inferring the model was: (((Daph_pulex,Trio_longi),((((((Aleu_aceri,Aleu_duges),Schi_grami),lepi_RS_20), ((((Ostr_furna,Bomb_manda),(Dros_yakub,Anop_gambi)),Apis_melli),Trib_casta)), ((Gryl_orien,Locu_migra),(Pter_princ,Peri_fulig))),(Tric_gerts,Ther_domes)), (Scut_coleo,Thyr_sp),Varg_hilge,Hutc_macra,((((Ixod_uriae,Haem_flava),Varr_destr), (Habr_orego,Hept_hangz)),Limu_polyp),(Poll_polym,Mega_volca),(Gomp_hodgs,Tetr_biela), ((Pagu_longi,Pena_monod),Harp_harpa),Spel_tulum)); Note this is not the ML topology but the consensus one (based on morphological data, phylogenetic reconstruction using nuclear genes, etc). Where relationships are not clear, a polytomy was introduced (it contains quite a lot of polytomies!). The model was estimated using Ziheng Yang's Paml software package. A four-categorized gamma distribution was used to account for heterogeneity (alpha was estimated to be 0.47821). Sites with ambiguity data were taken into account. Reference Abascal, F., D. Posada, and R. Zardoya. 2007. MtArt: A new Model of amino acid replacement for Arthropoda. Mol. Biol. Evol. 24:1-5. phangorn/inst/extdata/HIVb.dat0000644000175100001440000000337412761653173016017 0ustar hornikusers 0.307507 0.005 0.295543 1.45504 0.005 17.6612 0.123758 0.351721 0.0860642 0.005 0.0551128 3.4215 0.672052 0.005 0.005 1.48135 0.0749218 0.0792633 10.5872 0.005 2.5602 2.13536 3.65345 0.323401 2.83806 0.897871 0.0619137 3.92775 0.0847613 9.04044 7.64585 1.9169 0.240073 7.05545 0.11974 0.005 0.005 0.677289 0.680565 0.0176792 0.005 0.005 0.00609079 0.005 0.103111 0.215256 0.701427 0.005 0.00876048 0.129777 1.49456 0.005 0.005 1.74171 5.95879 0.005 20.45 7.90443 0.005 0.005 6.54737 4.61482 0.521705 0.005 0.322319 0.0814995 0.0186643 2.51394 0.005 0.005 0.005 0.303676 0.175789 0.005 0.005 11.2065 5.31961 1.28246 0.0141269 0.005 0.005 0.005 9.29815 0.005 0.005 0.291561 0.145558 3.39836 8.52484 0.0342658 0.188025 2.12217 1.28355 0.00739578 0.0342658 0.005 4.47211 0.0120226 0.005 2.45318 0.0410593 2.07757 0.0313862 0.005 0.005 2.46633 3.4791 13.1447 0.52823 4.69314 0.116311 0.005 4.38041 0.382747 1.21803 0.927656 0.504111 0.005 0.956472 5.37762 15.9183 2.86868 6.88667 0.274724 0.739969 0.243589 0.289774 0.369615 0.711594 8.61217 0.0437673 4.67142 4.94026 0.0141269 2.01417 8.93107 0.005 0.991338 0.005 0.005 2.63277 0.026656 0.005 1.21674 0.0695179 0.005 0.748843 0.005 0.089078 0.829343 0.0444506 0.0248728 0.005 0.005 0.00991826 1.76417 0.674653 7.57932 0.113033 0.0792633 0.005 18.6943 0.148168 0.111986 0.005 0.005 15.34 0.0304381 0.648024 0.105652 1.28022 7.61428 0.0812454 0.026656 1.04793 0.420027 0.0209153 1.02847 0.953155 0.005 17.7389 1.41036 0.265829 6.8532 0.723274 0.005 0.0749218 0.709226 0.005 0.0410593 0.060490222 0.066039665 0.044127815 0.042109048 0.020075899 0.053606488 0.071567447 0.072308239 0.022293943 0.069730629 0.098851122 0.056968211 0.019768318 0.028809447 0.046025282 0.05060433 0.053636813 0.033011601 0.028350243 0.061625237 phangorn/inst/extdata/mtREV24.dat0000644000175100001440000000414112761653173016363 0ustar hornikusers 23.18 26.95 13.24 17.67 1.90 794.38 59.93 103.33 58.94 1.90 1.90 220.99 173.56 55.28 75.24 9.77 1.90 63.05 583.55 1.90 313.56 120.71 23.03 53.30 56.77 30.71 6.75 28.28 13.90 165.23 496.13 113.99 141.49 582.40 49.12 1.90 96.49 1.90 27.10 4.34 62.73 8.34 3.31 5.98 12.26 25.46 15.58 15.16 1.90 25.65 39.70 1.90 2.41 11.49 329.09 8.36 141.40 608.70 2.31 1.90 465.58 313.86 22.73 127.67 19.57 14.88 141.88 1.90 65.41 1.90 6.18 47.37 1.90 1.90 11.97 517.98 537.53 91.37 6.37 4.69 15.20 4.98 70.80 19.11 2.67 1.90 48.16 84.67 216.06 6.44 90.82 54.31 23.64 73.31 13.43 31.26 137.29 12.83 1.90 60.97 20.63 40.10 50.10 18.84 17.31 387.86 6.04 494.39 69.02 277.05 54.11 54.71 125.93 77.46 47.70 73.61 105.79 111.16 64.29 169.90 480.72 2.08 238.46 28.01 179.97 94.93 14.82 11.17 44.78 368.43 126.40 136.33 528.17 33.85 128.22 597.21 1.90 21.95 10.68 19.86 33.60 1.90 1.90 10.92 7.08 1.90 32.44 24.00 21.71 7.84 4.21 38.58 9.99 6.48 1.90 191.36 21.21 254.77 38.82 13.12 3.21 670.14 25.01 44.15 51.17 39.96 465.58 16.21 64.92 38.73 26.25 195.06 7.64 1.90 1.90 1.90 19.00 21.14 2.53 1.90 1222.94 91.67 1.90 387.54 6.35 8.23 1.90 204.54 5.37 1.90 0.072 0.019 0.039 0.019 0.006 0.025 0.024 0.056 0.028 0.088 0.169 0.023 0.054 0.061 0.054 0.072 0.086 0.029 0.033 0.043 Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val S_ij = S_ji and PI_i for the mtREV24 model (Adachi and Hasegawa 1996). The PI's used to sum to 0.999 and I changed one of the freq from 0.168 into 0.169 so that the sum is 1. Prepared by Z. Yang according to data sent by Dr M. Hasegawa. This matrix was obtained from the 12 mitochondrial proteins encoded by the same strand of the DNA from a diverse range of species including bird, fish, frog, lamprey, as well as mammals (see Adachi and Hasegawa 1996 for details). The other matrix (mtmam.dat) included in the package is based on the same proteins from mammals only. Adachi, J. and Hasegawa, M. (1996) MOLPHY version 2.3: programs for molecular phylogenetics based on maximum likelihood. Computer Science Monographs of Institute of Statistical Mathematics 28:1-150. phangorn/inst/extdata/trees/0000755000175100001440000000000012761653173015650 5ustar hornikusersphangorn/inst/extdata/trees/woodmouse.mrbayes.nex.con0000644000175100001440000000064312761653173022627 0ustar hornikusers#NEXUS [ID: 2542150086] begin trees; [Note: This tree contains information on the topology, branch lengths (if present), and the probability of the partition indicated by the branch.] tree con_50_majrule = (No305,No1114S,((((No304,No0913S)0.98,No306)0.90,(No0906S,(No0910S,No1202S)0.92)0.54,No0908S,No1206S)0.80,((No0909S,No1007S,No1208S)1.00,(No0912S,No1103S)0.50)0.94)0.80); end; phangorn/inst/extdata/trees/woodmouse.nxs0000644000175100001440000007751512761653173020442 0ustar hornikusers#nexus BEGIN Taxa; DIMENSIONS ntax=15; TAXLABELS [1] 'No305' [2] 'No304' [3] 'No306' [4] 'No0906S' [5] 'No0908S' [6] 'No0909S' [7] 'No0910S' [8] 'No0912S' [9] 'No0913S' [10] 'No1103S' [11] 'No1007S' [12] 'No1114S' [13] 'No1202S' [14] 'No1206S' [15] 'No1208S' ; END; [Taxa] BEGIN Characters; DIMENSIONS nchar=965; FORMAT datatype=DNA missing=? gap=- symbols="atgc" labels=left transpose=no interleave=no; MATRIX 'No305' nttcgaaaaacacacccactactaaaanttatcagtcactccttcatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggagtctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatacatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacattttcatagaaacatgaaacatcggtgtggtccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaanttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagccacactgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgtccacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatatttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcagaccctata 'No304' attcgaaaaacacacccactactaaaaattatcaaccactccttcatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacatttttatagaaacatgaaacatcggtgtagtccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattncaaatttactntcagcaattccatacatcggaactaccctagtagaatgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactctagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacggtccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaaccctgtn 'No306' attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagaatgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacnncatattaaaccagaatgatacttcctatttgcctatgcaatcctacggtccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaaccctata 'No0906S' attcgaaaaacacacccactactaaaaatcatcaatcactccttcatcgatttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacggatcatacatttttatagaaacatgaaacatcggtatgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataataatttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaatacnnnn 'No0908S' attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagccccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttctccatgtaggacgaggaatgtattacgggtcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggggctacagtaattacaaatttactatcagcaattccatatatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatcctcccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No0909S' attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaactatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacactttcatagaaacatgaaacatcggtgtgatccttctattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcgggactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaatcctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No0910S' attcgaaaaacacacccactactaaaaattatcaatcactctttcatcgatttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccagatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacggatcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggcgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttgcaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No0912S' attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacggggaatgtattacgggtcatacattttcatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgctttccacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagacattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatatttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No0913S' attcgaaaaacacacccactactaaaaattatcaaccactccttcatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatgcatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacggatcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagaatgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggcgtactaataatagtttccttcctaataactctagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacggtccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No1103S' attcgaaaaacacacccactactaaaaattattaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacattttcatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatatttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No1007S' attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgccgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacactttcatagaaacatgaaacatcggtgtgatccttctattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcgggactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaatcctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No1114S' nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggagtctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagnaacccatatttgccgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatatattacggctcatacattctcatagaaacatgaaacatcggtgtggtccttttattcgcagtaatagtcacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttcctatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaggatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatatttcctatttgcctatgcaatcctacggtccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccattccttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No1202S' attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgatttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccagatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgccgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacggatcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggcgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No1206S' attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgatttgccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttctccatgtaggacgaggaatgtattacgggtcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgtgcacctcttgtttcttcatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagacctcttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn 'No1208S' nnncgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgccgagacgtaaactatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacactttcatagaaacatgaaacatcggtgtgatccttctattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccctctgaggagctacagtaattacaaatttactatcagcaattccatacatcgggactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaatcctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn ; END; [Characters] BEGIN Distances; DIMENSIONS ntax=15; FORMAT labels=left diagonal triangle=both; MATRIX [1] 'No305' 0.0 0.016684046 0.013541667 0.018789144 0.016701462 0.016701462 0.017745303 0.014613778 0.018789144 0.012526096 0.016701462 0.015317286 0.016701462 0.016701462 0.018828452 [2] 'No304' 0.016684046 0.0 0.0052083335 0.0135557875 0.0114702815 0.015641293 0.012513034 0.0135557875 0.0052137645 0.0114702815 0.015641293 0.016429354 0.0114702815 0.012513034 0.017782427 [3] 'No306' 0.013541667 0.0052083335 0.0 0.0093847755 0.00729927 0.0114702815 0.008342023 0.0093847755 0.0052137645 0.00729927 0.0114702815 0.015334063 0.00729927 0.008342023 0.013598327 [4] 'No0906S' 0.018789144 0.0135557875 0.0093847755 0.0 0.012486992 0.016649324 0.009365245 0.014568158 0.012486992 0.012486992 0.016649324 0.020765027 0.008324662 0.01144641 0.018789144 [5] 'No0908S' 0.016701462 0.0114702815 0.00729927 0.012486992 0.0 0.014568158 0.01144641 0.012486992 0.012486992 0.010405827 0.014568158 0.020765027 0.010405827 0.009365245 0.016701462 [6] 'No0909S' 0.016701462 0.015641293 0.0114702815 0.016649324 0.014568158 0.0 0.015608741 0.010405827 0.016649324 0.008324662 0.0020811656 0.020765027 0.014568158 0.015608741 0.0020876827 [7] 'No0910S' 0.017745303 0.012513034 0.008342023 0.009365245 0.01144641 0.015608741 0.0 0.013527576 0.009365245 0.01144641 0.015608741 0.019672131 0.003121748 0.010405827 0.017745303 [8] 'No0912S' 0.014613778 0.0135557875 0.0093847755 0.014568158 0.012486992 0.010405827 0.013527576 0.0 0.014568158 0.004162331 0.010405827 0.018579235 0.012486992 0.013527576 0.012526096 [9] 'No0913S' 0.018789144 0.0052137645 0.0052137645 0.012486992 0.012486992 0.016649324 0.009365245 0.014568158 0.0 0.012486992 0.016649324 0.018579235 0.008324662 0.013527576 0.018789144 [10] 'No1103S' 0.012526096 0.0114702815 0.00729927 0.012486992 0.010405827 0.008324662 0.01144641 0.004162331 0.012486992 0.0 0.008324662 0.015300547 0.010405827 0.01144641 0.010438413 [11] 'No1007S' 0.016701462 0.015641293 0.0114702815 0.016649324 0.014568158 0.0020811656 0.015608741 0.010405827 0.016649324 0.008324662 0.0 0.018579235 0.012486992 0.015608741 0.0020876827 [12] 'No1114S' 0.015317286 0.016429354 0.015334063 0.020765027 0.020765027 0.020765027 0.019672131 0.018579235 0.018579235 0.015300547 0.018579235 0.0 0.01748634 0.021857923 0.020765027 [13] 'No1202S' 0.016701462 0.0114702815 0.00729927 0.008324662 0.010405827 0.014568158 0.003121748 0.012486992 0.008324662 0.010405827 0.012486992 0.01748634 0.0 0.009365245 0.014613778 [14] 'No1206S' 0.016701462 0.012513034 0.008342023 0.01144641 0.009365245 0.015608741 0.010405827 0.013527576 0.013527576 0.01144641 0.015608741 0.021857923 0.009365245 0.0 0.017745303 [15] 'No1208S' 0.018828452 0.017782427 0.013598327 0.018789144 0.016701462 0.0020876827 0.017745303 0.012526096 0.018789144 0.010438413 0.0020876827 0.020765027 0.014613778 0.017745303 0.0 ; END; [Distances] BEGIN Splits; DIMENSIONS ntax=15 nsplits=43; FORMAT labels=no weights=yes confidences=no intervals=no; PROPERTIES fit=97.76178978414268 cyclic; CYCLE 1 12 3 2 9 13 7 4 14 5 11 15 6 8 10; MATRIX [1, size=1] 0.007878401 1 2 3 4 5 6 7 8 9 10 11 13 14 15, [2, size=4] 9.67039E-4 1 4 5 6 7 8 10 11 13 14 15, [3, size=7] 3.0405898E-4 1 5 6 8 10 11 14 15, [4, size=4] 8.927283E-5 1 6 8 10, [5, size=1] 0.006068427 1, [6, size=1] 4.1818016E-4 1 2 4 5 6 7 8 9 10 11 12 13 14 15, [7, size=3] 6.354771E-4 1 4 5 6 7 8 10 11 12 13 14 15, [8, size=7] 0.0018286711 1 6 8 10 11 12 15, [9, size=3] 3.1608884E-5 1 10 12, [10, size=2] 0.0030639141 1 12, [11, size=1] 0.0020751753 1 3 4 5 6 7 8 9 10 11 12 13 14 15, [12, size=2] 0.0022168073 1 3 4 5 6 7 8 10 11 12 13 14 15, [13, size=1] 0.0012816527 1 2 3 4 5 6 7 8 10 11 12 13 14 15, [14, size=3] 9.066461E-4 1 2 3 4 5 6 8 10 11 12 14 15, [15, size=4] 7.542655E-4 1 2 3 5 6 8 10 11 12 14 15, [16, size=5] 1.0719955E-4 1 2 3 5 6 8 10 11 12 15, [17, size=6] 6.0168037E-5 1 2 3 6 8 10 11 12 15, [18, size=1] 7.864587E-4 1 2 3 4 5 6 7 8 9 10 11 12 14 15, [19, size=2] 0.0011441667 1 2 3 4 5 6 8 9 10 11 12 14 15, [20, size=3] 3.7099395E-5 1 2 3 5 6 8 9 10 11 12 14 15, [21, size=4] 7.577939E-4 1 2 3 5 6 8 9 10 11 12 15, [22, size=6] 8.6808155E-5 1 2 3 9 10 12, [23, size=5] 1.3156666E-4 1 2 3 9 12, [24, size=1] 0.0020897626 1 2 3 4 5 6 8 9 10 11 12 13 14 15, [25, size=3] 1.0902213E-4 1 2 3 5 6 8 9 10 11 12 13 15, [26, size=4] 1.458757E-4 1 2 3 6 8 9 10 11 12 13 15, [27, size=1] 0.0051963497 1 2 3 5 6 7 8 9 10 11 12 13 14 15, [28, size=3] 1.40733355E-5 1 2 3 6 7 8 9 10 11 12 13 15, [29, size=1] 0.004156471 1 2 3 4 5 6 7 8 9 10 11 12 13 15, [30, size=2] 0.0010121305 1 2 3 4 6 7 8 9 10 11 12 13 15, [31, size=1] 0.004199673 1 2 3 4 6 7 8 9 10 11 12 13 14 15, [32, size=2] 8.544509E-4 1 2 3 4 5 6 7 8 9 10 12 13 14, [33, size=3] 0.0051881927 1 2 3 4 5 7 8 9 10 12 13 14, [34, size=4] 6.061471E-6 1 2 3 4 5 7 9 10 12 13 14, [35, size=5] 0.0010072162 1 2 3 4 5 7 9 12 13 14, [36, size=1] 0.0010342944 1 2 3 4 5 6 7 8 9 10 11 12 13 14, [37, size=2] 0.0010670845 1 2 3 4 5 7 8 9 10 11 12 13 14, [38, size=4] 2.1973468E-5 1 2 3 4 5 7 9 11 12 13 14, [39, size=2] 3.0782125E-5 1 2 3 4 5 7 9 10 11 12 13 14 15, [40, size=3] 7.4452415E-5 1 2 3 4 5 7 9 11 12 13 14 15, [41, size=1] 0.0030889313 1 2 3 4 5 6 7 9 10 11 12 13 14 15, [42, size=2] 0.0010669535 1 2 3 4 5 6 7 9 11 12 13 14 15, [43, size=1] 9.133177E-4 1 2 3 4 5 6 7 8 9 11 12 13 14 15, ; END; [Splits] BEGIN Network; DIMENSIONS ntax=15 nvertices=130 nedges=215; DRAW to_scale; TRANSLATE 2 'No305', 3 'No1114S', 4 'No306', 5 'No304', 6 'No0913S', 7 'No1202S', 8 'No0910S', 9 'No0906S', 10 'No1206S', 11 'No0908S', 12 'No1208S', 13 'No0912S', 14 'No1103S', 117 'No1007S', 123 'No0909S', ; VERTICES 1 -0.006068427 0.0 s=n, 2 0.0 0.0 s=n, 3 0.0015083641 0.0043967813 s=n, 4 -0.009464912 0.002829434 s=n, 5 -0.008871745 0.006696938 s=n, 6 -0.010746697 0.0074311504 s=n, 7 -0.013563724 0.0042226063 s=n, 8 -0.015113663 0.004792512 s=n, 9 -0.017300652 0.003006176 s=n, 10 -0.016641626 -2.3478366E-4 s=n, 11 -0.015087945 -0.0022330103 s=n, 12 -0.009807543 -0.009778595 s=n, 13 -0.0059371023 -0.004773448 s=n, 14 -0.00708383 -0.002765694 s=n, 15 -0.009065388 -6.370236E-4 s=n, 16 -0.009096996 -6.370236E-4 s=n, 17 -0.0061406503 5.2473253E-5 s=n, 18 -0.009137611 -5.845503E-4 s=n, 19 -0.009169219 -5.845503E-4 s=n, 20 -0.009257255 -6.823234E-4 s=n, 21 -0.009185032 -7.3479663E-4 s=n, 22 -0.009153423 -7.3479663E-4 s=n, 23 -0.009327484 -7.3334796E-4 s=n, 24 -0.009255261 -7.858212E-4 s=n, 25 -0.010392839 7.7441713E-4 s=n, 26 -0.010480874 6.7664403E-4 s=n, 27 -0.010551103 6.256195E-4 s=n, 28 -0.006172433 3.5486656E-4 s=n, 29 -0.009169393 -2.82157E-4 s=n, 30 -0.009201002 -2.82157E-4 s=n, 31 -0.010424622 0.0010768104 s=n, 32 -0.010512657 9.790374E-4 s=n, 33 -0.010582887 9.280128E-4 s=n, 34 -0.010479588 0.001101283 s=n, 35 -0.010567623 0.00100351 s=n, 36 -0.010637853 9.5248537E-4 s=n, 37 -0.01060607 6.50092E-4 s=n, 38 -0.010566315 0.0011642933 s=n, 39 -0.01065435 0.0010665202 s=n, 40 -0.010724579 0.0010154956 s=n, 41 -0.010692797 7.1310234E-4 s=n, 42 -0.009016237 -0.0016912675 s=n, 43 -0.008944014 -0.0017437408 s=n, 44 -0.008873785 -0.0016927161 s=n, 45 -0.008842176 -0.0016927161 s=n, 46 -0.0056889136 0.0011923469 s=n, 47 -0.008685874 5.553234E-4 s=n, 48 -0.008717483 5.553234E-4 s=n, 49 -0.009941103 0.0019142908 s=n, 50 -0.009996069 0.0019387633 s=n, 51 -0.010082795 0.0020017736 s=n, 52 -0.010587498 0.0025623022 s=n, 53 -0.011071017 0.0017248219 s=n, 54 -0.011159052 0.0016270487 s=n, 55 -0.011229281 0.0015760242 s=n, 56 -0.01192156 0.0018842467 s=n, 57 -0.011416858 0.0013237182 s=n, 58 -0.011385076 0.0010213249 s=n, 59 -0.012067436 0.0018842467 s=n, 60 -0.011562734 0.0013237182 s=n, 61 -0.011530952 0.0010213249 s=n, 62 -0.010838672 7.1310234E-4 s=n, 63 -0.010751946 6.50092E-4 s=n, 64 -0.009015604 -0.0016972957 s=n, 65 -0.00894338 -0.001749769 s=n, 66 -0.009004617 -0.0017163253 s=n, 67 -0.008932394 -0.0017687986 s=n, 68 -0.008933027 -0.0017627702 s=n, 69 -0.008862798 -0.0017117457 s=n, 70 -0.008831189 -0.0017117457 s=n, 71 -0.009744729 0.0025186655 s=n, 72 -0.009799696 0.0025431379 s=n, 73 -0.009886422 0.0026061484 s=n, 74 -0.010391124 0.0031666767 s=n, 75 -0.010844447 0.0039518555 s=n, 76 -0.01104082 0.0033474807 s=n, 77 -0.01152434 0.0025100003 s=n, 78 -0.011612375 0.0024122272 s=n, 79 -0.011682604 0.0023612028 s=n, 80 -0.0123748835 0.0026694252 s=n, 81 -0.012520759 0.0026694252 s=n, 82 -0.012404897 0.0026912317 s=n, 83 -0.012550773 0.0026912317 s=n, 84 -0.01209745 0.0019060532 s=n, 85 -0.012657413 0.0027138987 s=n, 86 -0.01220409 0.0019287202 s=n, 87 -0.0121740755 0.0019069137 s=n, 88 -0.011669374 0.0013463852 s=n, 89 -0.011637591 0.0010439919 s=n, 90 -0.012217856 0.0019257942 s=n, 91 -0.012187841 0.0019039877 s=n, 92 -0.01168314 0.0013434592 s=n, 93 -0.011651357 0.0010410659 s=n, 94 -0.011544717 0.0010183989 s=n, 95 -0.0108524375 7.1017636E-4 s=n, 96 -0.010765712 6.4716605E-4 s=n, 97 -0.009557918 -0.006857067 s=n, 98 -0.009546931 -0.0068760966 s=n, 99 -0.009474708 -0.0069285696 s=n, 100 -0.0094248885 -0.0069838986 s=n, 101 -0.008882575 -0.0018241275 s=n, 102 -0.008883209 -0.0018180992 s=n, 103 -0.00881298 -0.0017670747 s=n, 104 -0.008781371 -0.0017670747 s=n, 105 -0.00951301 0.0047233286 s=n, 106 -0.009567976 0.0047478015 s=n, 107 -0.009654703 0.0048108115 s=n, 108 -0.010159405 0.00537134 s=n, 109 -0.010612727 0.0061565186 s=n, 110 -0.013170495 0.0035415133 s=n, 111 -0.013316371 0.0035415133 s=n, 112 -0.01342301 0.0035641803 s=n, 113 -0.012575984 6.293953E-4 s=n, 114 -0.012469344 6.067283E-4 s=n, 115 -0.011777065 2.9850577E-4 s=n, 116 -0.011690339 2.3549546E-4 s=n, 117 -0.009821957 -0.007669698 s=n, 118 -0.00981097 -0.0076887277 s=n, 119 -0.009699429 -0.0087499665 s=n, 120 -0.00943539 -0.007937335 s=n, 121 -0.009363167 -0.007989809 s=n, 122 -0.009313349 -0.008045138 s=n, 123 -0.009297958 -0.008071796 s=n, 124 -0.0094094975 -0.0070105568 s=n, 125 -0.008867184 -0.0018507856 s=n, 126 -0.008004 -0.002477925 s=n, 127 -0.008019391 -0.002451267 s=n, 128 -0.008020026 -0.0024452389 s=n, 129 -0.007949796 -0.0023942143 s=n, 130 -0.007918187 -0.0023942143 s=n, ; VLABELS 2 'No305' x=12 y=9 f='Dialog-PLAIN-16', 3 'No1114S' x=11 y=14, 4 'No306' x=-15 y=26, 5 'No304' x=-19 y=28, 6 'No0913S' x=-34 y=29, 7 'No1202S' x=-39 y=27, 8 'No0910S' x=-77 y=16, 9 'No0906S' x=-79 y=11, 10 'No1206S' x=-79 y=6, 11 'No0908S' x=-77 y=2, 12 'No1208S' x=-34 y=-19, 13 'No0912S' x=-25 y=-16, 14 'No1103S' x=11 y=4, 117 'No1007S' x=-37 y=-18, 123 'No0909S' x=-31 y=-24, ; EDGES 1 1 2 s=5 w=0.006068427, 2 15 1 s=10 w=0.0030639141, 3 16 15 s=9 w=3.1608884E-5, 4 17 1 s=4 w=8.927283E-5, 5 18 15 s=4 w=8.927283E-5, 6 18 17 s=10 w=0.0030639141, 7 19 16 s=4 w=8.927283E-5, 8 19 18 s=9 w=3.1608884E-5, 9 20 19 s=23 w=1.3156666E-4, 10 21 16 s=23 w=1.3156666E-4, 11 21 20 s=4 w=8.927283E-5, 12 22 15 s=23 w=1.3156666E-4, 13 22 21 s=9 w=3.1608884E-5, 14 23 20 s=22 w=8.6808155E-5, 15 24 21 s=22 w=8.6808155E-5, 16 24 23 s=4 w=8.927283E-5, 17 25 19 s=8 w=0.0018286711, 18 26 20 s=8 w=0.0018286711, 19 26 25 s=23 w=1.3156666E-4, 20 27 23 s=8 w=0.0018286711, 21 27 26 s=22 w=8.6808155E-5, 22 28 17 s=3 w=3.0405898E-4, 23 29 18 s=3 w=3.0405898E-4, 24 29 28 s=10 w=0.0030639141, 25 30 19 s=3 w=3.0405898E-4, 26 30 29 s=9 w=3.1608884E-5, 27 31 25 s=3 w=3.0405898E-4, 28 31 30 s=8 w=0.0018286711, 29 32 26 s=3 w=3.0405898E-4, 30 32 31 s=23 w=1.3156666E-4, 31 33 27 s=3 w=3.0405898E-4, 32 33 32 s=22 w=8.6808155E-5, 33 34 31 s=17 w=6.0168037E-5, 34 35 32 s=17 w=6.0168037E-5, 35 35 34 s=23 w=1.3156666E-4, 36 36 33 s=17 w=6.0168037E-5, 37 36 35 s=22 w=8.6808155E-5, 38 37 27 s=17 w=6.0168037E-5, 39 37 36 s=3 w=3.0405898E-4, 40 38 34 s=16 w=1.0719955E-4, 41 39 35 s=16 w=1.0719955E-4, 42 39 38 s=23 w=1.3156666E-4, 43 40 36 s=16 w=1.0719955E-4, 44 40 39 s=22 w=8.6808155E-5, 45 41 37 s=16 w=1.0719955E-4, 46 41 40 s=3 w=3.0405898E-4, 47 42 23 s=35 w=0.0010072162, 48 43 24 s=35 w=0.0010072162, 49 43 42 s=4 w=8.927283E-5, 50 44 21 s=35 w=0.0010072162, 51 44 43 s=22 w=8.6808155E-5, 52 45 22 s=35 w=0.0010072162, 53 45 44 s=9 w=3.1608884E-5, 54 46 28 s=2 w=9.67039E-4, 55 46 3 s=1 w=0.007878401, 56 47 29 s=2 w=9.67039E-4, 57 47 46 s=10 w=0.0030639141, 58 48 30 s=2 w=9.67039E-4, 59 48 47 s=9 w=3.1608884E-5, 60 49 31 s=2 w=9.67039E-4, 61 49 48 s=8 w=0.0018286711, 62 50 34 s=2 w=9.67039E-4, 63 50 49 s=17 w=6.0168037E-5, 64 51 38 s=2 w=9.67039E-4, 65 51 50 s=16 w=1.0719955E-4, 66 52 51 s=15 w=7.542655E-4, 67 53 38 s=15 w=7.542655E-4, 68 53 52 s=2 w=9.67039E-4, 69 54 39 s=15 w=7.542655E-4, 70 54 53 s=23 w=1.3156666E-4, 71 55 40 s=15 w=7.542655E-4, 72 55 54 s=22 w=8.6808155E-5, 73 56 55 s=21 w=7.577939E-4, 74 57 40 s=21 w=7.577939E-4, 75 57 56 s=15 w=7.542655E-4, 76 58 41 s=21 w=7.577939E-4, 77 58 57 s=3 w=3.0405898E-4, 78 59 56 s=26 w=1.458757E-4, 79 60 57 s=26 w=1.458757E-4, 80 60 59 s=15 w=7.542655E-4, 81 61 58 s=26 w=1.458757E-4, 82 61 60 s=3 w=3.0405898E-4, 83 62 41 s=26 w=1.458757E-4, 84 62 61 s=21 w=7.577939E-4, 85 63 37 s=26 w=1.458757E-4, 86 63 62 s=16 w=1.0719955E-4, 87 64 42 s=34 w=6.061471E-6, 88 65 43 s=34 w=6.061471E-6, 89 65 64 s=4 w=8.927283E-5, 90 66 64 s=38 w=2.1973468E-5, 91 67 65 s=38 w=2.1973468E-5, 92 67 66 s=4 w=8.927283E-5, 93 68 43 s=38 w=2.1973468E-5, 94 68 67 s=34 w=6.061471E-6, 95 69 44 s=38 w=2.1973468E-5, 96 69 68 s=22 w=8.6808155E-5, 97 70 45 s=38 w=2.1973468E-5, 98 70 69 s=9 w=3.1608884E-5, 99 71 49 s=7 w=6.354771E-4, 100 71 4 s=6 w=4.1818016E-4, 101 72 50 s=7 w=6.354771E-4, 102 72 71 s=17 w=6.0168037E-5, 103 73 51 s=7 w=6.354771E-4, 104 73 72 s=16 w=1.0719955E-4, 105 74 52 s=7 w=6.354771E-4, 106 74 73 s=15 w=7.542655E-4, 107 75 74 s=14 w=9.066461E-4, 108 76 52 s=14 w=9.066461E-4, 109 76 75 s=7 w=6.354771E-4, 110 77 53 s=14 w=9.066461E-4, 111 77 76 s=2 w=9.67039E-4, 112 78 54 s=14 w=9.066461E-4, 113 78 77 s=23 w=1.3156666E-4, 114 79 55 s=14 w=9.066461E-4, 115 79 78 s=22 w=8.6808155E-5, 116 80 56 s=14 w=9.066461E-4, 117 80 79 s=21 w=7.577939E-4, 118 81 59 s=14 w=9.066461E-4, 119 81 80 s=26 w=1.458757E-4, 120 82 80 s=20 w=3.7099395E-5, 121 83 81 s=20 w=3.7099395E-5, 122 83 82 s=26 w=1.458757E-4, 123 84 59 s=20 w=3.7099395E-5, 124 84 83 s=14 w=9.066461E-4, 125 85 83 s=25 w=1.0902213E-4, 126 86 84 s=25 w=1.0902213E-4, 127 86 85 s=14 w=9.066461E-4, 128 87 59 s=25 w=1.0902213E-4, 129 87 86 s=20 w=3.7099395E-5, 130 88 60 s=25 w=1.0902213E-4, 131 88 87 s=15 w=7.542655E-4, 132 89 61 s=25 w=1.0902213E-4, 133 89 88 s=3 w=3.0405898E-4, 134 90 86 s=28 w=1.40733355E-5, 135 90 9 s=27 w=0.0051963497, 136 91 87 s=28 w=1.40733355E-5, 137 91 90 s=20 w=3.7099395E-5, 138 92 88 s=28 w=1.40733355E-5, 139 92 91 s=15 w=7.542655E-4, 140 93 89 s=28 w=1.40733355E-5, 141 93 92 s=3 w=3.0405898E-4, 142 94 61 s=28 w=1.40733355E-5, 143 94 93 s=25 w=1.0902213E-4, 144 95 62 s=28 w=1.40733355E-5, 145 95 94 s=21 w=7.577939E-4, 146 96 63 s=28 w=1.40733355E-5, 147 96 95 s=16 w=1.0719955E-4, 148 97 64 s=33 w=0.0051881927, 149 98 66 s=33 w=0.0051881927, 150 98 97 s=38 w=2.1973468E-5, 151 99 67 s=33 w=0.0051881927, 152 99 98 s=4 w=8.927283E-5, 153 100 99 s=40 w=7.4452415E-5, 154 101 67 s=40 w=7.4452415E-5, 155 101 100 s=33 w=0.0051881927, 156 102 68 s=40 w=7.4452415E-5, 157 102 101 s=34 w=6.061471E-6, 158 103 69 s=40 w=7.4452415E-5, 159 103 102 s=22 w=8.6808155E-5, 160 104 70 s=40 w=7.4452415E-5, 161 104 103 s=9 w=3.1608884E-5, 162 105 71 s=12 w=0.0022168073, 163 105 5 s=11 w=0.0020751753, 164 106 72 s=12 w=0.0022168073, 165 106 105 s=17 w=6.0168037E-5, 166 107 73 s=12 w=0.0022168073, 167 107 106 s=16 w=1.0719955E-4, 168 108 74 s=12 w=0.0022168073, 169 108 107 s=15 w=7.542655E-4, 170 109 75 s=12 w=0.0022168073, 171 109 108 s=14 w=9.066461E-4, 172 109 6 s=13 w=0.0012816527, 173 110 82 s=19 w=0.0011441667, 174 110 7 s=18 w=7.864587E-4, 175 111 83 s=19 w=0.0011441667, 176 111 110 s=26 w=1.458757E-4, 177 112 85 s=19 w=0.0011441667, 178 112 111 s=25 w=1.0902213E-4, 179 112 8 s=24 w=0.0020897626, 180 113 93 s=30 w=0.0010121305, 181 113 10 s=29 w=0.004156471, 182 114 94 s=30 w=0.0010121305, 183 114 113 s=25 w=1.0902213E-4, 184 115 95 s=30 w=0.0010121305, 185 115 114 s=21 w=7.577939E-4, 186 116 96 s=30 w=0.0010121305, 187 116 115 s=16 w=1.0719955E-4, 188 116 11 s=31 w=0.004199673, 189 117 97 s=32 w=8.544509E-4, 190 118 98 s=32 w=8.544509E-4, 191 118 117 s=38 w=2.1973468E-5, 192 119 118 s=37 w=0.0010670845, 193 119 12 s=36 w=0.0010342944, 194 120 98 s=37 w=0.0010670845, 195 120 119 s=32 w=8.544509E-4, 196 121 99 s=37 w=0.0010670845, 197 121 120 s=4 w=8.927283E-5, 198 122 100 s=37 w=0.0010670845, 199 122 121 s=40 w=7.4452415E-5, 200 123 122 s=39 w=3.0782125E-5, 201 124 100 s=39 w=3.0782125E-5, 202 124 123 s=37 w=0.0010670845, 203 125 101 s=39 w=3.0782125E-5, 204 125 124 s=33 w=0.0051881927, 205 126 125 s=42 w=0.0010669535, 206 126 13 s=41 w=0.0030889313, 207 127 101 s=42 w=0.0010669535, 208 127 126 s=39 w=3.0782125E-5, 209 128 102 s=42 w=0.0010669535, 210 128 127 s=34 w=6.061471E-6, 211 129 103 s=42 w=0.0010669535, 212 129 128 s=22 w=8.6808155E-5, 213 130 104 s=42 w=0.0010669535, 214 130 129 s=9 w=3.1608884E-5, 215 130 14 s=43 w=9.133177E-4, ; END; [Network] BEGIN st_Assumptions; uptodate; chartransform=Uncorrected_P; disttransform=NeighborNet; splitstransform=EqualAngle; SplitsPostProcess filter=dimension value=4; exclude no missing; autolayoutnodelabels; END; [st_Assumptions] phangorn/inst/extdata/trees/RAxML_bipartitions.woodmouse0000644000175100001440000000142212761653173023324 0ustar hornikusers(No305:0.00622878382196208596,(((No1007S:0.00000170671604060440,(No0909S:0.00108390188878723214,No1208S:0.00108436668237289475)52:0.00108405665369984980)99:0.00776063902846151413,(No0912S:0.00328637227135690751,No1103S:0.00109428209179901806)77:0.00000170671604060440)74:0.00223367776102044056,((No0908S:0.00552992494657482316,(No1206S:0.00557570157809361960,((No1202S:0.00108229934579785574,No0910S:0.00222680065047513396)95:0.00225951139716124754,No0906S:0.00571167759421415972)73:0.00108738027363135628)52:0.00108364136649804653)49:0.00000170671604060440,(No306:0.00000170671604060440,(No0913S:0.00331906405569605848,No304:0.00275623272441480370)81:0.00275326022717664034)76:0.00218358914928350797)74:0.00218629437313406583)88:0.00379919491759181556,No1114S:0.01098665288440475382); phangorn/inst/extdata/trees/woodmouse.mrbayes.nex.run2.t0000644000175100001440000025045012761653173023203 0ustar hornikusers#NEXUS [ID: 2542150086] begin trees; translate 1 No305, 2 No304, 3 No306, 4 No0906S, 5 No0908S, 6 No0909S, 7 No0910S, 8 No0912S, 9 No0913S, 10 No1103S, 11 No1007S, 12 No1114S, 13 No1202S, 14 No1206S, 15 No1208S; tree rep.1 = ((((3,(((4,(8,(15,5))),2),((14,11),12))),(9,13)),7),(10,6),1); tree rep.10000 = (12,(((8,((15,6),11)),10),((((2,9),3),(14,((7,13),4))),5)),1); tree rep.20000 = (12,((8,((6,(11,15)),10)),(((14,((13,7),5)),4),((9,2),3))),1); tree rep.30000 = (12,(((3,(9,2)),(5,((14,4),(7,13)))),((8,10),((6,15),11))),1); tree rep.40000 = ((((12,5),((2,9),3)),(14,((7,13),4))),((8,((11,15),6)),10),1); tree rep.50000 = (((((11,(6,15)),(8,10)),((4,((13,7),14)),5)),((2,9),3)),12,1); tree rep.60000 = ((10,(8,(((4,(((9,2),3),((7,13),14))),5),(11,(15,6))))),12,1); tree rep.70000 = (12,((10,(((11,15),6),8)),((((13,7),4),(5,14)),((9,2),3))),1); tree rep.80000 = (12,(((3,(9,2)),(((7,13),4),(14,5))),((8,(11,(15,6))),10)),1); tree rep.90000 = (((((((13,7),4),5),((2,9),3)),14),((10,8),((11,15),6))),12,1); tree rep.100000 = (12,((10,8),(((9,2),((13,7),4)),(((5,14),(6,(11,15))),3))),1); tree rep.110000 = (12,((((7,13),4),(14,(5,((9,2),3)))),(10,(8,(11,(6,15))))),1); tree rep.120000 = (((10,(8,((6,15),11))),((5,(3,(2,9))),(14,(4,(13,7))))),12,1); tree rep.130000 = (((((4,((7,13),14)),5),(3,(2,9))),(8,(10,((15,6),11)))),12,1); tree rep.140000 = ((((10,8),((15,11),6)),((((7,13),(4,14)),5),(3,(2,9)))),12,1); tree rep.150000 = ((((5,(((9,2),3),14)),((7,13),4)),12),((8,10),((15,11),6)),1); tree rep.160000 = (((((11,(6,15)),10),8),((5,(((7,13),4),14)),((9,2),3))),12,1); tree rep.170000 = (12,((((10,8),((15,6),11)),4),((((9,2),3),5),(14,(7,13)))),1); tree rep.180000 = ((((3,(9,2)),(5,(4,(14,(13,7))))),((8,((15,11),6)),10)),12,1); tree rep.190000 = (12,(((((9,2),3),5),((14,4),(13,7))),((((6,15),11),8),10)),1); tree rep.200000 = (12,((14,((5,(7,13)),(((((11,6),15),10),8),4))),((9,2),3)),1); tree rep.210000 = ((((5,(3,(9,2))),(((13,4),7),14)),((8,10),((15,11),6))),12,1); tree rep.220000 = (12,(((((13,7),14),4),5),(((9,2),3),((11,(6,15)),(8,10)))),1); tree rep.230000 = (12,(((6,15),11),(((5,((13,(4,7)),(3,(9,2)))),14),(8,10))),1); tree rep.240000 = ((((13,7),(((11,(6,15)),(8,10)),((5,4),14))),((2,9),3)),12,1); tree rep.250000 = (((4,(13,7)),((5,14),((2,9),3))),(((10,8),((11,15),6)),12),1); tree rep.260000 = ((((((4,(13,7)),14),(3,(2,9))),5),(8,(((15,11),6),10))),12,1); tree rep.270000 = (12,((8,(10,((11,15),6))),(3,((13,7),(((9,2),5),(4,14))))),1); tree rep.280000 = (((((9,2),3),(5,((7,13),(14,4)))),(((11,15),6),(8,10))),12,1); tree rep.290000 = ((((((7,13),14),4),((3,(2,9)),5)),((10,8),(11,(15,6)))),12,1); tree rep.300000 = ((((((8,(6,(11,15))),10),5),((9,2),3)),(((13,7),14),4)),12,1); tree rep.310000 = (12,(((((2,9),3),5),(((13,7),14),4)),(8,((11,(15,6)),10))),1); tree rep.320000 = (12,((10,((6,(15,11)),8)),((5,((4,(13,7)),14)),((2,9),3))),1); tree rep.330000 = (12,(((8,10),((6,15),11)),(((13,7),(4,(5,14))),((2,9),3))),1); tree rep.340000 = ((((((11,15),6),((((2,9),3),5),(4,((13,7),14)))),10),8),12,1); tree rep.350000 = (12,((8,(((15,6),11),10)),((((9,2),3),5),(14,(4,(7,13))))),1); tree rep.360000 = ((((((11,15),6),8),10),((5,((9,2),3)),((7,13),(14,4)))),12,1); tree rep.370000 = ((((((11,15),6),(8,10)),(3,(5,(9,2)))),((4,(7,13)),14)),12,1); tree rep.380000 = ((((((14,(7,13)),4),(3,(9,2))),5),(((6,15),11),(10,8))),12,1); tree rep.390000 = (12,((14,((((9,2),3),((13,7),4)),5)),(((11,(6,15)),8),10)),1); tree rep.400000 = (12,((5,((14,((7,13),4)),(3,(9,2)))),(10,((6,(15,11)),8))),1); tree rep.410000 = (((10,((6,(15,11)),8)),(((3,(9,2)),5),(((7,13),14),4))),12,1); tree rep.420000 = (12,((5,((7,13),((14,4),((9,2),3)))),((11,(15,6)),(10,8))),1); tree rep.430000 = ((((7,13),5),((((9,2),3),4),14)),((10,(8,(6,(11,15)))),12),1); tree rep.440000 = (12,(((4,(14,(13,7))),(5,(3,(9,2)))),((8,((15,11),6)),10)),1); tree rep.450000 = (((((9,2),3),((((7,13),4),14),5)),((8,10),((11,15),6))),12,1); tree rep.460000 = (12,((((15,11),6),(10,8)),((4,((9,2),((3,5),(13,7)))),14)),1); tree rep.470000 = (12,((8,((6,(11,15)),10)),(5,((3,(9,2)),((7,(13,4)),14)))),1); tree rep.480000 = (12,(((5,((9,2),3)),((11,(15,6)),(8,10))),(((7,13),14),4)),1); tree rep.490000 = (12,(((((13,7),4),(5,14)),(3,(9,2))),(((6,15),11),(8,10))),1); tree rep.500000 = (((((13,7),4),(14,5)),(12,((9,2),3))),((8,((11,15),6)),10),1); tree rep.510000 = ((((8,10),(6,(15,11))),((7,13),(((9,2),3),(5,(14,4))))),12,1); tree rep.520000 = (12,((((((9,2),3),((5,14),(13,(7,4)))),(6,(15,11))),8),10),1); tree rep.530000 = (12,((5,(((2,9),3),((13,7),(4,14)))),((10,8),(11,(6,15)))),1); tree rep.540000 = ((((((15,6),11),8),10),((((7,13),4),(14,5)),(3,(2,9)))),12,1); tree rep.550000 = ((((12,(9,2)),(4,(13,7))),((14,5),3)),(((15,6),11),(10,8)),1); tree rep.560000 = (12,((((7,13),((9,2),3)),(4,(14,5))),((10,8),((6,15),11))),1); tree rep.570000 = (12,((((5,14),(4,(7,13))),((2,9),3)),((6,(15,11)),(8,10))),1); tree rep.580000 = (12,(((8,10),((6,15),11)),(((13,4),7),(((2,9),3),(14,5)))),1); tree rep.590000 = ((5,(3,(9,2))),((12,(10,(8,(6,(11,15))))),((4,14),(7,13))),1); tree rep.600000 = (12,(((8,(15,(6,11))),10),(((9,2),3),(((7,13),(5,14)),4))),1); tree rep.610000 = (12,(((3,(2,9)),(5,((4,7),(14,13)))),(8,(((15,6),11),10))),1); tree rep.620000 = (((((6,(11,15)),8),10),(((14,((7,13),4)),5),((2,9),3))),12,1); tree rep.630000 = (((((2,9),3),(5,(14,((7,13),4)))),(((11,(15,6)),10),8)),12,1); tree rep.640000 = (12,(((((9,2),3),(13,(7,4))),(14,5)),(10,(8,((6,15),11)))),1); tree rep.650000 = (12,((8,(((15,11),6),10)),(((3,(9,2)),(4,(7,13))),(14,5))),1); tree rep.660000 = ((((3,(2,9)),(((13,(7,4)),14),5)),(((15,11),6),(8,10))),12,1); tree rep.670000 = ((((11,(15,6)),(8,10)),(((14,(7,13)),4),(5,(3,(2,9))))),12,1); tree rep.680000 = (((((11,15),6),(8,10)),(((4,(14,(7,13))),5),(3,(9,2)))),12,1); tree rep.690000 = (12,(((8,10),((11,6),15)),((3,(9,2)),((14,4),(5,(7,13))))),1); tree rep.700000 = (((((5,(7,13)),14),(((2,9),3),4)),((6,(11,15)),(8,10))),12,1); tree rep.710000 = (12,(((10,8),(11,(6,15))),((((14,(7,13)),4),(3,(9,2))),5)),1); tree rep.720000 = (12,((5,((3,(9,2)),((4,14),(7,13)))),(8,(10,(11,(6,15))))),1); tree rep.730000 = ((((13,7),((3,(9,2)),(((15,6),11),(10,8)))),((14,5),4)),12,1); tree rep.740000 = (12,((8,(10,((15,6),11))),((3,(9,2)),(4,((7,13),(14,5))))),1); tree rep.750000 = (((5,((((8,10),((11,15),6)),12),((2,9),3))),((7,13),4)),14,1); tree rep.760000 = (12,(((14,4),5),((((8,10),(11,(6,15))),(3,(2,9))),(13,7))),1); tree rep.770000 = ((((14,(7,13)),4),(((((11,6),15),(8,10)),((2,9),3)),5)),12,1); tree rep.780000 = (((((3,(2,9)),((8,10),((11,15),6))),(14,5)),(4,(13,7))),12,1); tree rep.790000 = (((((4,(13,7)),14),(5,(3,(9,2)))),((10,8),(6,(11,15)))),12,1); tree rep.800000 = (12,(8,((10,(((14,(4,(7,13))),5),((9,2),3))),(11,(6,15)))),1); tree rep.810000 = (12,(((10,((6,15),11)),8),(14,(((4,(13,7)),(3,(2,9))),5))),1); tree rep.820000 = ((((5,(14,((13,7),4))),(3,(2,9))),((10,8),(11,(15,6)))),12,1); tree rep.830000 = ((4,(13,7)),((14,((((2,9),12),3),(((6,15),11),(8,10)))),5),1); tree rep.840000 = ((((10,8),(6,(11,15))),((14,((7,13),4)),(5,(3,(2,9))))),12,1); tree rep.850000 = (((8,(10,((15,11),6))),((((9,2),3),14),(((13,7),4),5))),12,1); tree rep.860000 = (12,((((6,(11,15)),8),10),((4,(7,13)),((14,5),((9,2),3)))),1); tree rep.870000 = (12,(((4,(7,13)),(((9,2),3),(14,5))),(8,(((15,6),11),10))),1); tree rep.880000 = (12,(((3,(9,2)),(((4,13),7),(5,14))),((((11,6),15),8),10)),1); tree rep.890000 = (((2,9),((5,(((13,7),14),4)),3)),(12,((11,(15,6)),(10,8))),1); tree rep.900000 = (12,(8,((((3,(9,2)),(5,((4,(13,7)),14))),((15,11),6)),10)),1); tree rep.910000 = ((12,5),(((((7,13),4),((((15,11),6),8),10)),((2,9),3)),14),1); tree rep.920000 = (((((9,2),3),((14,(4,(13,7))),5)),((11,(6,15)),(8,10))),12,1); tree rep.930000 = (((8,((6,(15,11)),10)),(((14,(5,4)),(7,13)),((9,2),3))),12,1); tree rep.940000 = ((((10,8),((15,11),6)),((7,13),(14,(((2,(9,3)),4),5)))),12,1); tree rep.950000 = (((5,(3,(2,9))),(14,((4,7),13))),(12,(8,(10,(6,(15,11))))),1); tree rep.960000 = (((((((6,15),11),8),10),5),(4,((3,(9,2)),(14,(13,7))))),12,1); tree rep.970000 = ((((10,((11,15),6)),8),(((3,(2,9)),((14,4),(7,13))),5)),12,1); tree rep.980000 = (12,((3,(2,9)),((((8,((15,11),6)),10),5),((4,(13,7)),14))),1); tree rep.990000 = (12,(((14,((7,13),4)),(((9,2),3),5)),((10,((6,15),11)),8)),1); tree rep.1000000 = ((((6,(11,15)),8),(10,((((9,2),3),((7,13),4)),(14,5)))),12,1); tree rep.1010000 = ((((10,(11,(15,6))),8),(5,(((2,9),3),(((13,7),4),14)))),12,1); tree rep.1020000 = ((((10,(11,(6,15))),8),((5,(14,(4,(13,7)))),((9,2),3))),12,1); tree rep.1030000 = ((((5,(4,(3,(9,2)))),(14,(7,13))),(8,((11,(15,6)),10))),12,1); tree rep.1040000 = ((((((11,15),6),8),10),(4,((14,(5,(3,(9,2)))),(13,7)))),12,1); tree rep.1050000 = (12,((5,(14,4)),((((9,2),3),(13,7)),(10,(8,((11,6),15))))),1); tree rep.1060000 = (((((4,(7,13)),(5,14)),(3,(9,2))),((10,8),((15,6),11))),12,1); tree rep.1070000 = ((((10,(11,(15,6))),8),((((9,2),14),((13,5),7)),(4,3))),12,1); tree rep.1080000 = ((((10,8),(11,(15,6))),(((2,3),5),(14,((9,4),(13,7))))),12,1); tree rep.1090000 = (12,((8,(((6,11),15),10)),(((4,5),(14,(13,7))),(3,(2,9)))),1); tree rep.1100000 = (((11,(6,15)),((4,(((7,13),(14,5)),(3,(9,2)))),(8,10))),12,1); tree rep.1110000 = ((((((4,13),7),14),5),(3,(9,2))),(12,(10,(8,(6,(15,11))))),1); tree rep.1120000 = ((((((13,7),14),4),(5,((9,2),3))),((10,(15,(6,11))),8)),12,1); tree rep.1130000 = (12,((10,(8,((15,11),6))),(5,(((9,2),3),(14,(4,(7,13)))))),1); tree rep.1140000 = (12,(((4,((6,(11,15)),(8,10))),14),(((2,9),3),((7,13),5))),1); tree rep.1150000 = (12,((((6,(11,15)),8),10),((5,((2,9),3)),((7,13),(4,14)))),1); tree rep.1160000 = (12,((((3,(2,9)),((7,13),4)),(5,14)),(8,(((15,11),6),10))),1); tree rep.1170000 = ((((3,(2,9)),(((7,13),14),4)),5),((10,8),((11,(6,15)),12)),1); tree rep.1180000 = (((5,14),(((7,13),4),(3,(2,9)))),(12,((8,((15,11),6)),10)),1); tree rep.1190000 = (((7,13),(4,(14,(5,(3,(9,2)))))),((((15,6),11),(10,8)),12),1); tree rep.1200000 = ((((10,(11,(6,15))),8),(((13,7),4),(((9,2),3),(14,5)))),12,1); tree rep.1210000 = ((((8,((6,15),11)),10),(((3,5),(2,9)),((14,4),(7,13)))),12,1); tree rep.1220000 = (((((15,(6,11)),8),10),(((5,((9,2),3)),14),(4,(13,7)))),12,1); tree rep.1230000 = (12,((4,((((2,9),3),((13,7),14)),5)),((10,(15,(11,6))),8)),1); tree rep.1240000 = (12,(((10,8),(6,(11,15))),((5,((2,9),3)),((13,7),(14,4)))),1); tree rep.1250000 = ((12,(3,(9,2))),(((10,8),((15,6),11)),(5,((14,4),(7,13)))),1); tree rep.1260000 = (((14,(7,13)),(4,(((9,2),3),5))),(12,(10,(((15,6),11),8))),1); tree rep.1270000 = (12,(((15,6),11),(10,(8,((((9,2),3),(14,(4,(13,7)))),5)))),1); tree rep.1280000 = ((((10,8),((15,11),6)),(((5,14),(4,(7,13))),(3,(9,2)))),12,1); tree rep.1290000 = (((14,5),(((3,(2,9)),(((10,8),(11,(15,6))),(7,13))),4)),12,1); tree rep.1300000 = (12,((((10,(6,(15,11))),8),5),(((7,13),(14,4)),(3,(2,9)))),1); tree rep.1310000 = (((((15,11),6),(((7,13),(14,4)),(((9,2),3),5))),(10,8)),12,1); tree rep.1320000 = (12,(((14,5),((4,(7,13)),(3,(9,2)))),((6,(15,11)),(8,10))),1); tree rep.1330000 = ((((3,(2,9)),((13,7),14)),(4,5)),((8,(10,(6,(15,11)))),12),1); tree rep.1340000 = ((((10,8),((6,15),11)),((5,(3,(2,9))),(((13,7),4),14))),12,1); tree rep.1350000 = (((((14,(7,4)),13),(((9,2),3),5)),((10,(11,(6,15))),8)),12,1); tree rep.1360000 = (12,((((15,11),6),(8,10)),((7,13),((4,14),(((2,9),3),5)))),1); tree rep.1370000 = (12,((((5,(14,(13,(7,4)))),3),(2,9)),(10,((15,(11,6)),8))),1); tree rep.1380000 = ((8,((11,(15,6)),10)),((12,(14,((7,13),4))),(5,((2,9),3))),1); tree rep.1390000 = ((((3,(2,9)),(4,((7,13),14))),5),(((((6,15),11),8),10),12),1); tree rep.1400000 = ((((13,7),((5,(2,9)),(4,((10,(6,(11,15))),8)))),(14,3)),12,1); tree rep.1410000 = ((((6,(11,15)),(8,10)),(((3,(9,2)),((14,5),(13,7))),4)),12,1); tree rep.1420000 = (12,((((13,7),4),5),(((((11,15),6),(8,10)),((9,2),3)),14)),1); tree rep.1430000 = (12,(((6,(11,15)),(8,10)),(((13,7),((14,5),4)),(3,(9,2)))),1); tree rep.1440000 = (((((3,(9,2)),5),(14,(4,(13,7)))),(8,(10,((15,6),11)))),12,1); tree rep.1450000 = (12,(((5,(((13,7),4),14)),(3,(9,2))),((8,((15,6),11)),10)),1); tree rep.1460000 = (((11,(6,15)),((10,8),(((5,14),((9,2),3)),((13,7),4)))),12,1); tree rep.1470000 = ((((11,(15,6)),(((14,5),((13,7),4)),(10,8))),12),(3,(9,2)),1); tree rep.1480000 = (((12,(8,(((15,11),6),10))),((2,9),3)),(((4,(7,13)),5),14),1); tree rep.1490000 = (((10,((6,(15,11)),8)),((5,((2,9),3)),(14,((7,13),4)))),12,1); tree rep.1500000 = (12,((5,(14,((3,(9,2)),(13,(4,7))))),((8,(6,(11,15))),10)),1); tree rep.1510000 = (12,(((5,((9,2),3)),(4,((7,13),14))),((10,8),(6,(15,11)))),1); tree rep.1520000 = (12,((((14,(3,(9,2))),(5,((13,7),4))),(10,8)),((15,6),11)),1); tree rep.1530000 = (12,(((8,10),((11,15),6)),(((3,(9,2)),5),(((13,7),4),14))),1); tree rep.1540000 = ((((11,(15,6)),(10,8)),((((13,7),4),(14,5)),(3,(9,2)))),12,1); tree rep.1550000 = (12,((((8,(10,(11,(15,6)))),(5,14)),((3,9),2)),((13,7),4)),1); tree rep.1560000 = (((((3,(2,9)),((13,7),(14,5))),4),((11,(6,15)),(10,8))),12,1); tree rep.1570000 = (12,((6,(15,11)),((10,(4,(((3,(2,9)),(14,(13,7))),5))),8)),1); tree rep.1580000 = (12,((((2,9),3),(5,((14,4),(7,13)))),(10,(((15,6),11),8))),1); tree rep.1590000 = ((((10,8),((11,15),6)),((4,5),((3,(9,2)),((7,13),14)))),12,1); tree rep.1600000 = (12,((10,8),((14,((3,(9,2)),(((13,7),4),5))),((11,15),6))),1); tree rep.1610000 = ((((5,(((13,7),4),14)),((2,9),3)),(10,(8,(6,(15,11))))),12,1); tree rep.1620000 = ((12,((3,(2,9)),(((14,4),5),(7,13)))),(((6,(11,15)),8),10),1); tree rep.1630000 = (12,((4,((14,(5,(7,13))),(3,(2,9)))),((8,10),((15,11),6))),1); tree rep.1640000 = (12,((((10,8),(11,(15,6))),((3,(9,2)),((14,4),(7,13)))),5),1); tree rep.1650000 = (((((6,15),11),(10,8)),(((3,(9,2)),5),((7,13),(14,4)))),12,1); tree rep.1660000 = (((((11,15),6),10),8),(12,(14,(((7,13),(3,(2,9))),(5,4)))),1); tree rep.1670000 = (((((2,9),3),((((13,7),4),14),5)),(((6,15),11),(10,8))),12,1); tree rep.1680000 = (((10,(8,(11,(15,6)))),(5,((((7,13),4),14),(3,(9,2))))),12,1); tree rep.1690000 = ((((3,(9,2)),5),(4,(14,(7,13)))),(12,((11,(15,6)),(8,10))),1); tree rep.1700000 = (((((13,7),(4,14)),((9,2),3)),(12,(((6,15),11),(10,8)))),5,1); tree rep.1710000 = (((((14,((7,13),(5,3))),4),(9,2)),((10,((15,6),11)),8)),12,1); tree rep.1720000 = (12,(((((13,7),4),(5,14)),((9,2),3)),((10,8),((15,11),6))),1); tree rep.1730000 = (((((6,(11,15)),10),8),(((2,9),3),((14,(5,(13,7))),4))),12,1); tree rep.1740000 = (((((9,2),3),(14,5)),((13,7),4)),(((8,10),((11,15),6)),12),1); tree rep.1750000 = (((8,(11,(15,6))),10),(((3,(9,2)),12),(((4,(7,13)),14),5)),1); tree rep.1760000 = (((((13,7),4),(14,((3,(2,9)),5))),12),(8,(10,(6,(15,11)))),1); tree rep.1770000 = (((10,8),(11,(15,6))),(((13,7),4),((5,14),(12,((2,9),3)))),1); tree rep.1780000 = ((12,((5,(14,((13,7),((2,9),3)))),4)),(((6,15),11),(8,10)),1); tree rep.1790000 = (12,((6,(11,15)),(((((9,2),3),4),((13,7),(14,5))),(10,8))),1); tree rep.1800000 = ((((3,((13,7),(14,4))),5),(9,2)),(12,((15,(6,11)),(8,10))),1); tree rep.1810000 = (12,((((15,6),11),(8,10)),(((5,(3,(2,9))),(4,(13,7))),14)),1); tree rep.1820000 = (((((((15,11),6),(10,8)),((9,2),3)),5),(((13,7),4),14)),12,1); tree rep.1830000 = ((((10,8),((6,15),11)),((5,(((13,7),4),14)),(3,(9,2)))),12,1); tree rep.1840000 = (((12,(11,(15,6))),(8,10)),((((9,2),3),(7,13)),((14,4),5)),1); tree rep.1850000 = ((10,((((14,(((7,13),5),4)),((2,9),3)),((6,15),11)),8)),12,1); tree rep.1860000 = (((((4,(13,7)),((2,9),3)),(5,14)),((10,8),((15,6),11))),12,1); tree rep.1870000 = (((10,(8,((6,15),11))),((5,(3,(9,2))),((4,14),(7,13)))),12,1); tree rep.1880000 = (12,((10,(((15,6),11),8)),((7,13),(((4,5),14),((9,2),3)))),1); tree rep.1890000 = ((((3,(2,9)),((14,5),((7,13),4))),12),(10,(((11,15),6),8)),1); tree rep.1900000 = (((8,10),(6,(15,11))),(12,(((3,(2,9)),5),(14,(4,(7,13))))),1); tree rep.1910000 = ((((14,(((13,7),4),(9,2))),(5,3)),(((6,(11,15)),10),8)),12,1); tree rep.1920000 = (12,((3,(9,2)),((((13,7),5),(4,14)),(8,(10,((6,15),11))))),1); tree rep.1930000 = (((((5,(4,(7,13))),14),((9,2),3)),((10,8),(11,(6,15)))),12,1); tree rep.1940000 = (12,((13,(4,(((9,2),3),(7,(14,5))))),((10,8),((15,11),6))),1); tree rep.1950000 = (((8,10),(11,(6,15))),((((((13,7),4),3),((9,2),14)),5),12),1); tree rep.1960000 = ((((14,((13,7),4)),(((2,9),3),5)),((10,8),((6,15),11))),12,1); tree rep.1970000 = ((12,((10,8),(6,(15,11)))),((14,5),(((9,2),3),(4,(7,13)))),1); tree rep.1980000 = (12,(((((15,11),6),8),10),(5,(((9,2),3),((13,7),(4,14))))),1); tree rep.1990000 = (12,(((((2,9),3),(5,(14,(4,(7,13))))),(10,8)),((11,15),6)),1); tree rep.2000000 = (12,((((7,13),3),(14,(((9,2),5),4))),((10,8),((6,11),15))),1); tree rep.2010000 = (12,((((15,6),11),(8,10)),((5,(((4,7),13),14)),(3,(9,2)))),1); tree rep.2020000 = ((14,(((9,2),3),((4,(13,7)),5))),(12,(8,(10,(11,(15,6))))),1); tree rep.2030000 = (((((3,(9,2)),((7,4),13)),(14,5)),((10,8),(11,(15,6)))),12,1); tree rep.2040000 = ((5,((3,(9,2)),((((6,(11,15)),8),10),(((7,13),4),14)))),12,1); tree rep.2050000 = ((12,(5,(((9,2),3),(14,((13,7),4))))),((10,((11,15),6)),8),1); tree rep.2060000 = ((((((11,15),6),8),10),12),((4,((14,5),(7,13))),(3,(9,2))),1); tree rep.2070000 = (((14,(5,(((9,2),3),((7,13),4)))),(((15,6),11),(10,8))),12,1); tree rep.2080000 = (((((9,2),3),(((7,13),4),14)),(5,((8,(11,(15,6))),10))),12,1); tree rep.2090000 = (12,(((11,(6,15)),(10,8)),(((4,14),(13,7)),(((2,9),3),5))),1); tree rep.2100000 = ((((((15,6),11),8),10),((3,(2,9)),((14,(4,(13,7))),5))),12,1); tree rep.2110000 = (12,(((14,(((2,9),3),5)),((7,13),4)),(10,(8,((15,6),11)))),1); tree rep.2120000 = (12,((8,(((15,6),11),10)),((14,(4,(13,7))),(((9,2),3),5))),1); tree rep.2130000 = ((((((15,11),6),8),10),(((5,14),((2,9),3)),(4,(7,13)))),12,1); tree rep.2140000 = (((6,(15,11)),(10,8)),(((5,(3,(2,9))),(14,((13,7),4))),12),1); tree rep.2150000 = (12,(((8,10),((6,15),11)),((5,14),(((2,9),3),(4,(13,7))))),1); tree rep.2160000 = (12,((((9,2),3),((14,(5,4)),(7,13))),(8,(10,(11,(6,15))))),1); tree rep.2170000 = (((11,(15,6)),(10,8)),(((((13,7),(14,4)),(3,(9,2))),5),12),1); tree rep.2180000 = ((((6,(15,11)),(8,10)),(((14,5),(4,(3,(9,2)))),(13,7))),12,1); tree rep.2190000 = (12,((5,((14,((7,13),4)),((2,9),3))),((8,10),((15,6),11))),1); tree rep.2200000 = ((((((14,5),3),4),((13,7),(2,9))),(((15,6),11),(8,10))),12,1); tree rep.2210000 = ((((((((7,13),4),((2,9),3)),(14,5)),((11,15),6)),10),8),12,1); tree rep.2220000 = (((8,((6,(11,15)),10)),(((13,7),4),((14,5),(3,(9,2))))),12,1); tree rep.2230000 = (12,(((8,10),((4,(7,13)),((5,14),((9,2),3)))),(11,(15,6))),1); tree rep.2240000 = (((((5,(7,13)),14),(4,((2,9),3))),(10,((6,(11,15)),8))),12,1); tree rep.2250000 = (((((15,6),11),(10,8)),((5,(3,(9,2))),((7,13),(4,14)))),12,1); tree rep.2260000 = (12,(((5,(14,(7,13))),(4,((2,9),3))),(10,(8,((6,15),11)))),1); tree rep.2270000 = (((((14,(13,7)),(4,5)),((2,9),3)),(8,((11,(6,15)),10))),12,1); tree rep.2280000 = ((((10,((6,15),11)),8),(((14,(7,13)),4),(5,((9,2),3)))),12,1); tree rep.2290000 = ((((8,10),(14,(((9,2),3),(5,((7,13),4))))),12),(11,(15,6)),1); tree rep.2300000 = (((((11,6),15),(8,10)),(((9,2),3),(5,(14,(4,(7,13)))))),12,1); tree rep.2310000 = (12,(5,(((4,(7,13)),14),(((2,9),3),(((15,6),11),(10,8))))),1); tree rep.2320000 = (12,(((4,14),((2,9),3)),(((8,(((6,15),11),10)),(13,7)),5)),1); tree rep.2330000 = ((((10,8),(11,(15,6))),(((14,5),(((7,13),4),(2,9))),3)),12,1); tree rep.2340000 = ((((5,(7,13)),((3,(2,9)),(4,14))),12),(10,(((11,15),6),8)),1); tree rep.2350000 = ((((((9,2),3),((4,14),(7,13))),5),(10,(8,(11,(15,6))))),12,1); tree rep.2360000 = (((5,(((4,(7,13)),14),((9,2),3))),((6,(15,11)),(10,8))),12,1); tree rep.2370000 = (12,((((11,15),6),(8,10)),(((7,13),((4,((9,2),3)),5)),14)),1); tree rep.2380000 = ((8,(10,(15,(6,11)))),(12,((3,(9,2)),(14,((4,(7,13)),5)))),1); tree rep.2390000 = (((((14,5),(3,(9,2))),(4,(7,13))),((10,8),((15,6),11))),12,1); tree rep.2400000 = (12,(((6,(11,15)),(10,8)),(((4,(14,(13,7))),5),((2,9),3))),1); tree rep.2410000 = (((((3,(2,9)),5),((7,13),(14,4))),(((11,(6,15)),8),10)),12,1); tree rep.2420000 = ((5,(((13,7),(14,4)),(3,(9,2)))),(((10,8),(6,(11,15))),12),1); tree rep.2430000 = ((((6,(11,15)),10),8),((5,12),((3,(9,2)),((13,7),(14,4)))),1); tree rep.2440000 = (12,((((8,10),(6,(11,15))),((9,2),3)),((7,13),((5,14),4))),1); tree rep.2450000 = ((12,((8,(10,(6,(15,11)))),(14,((7,13),(4,5))))),((2,9),3),1); tree rep.2460000 = (12,((5,(((13,7),14),((10,8),((6,15),11)))),(((9,2),3),4)),1); tree rep.2470000 = ((((10,8),(6,(15,11))),((4,(13,7)),((3,(9,2)),(5,14)))),12,1); tree rep.2480000 = (12,((((4,(7,13)),((8,10),(6,(15,11)))),((9,2),3)),(5,14)),1); tree rep.2490000 = ((((7,13),(14,(4,(((9,2),3),5)))),(8,((6,(15,11)),10))),12,1); tree rep.2500000 = ((((((9,2),3),((13,7),5)),(14,4)),((10,(6,(15,11))),8)),12,1); tree rep.2510000 = ((12,(((2,(3,9)),5),(((7,13),4),14))),(8,(10,(6,(11,15)))),1); tree rep.2520000 = (12,(((11,(15,6)),(10,8)),((14,(((13,7),4),(3,(2,9)))),5)),1); tree rep.2530000 = ((((11,(15,6)),8),10),(12,(5,(((14,(13,7)),4),((2,9),3)))),1); tree rep.2540000 = ((((11,(6,15)),(8,10)),((5,((9,2),3)),(((13,7),14),4))),12,1); tree rep.2550000 = (12,(((10,8),(6,(11,15))),((5,(14,((7,13),4))),(3,(2,9)))),1); tree rep.2560000 = (12,(((14,(4,(13,7))),((5,3),(9,2))),((6,(11,15)),(10,8))),1); tree rep.2570000 = (((11,(15,6)),(10,8)),(12,((14,5),((4,(7,13)),((2,9),3)))),1); tree rep.2580000 = (12,((((3,(2,9)),5),(4,(14,(7,13)))),(((11,(15,6)),8),10)),1); tree rep.2590000 = (((10,((11,15),6)),8),(((5,(3,(9,2))),((14,4),(13,7))),12),1); tree rep.2600000 = ((((5,((14,((13,7),4)),3)),(2,9)),(((11,(6,15)),8),10)),12,1); tree rep.2610000 = (((8,(((15,6),11),10)),12),(((14,5),(3,(9,2))),(4,(7,13))),1); tree rep.2620000 = (((11,(6,15)),(10,8)),(((((7,13),(14,5)),4),(3,(9,2))),12),1); tree rep.2630000 = (12,(5,((((4,14),(13,7)),((9,2),3)),((8,10),((6,15),11)))),1); tree rep.2640000 = (12,(((5,14),((4,(7,13)),(3,(9,2)))),(10,((6,(11,15)),8))),1); tree rep.2650000 = (12,((5,(((14,(13,7)),4),((9,2),3))),(10,(8,(11,(15,6))))),1); tree rep.2660000 = (12,((7,(((13,14),(5,(3,(9,2)))),4)),(10,(8,((11,15),6)))),1); tree rep.2670000 = ((8,10),((6,(11,15)),(((5,((9,2),3)),(((7,13),14),4)),12)),1); tree rep.2680000 = ((((5,((7,13),((2,9),3))),(4,14)),((15,(11,6)),(8,10))),12,1); tree rep.2690000 = (12,(((10,(6,(15,11))),8),(((((7,13),4),14),5),((9,2),3))),1); tree rep.2700000 = ((((8,10),((6,15),11)),((5,((3,(9,2)),14)),((7,13),4))),12,1); tree rep.2710000 = (((4,((13,7),14)),(((2,9),3),5)),(((10,8),(11,(15,6))),12),1); tree rep.2720000 = (((5,(((4,(13,7)),(3,(2,9))),14)),((8,((15,11),6)),10)),12,1); tree rep.2730000 = (((((13,7),4),(5,(14,(3,(9,2))))),((10,8),((15,11),6))),12,1); tree rep.2740000 = (12,(((8,10),(6,(11,15))),(5,((((7,13),14),4),((9,2),3)))),1); tree rep.2750000 = (((((13,7),(14,4)),((3,(9,2)),5)),((8,((15,6),11)),10)),12,1); tree rep.2760000 = ((((((13,7),(2,9)),3),((4,14),5)),((11,(6,15)),(8,10))),12,1); tree rep.2770000 = ((((14,(3,(2,9))),(5,(4,(13,7)))),((10,8),(11,(15,6)))),12,1); tree rep.2780000 = (12,((((5,14),(((2,9),3),4)),(13,7)),(((11,15),6),(8,10))),1); tree rep.2790000 = (12,(((13,7),4),(((14,5),((2,9),3)),((10,((15,6),11)),8))),1); tree rep.2800000 = ((((8,((6,15),11)),10),((((2,(3,9)),((7,13),14)),4),5)),12,1); tree rep.2810000 = ((((5,(3,(9,2))),(14,(4,(7,13)))),(((11,(6,15)),10),8)),12,1); tree rep.2820000 = (12,(((10,((11,15),6)),8),((4,((13,7),14)),((3,(9,2)),5))),1); tree rep.2830000 = ((12,((11,(6,15)),(8,10))),(((9,2),3),(5,((14,4),(7,13)))),1); tree rep.2840000 = ((10,(8,((11,15),6))),((5,(14,(((7,13),4),(3,(9,2))))),12),1); tree rep.2850000 = (((11,(6,15)),(8,10)),((((9,2),3),((((13,7),4),14),5)),12),1); tree rep.2860000 = ((((15,6),11),(10,8)),(((12,5),(3,(9,2))),(14,((7,13),4))),1); tree rep.2870000 = (12,(((((13,7),4),((2,9),3)),(5,14)),((8,10),(11,(6,15)))),1); tree rep.2880000 = ((((8,10),(15,(6,11))),(((7,13),((3,(2,9)),(4,14))),5)),12,1); tree rep.2890000 = (12,(((10,(6,(15,11))),8),((7,13),(((5,14),((2,9),3)),4))),1); tree rep.2900000 = (12,((5,(3,(9,2))),((8,((6,(11,15)),10)),(((7,13),4),14))),1); tree rep.2910000 = ((((10,((15,6),11)),8),12),((((14,4),(13,7)),((2,9),3)),5),1); tree rep.2920000 = (12,((((6,15),11),(10,8)),((5,(((4,(7,13)),14),3)),(9,2))),1); tree rep.2930000 = (((((9,2),3),(14,(5,(4,(13,7))))),((8,10),(11,(15,6)))),12,1); tree rep.2940000 = (12,(((3,(2,9)),(((13,7),(14,5)),4)),(((11,(6,15)),8),10)),1); tree rep.2950000 = ((((((7,13),14),4),5),(12,((2,9),3))),((8,((11,15),6)),10),1); tree rep.2960000 = (((4,(((13,7),3),((2,9),5))),14),(12,((10,8),((6,15),11))),1); tree rep.2970000 = (12,(((((13,7),(14,5)),4),((9,2),3)),((6,(15,11)),(10,8))),1); tree rep.2980000 = (((10,(8,((14,(5,((7,13),(3,(2,9))))),4))),((11,15),6)),12,1); tree rep.2990000 = (((((6,11),15),(8,10)),(((14,4),(13,7)),((5,3),(2,9)))),12,1); tree rep.3000000 = (12,((5,(((2,9),3),(((15,11),6),(10,8)))),(14,((13,7),4))),1); tree rep.3010000 = ((((((13,7),4),14),((3,(2,9)),5)),((6,(11,15)),(8,10))),12,1); tree rep.3020000 = (12,(((14,5),(((9,2),3),(4,(13,7)))),((11,(6,15)),(8,10))),1); tree rep.3030000 = ((((8,(11,(15,6))),10),(((9,2),3),((5,14),((13,7),4)))),12,1); tree rep.3040000 = ((((8,(6,(15,11))),10),(5,((4,(14,(7,13))),((2,9),3)))),12,1); tree rep.3050000 = (12,((((((13,7),14),4),((2,9),3)),5),((8,10),((15,6),11))),1); tree rep.3060000 = (12,(((10,8),(6,(11,15))),(((7,4),13),((3,(2,9)),(14,5)))),1); tree rep.3070000 = ((((11,(15,6)),10),8),(((3,(2,9)),(((13,7),(14,5)),4)),12),1); tree rep.3080000 = (12,((8,((6,(11,15)),10)),((((13,7),(4,14)),(3,(2,9))),5)),1); tree rep.3090000 = (12,((((3,(2,9)),5),((14,(7,13)),4)),(10,(8,((15,6),11)))),1); tree rep.3100000 = (((((15,6),11),(((2,9),(5,(14,(4,(13,7))))),3)),(10,8)),12,1); tree rep.3110000 = (((10,((11,(15,6)),8)),((3,(2,9)),((14,5),(7,(4,13))))),12,1); tree rep.3120000 = (((((14,4),(13,7)),(((2,9),3),5)),((8,(11,(15,6))),10)),12,1); tree rep.3130000 = ((12,((6,(11,15)),(8,10))),((((2,9),3),(14,(4,(7,13)))),5),1); tree rep.3140000 = (((10,((11,(6,15)),8)),(((4,(14,(7,13))),5),((2,9),3))),12,1); tree rep.3150000 = (((10,8),((((9,2),3),((14,((7,13),4)),5)),((6,15),11))),12,1); tree rep.3160000 = (((10,(((15,11),6),8)),((5,14),((4,(3,(2,9))),(7,13)))),12,1); tree rep.3170000 = ((5,(((((13,7),4),12),(3,(2,9))),14)),((10,8),((15,6),11)),1); tree rep.3180000 = ((((((9,2),3),((4,(14,5)),(13,7))),(6,(11,15))),(8,10)),12,1); tree rep.3190000 = (((((15,11),6),(10,8)),(5,(((9,2),3),(((13,7),4),14)))),12,1); tree rep.3200000 = (((((15,11),6),8),10),(12,(((5,(4,(13,7))),14),(3,(9,2)))),1); tree rep.3210000 = (((((3,(9,2)),(7,13)),(4,(5,14))),((11,(15,6)),(8,10))),12,1); tree rep.3220000 = (((5,((4,(14,(13,7))),((9,2),3))),((10,((15,6),11)),8)),12,1); tree rep.3230000 = ((((((4,(13,7)),14),((9,2),3)),5),((10,8),((15,6),11))),12,1); tree rep.3240000 = ((((5,((3,9),2)),(14,(4,(13,7)))),((10,(11,(15,6))),8)),12,1); tree rep.3250000 = (12,(((15,(11,6)),(10,8)),(4,(((14,5),((9,2),3)),(7,13)))),1); tree rep.3260000 = (12,((8,((11,(6,15)),10)),((((7,13),4),((2,9),3)),(5,14))),1); tree rep.3270000 = (((14,(4,(5,(13,7)))),(((((6,15),11),8),10),(3,(2,9)))),12,1); tree rep.3280000 = (((10,(8,((6,11),15))),12),((4,14),((3,(2,9)),(5,(13,7)))),1); tree rep.3290000 = (((8,10),((15,6),11)),((14,((7,13),(5,(12,(3,(2,9)))))),4),1); tree rep.3300000 = ((((5,((2,9),3)),((4,14),(7,13))),((10,8),((11,15),6))),12,1); tree rep.3310000 = (((10,((6,(15,11)),8)),(5,(((9,2),3),((14,4),(13,7))))),12,1); tree rep.3320000 = ((((((14,(4,13)),7),5),(3,(9,2))),((10,8),(6,(11,15)))),12,1); tree rep.3330000 = (12,((8,(((15,11),6),10)),((14,(7,13)),(4,(5,((9,2),3))))),1); tree rep.3340000 = (12,((3,(9,2)),(5,(((4,(13,7)),14),(10,(((6,11),15),8))))),1); tree rep.3350000 = (12,((10,(((15,11),6),8)),(((9,2),3),((14,5),(4,(7,13))))),1); tree rep.3360000 = (12,(((10,8),(((14,(13,7)),4),(5,(3,(9,2))))),((11,6),15)),1); tree rep.3370000 = ((12,((14,(13,7)),4)),(((9,2),(5,3)),(((11,15),6),(10,8))),1); tree rep.3380000 = ((((4,((14,5),(7,13))),(3,(2,9))),(8,((6,(15,11)),10))),12,1); tree rep.3390000 = ((((3,(2,9)),5),((14,4),(13,7))),((((15,11),6),(10,8)),12),1); tree rep.3400000 = (((10,((11,(6,15)),8)),(((7,13),(14,(5,4))),(3,(9,2)))),12,1); tree rep.3410000 = (12,((((9,2),3),((((13,7),4),14),5)),(10,(((15,11),6),8))),1); tree rep.3420000 = (12,(((((4,(13,7)),((2,9),3)),(((6,15),11),(8,10))),14),5),1); tree rep.3430000 = (12,(((7,13),((4,((3,(2,9)),5)),14)),((10,8),((11,15),6))),1); tree rep.3440000 = (((14,((3,(2,9)),5)),((10,(((11,15),6),8)),(13,7))),(12,4),1); tree rep.3450000 = ((((4,(7,13)),((5,14),((9,2),3))),((15,(11,6)),(10,8))),12,1); tree rep.3460000 = (((((15,6),11),(10,8)),(((3,(9,2)),5),(14,((13,7),4)))),12,1); tree rep.3470000 = ((((9,2),3),(5,((4,14),(7,13)))),((((15,6),11),(10,8)),12),1); tree rep.3480000 = (12,(((8,(11,(6,15))),10),((((9,2),3),(14,5)),(4,(7,13)))),1); tree rep.3490000 = (((5,((3,14),(((7,13),4),(9,2)))),(10,((6,(15,11)),8))),12,1); tree rep.3500000 = (12,((((2,9),3),(5,(((13,7),4),14))),(10,(8,(15,(11,6))))),1); tree rep.3510000 = (12,(((14,5),((4,(7,13)),(3,(9,2)))),(10,(((15,6),11),8))),1); tree rep.3520000 = ((12,(((3,(2,9)),(5,14)),(4,(13,7)))),(((11,(6,15)),8),10),1); tree rep.3530000 = ((((6,(11,15)),(10,8)),(((13,7),4),((2,9),(3,(5,14))))),12,1); tree rep.3540000 = ((((5,(3,(2,9))),((4,(13,7)),14)),((10,8),((6,15),11))),12,1); tree rep.3550000 = (12,((3,(2,9)),((5,14),((4,(13,7)),((10,8),(11,(6,15)))))),1); tree rep.3560000 = (12,((((((9,2),3),(7,13)),5),4),(((8,(15,(11,6))),10),14)),1); tree rep.3570000 = ((((10,8),(11,(6,15))),((5,(14,(4,(13,7)))),(3,(9,2)))),12,1); tree rep.3580000 = (12,((((9,2),3),((5,14),((7,13),4))),(8,(10,(15,(11,6))))),1); tree rep.3590000 = ((((8,(6,(15,11))),10),(((9,2),3),((14,(4,(13,7))),5))),12,1); tree rep.3600000 = (12,(((((4,(7,13)),14),(((6,(15,11)),10),8)),5),((9,2),3)),1); tree rep.3610000 = (((5,((14,(4,(7,13))),(3,(9,2)))),((8,10),(11,(6,15)))),12,1); tree rep.3620000 = (12,((((((7,13),(8,(10,(11,(6,15))))),5),14),4),((9,2),3)),1); tree rep.3630000 = ((12,((6,(11,15)),(10,8))),((5,14),((3,(9,2)),(7,(4,13)))),1); tree rep.3640000 = ((((8,(11,(15,6))),10),((5,((2,9),3)),(((13,7),4),14))),12,1); tree rep.3650000 = (((5,(3,(9,2))),((14,4),(13,7))),(12,(10,((11,(15,6)),8))),1); tree rep.3660000 = ((((4,(7,13)),(5,14)),((9,2),3)),(12,((8,10),((11,15),6))),1); tree rep.3670000 = (12,((((11,(15,6)),8),10),(5,(4,(((9,2),3),((7,13),14))))),1); tree rep.3680000 = ((((((9,2),3),((4,(7,13)),14)),5),(((15,6),11),(10,8))),12,1); tree rep.3690000 = (12,(((8,10),((15,11),6)),((3,(9,2)),((14,((13,7),4)),5))),1); tree rep.3700000 = ((14,(((4,(7,13)),5),((9,2),3))),(12,(8,((6,(15,11)),10))),1); tree rep.3710000 = ((((5,14),(((7,13),4),(3,(2,9)))),12),(8,((11,(15,6)),10)),1); tree rep.3720000 = (12,(((5,((2,9),3)),((7,13),(4,14))),((8,((15,6),11)),10)),1); tree rep.3730000 = (12,(((5,((9,2),3)),((14,(7,13)),4)),((15,(6,11)),(8,10))),1); tree rep.3740000 = (((4,(9,(13,7))),((3,2),((14,5),(((11,15),6),(8,10))))),12,1); tree rep.3750000 = ((((14,5),(((7,13),4),(3,(9,2)))),12),((10,8),((15,6),11)),1); tree rep.3760000 = (((8,10),((5,((9,2),(((4,(7,13)),14),3))),((11,15),6))),12,1); tree rep.3770000 = ((((5,(((13,7),4),(3,(9,2)))),14),((10,((11,15),6)),8)),12,1); tree rep.3780000 = (12,(5,((14,(4,(13,7))),(((11,(15,6)),(8,10)),(3,(2,9))))),1); tree rep.3790000 = (12,((((10,8),((15,6),11)),((3,(9,2)),(14,(13,7)))),(5,4)),1); tree rep.3800000 = (((((11,15),6),(8,10)),(((9,2),3),(14,(((7,13),4),5)))),12,1); tree rep.3810000 = ((((6,(11,15)),(8,10)),(((7,13),(4,((9,2),3))),(5,14))),12,1); tree rep.3820000 = (((((15,6),11),(8,10)),((((13,7),4),14),(5,((9,2),3)))),12,1); tree rep.3830000 = (((((15,6),11),8),10),((5,((9,2),3)),((12,14),(4,(13,7)))),1); tree rep.3840000 = (12,((((13,7),4),(((3,(9,2)),14),5)),((10,(11,(6,15))),8)),1); tree rep.3850000 = (12,(((14,(4,(7,13))),(5,(3,(9,2)))),(10,(((15,6),11),8))),1); tree rep.3860000 = (((4,(13,7)),(((2,9),3),(5,14))),(((8,((11,15),6)),10),12),1); tree rep.3870000 = (12,(((((6,15),11),10),8),((4,((((9,2),3),(7,13)),14)),5)),1); tree rep.3880000 = (((5,((14,(4,(13,7))),(3,(9,2)))),(10,((11,(15,6)),8))),12,1); tree rep.3890000 = (12,(((3,(9,2)),(((4,(7,13)),14),5)),(((11,6),15),(8,10))),1); tree rep.3900000 = (((((9,2),3),((14,(4,(13,7))),5)),(10,(((15,11),6),8))),12,1); tree rep.3910000 = ((((3,(9,2)),((7,13),14)),(5,4)),(((10,(6,(15,11))),8),12),1); tree rep.3920000 = ((((8,10),((11,15),6)),(((4,(7,13)),14),(5,((2,9),3)))),12,1); tree rep.3930000 = (((12,(14,(4,(13,7)))),((3,(2,9)),5)),((10,8),(11,(15,6))),1); tree rep.3940000 = (12,(((4,((14,5),((9,2),3))),(13,7)),(((15,(11,6)),10),8)),1); tree rep.3950000 = (12,(((10,(6,(15,11))),8),(((14,5),(4,(13,7))),((9,2),3))),1); tree rep.3960000 = (12,(((3,(9,2)),((5,14),((7,13),4))),((10,8),((11,15),6))),1); tree rep.3970000 = (((14,((((7,13),4),5),(3,(9,2)))),((8,10),(6,(15,11)))),12,1); tree rep.3980000 = (((5,((((9,2),3),14),((13,7),4))),(10,(8,(11,(6,15))))),12,1); tree rep.3990000 = ((((4,((14,5),(3,(2,9)))),((8,10),((15,11),6))),(7,13)),12,1); tree rep.4000000 = (12,(((((2,9),3),5),(14,(4,(7,13)))),(10,((6,(11,15)),8))),1); tree rep.4010000 = ((((15,6),11),(10,8)),((12,((9,2),3)),((5,14),((13,7),4))),1); tree rep.4020000 = (12,((((15,6),11),(8,10)),(((((2,9),3),(14,5)),4),(7,13))),1); tree rep.4030000 = ((((((9,2),3),((8,10),(11,(15,6)))),(((13,7),4),14)),5),12,1); tree rep.4040000 = (12,(((((11,15),6),10),8),((13,(7,4)),((((9,2),3),14),5))),1); tree rep.4050000 = (((8,(10,((11,15),6))),(5,((4,((2,9),3)),(14,(7,13))))),12,1); tree rep.4060000 = ((((6,(11,15)),(10,8)),(((13,7),14),(((2,9),3),(5,4)))),12,1); tree rep.4070000 = ((((((3,(2,9)),5),((13,7),4)),14),(((15,6),11),(8,10))),12,1); tree rep.4080000 = ((((((2,9),3),5),(14,(4,(13,7)))),12),(((11,(6,15)),8),10),1); tree rep.4090000 = (((((13,(7,4)),(5,14)),(3,(2,9))),(8,(10,((15,11),6)))),12,1); tree rep.4100000 = (((((9,2),3),(5,(14,((7,13),4)))),((6,(11,15)),(10,8))),12,1); tree rep.4110000 = ((((10,8),(11,(6,15))),((3,(9,2)),((4,(14,(13,7))),5))),12,1); tree rep.4120000 = (((((9,2),3),(((4,14),(7,13)),5)),(10,(8,((15,6),11)))),12,1); tree rep.4130000 = ((((10,8),(11,(6,15))),12),((3,5),((9,2),((7,13),(4,14)))),1); tree rep.4140000 = (12,((4,((14,(7,13)),(5,((9,2),3)))),((8,10),((15,11),6))),1); tree rep.4150000 = (((10,(8,((6,15),11))),((5,((14,4),(7,13))),((9,2),3))),12,1); tree rep.4160000 = (12,((5,(((9,2),3),((4,14),(13,7)))),(((6,(15,11)),10),8)),1); tree rep.4170000 = (12,(((((9,2),3),((14,(7,13)),5)),4),(((15,11),6),(8,10))),1); tree rep.4180000 = (12,(((5,(3,(9,2))),((4,14),(7,13))),(10,(((15,6),11),8))),1); tree rep.4190000 = (((((15,6),11),(8,10)),12),((5,(3,(2,9))),((13,7),(4,14))),1); tree rep.4200000 = ((((8,(6,(15,11))),10),(((3,(2,9)),((7,13),(14,4))),5)),12,1); tree rep.4210000 = (((((11,15),6),8),10),((((((13,7),14),4),5),(3,(2,9))),12),1); tree rep.4220000 = ((((7,13),4),(((5,((10,8),((6,15),11))),(3,(2,9))),14)),12,1); tree rep.4230000 = (((((11,15),6),(10,8)),12),(4,((5,((7,14),13)),((2,9),3))),1); tree rep.4240000 = ((((14,((7,13),4)),5),((9,2),3)),(((((11,15),6),8),10),12),1); tree rep.4250000 = (((((15,6),11),(10,8)),(((4,(3,(2,9))),(14,(7,13))),5)),12,1); tree rep.4260000 = ((((3,(2,9)),(4,(14,((13,7),5)))),((10,8),((15,6),11))),12,1); tree rep.4270000 = (5,((((11,(6,15)),(10,8)),(((13,7),4),14)),(12,((9,2),3))),1); tree rep.4280000 = (((10,((15,6),11)),8),(12,((5,(((2,9),3),(4,14))),(7,13))),1); tree rep.4290000 = ((12,(10,8)),(((15,6),11),((5,((14,(13,7)),4)),((9,2),3))),1); tree rep.4300000 = (12,(((((9,(3,2)),4),(7,13)),(14,5)),(10,((15,(6,11)),8))),1); tree rep.4310000 = (12,(((((13,7),4),14),(5,(3,(9,2)))),((10,((15,11),6)),8)),1); tree rep.4320000 = (((10,(8,(6,(15,11)))),(4,(((13,7),((2,9),(14,5))),3))),12,1); tree rep.4330000 = (((10,(((15,6),11),8)),(((13,7),4),((14,5),(3,(9,2))))),12,1); tree rep.4340000 = (((12,(5,(((4,14),(7,13)),(3,(9,2))))),(10,(11,(15,6)))),8,1); tree rep.4350000 = (12,((14,((13,7),4)),((((6,15),11),(10,8)),(((9,2),3),5))),1); tree rep.4360000 = ((((10,8),(6,(15,11))),(((7,(13,14)),4),(5,(3,(2,9))))),12,1); tree rep.4370000 = ((((((2,9),3),((14,4),(13,7))),5),((8,(6,(15,11))),10)),12,1); tree rep.4380000 = (((((9,2),3),(5,((7,13),(4,14)))),(8,(10,(6,(15,11))))),12,1); tree rep.4390000 = (((10,(8,(11,(6,15)))),(((14,(4,(7,13))),(3,(2,9))),5)),12,1); tree rep.4400000 = ((10,((11,(15,6)),8)),(12,(((5,14),(4,(7,13))),(3,(9,2)))),1); tree rep.4410000 = ((((((11,15),6),(10,8)),((((13,7),(9,2)),5),(3,4))),12),14,1); tree rep.4420000 = ((12,(((5,(9,2)),((14,4),(7,13))),3)),((10,8),((15,11),6)),1); tree rep.4430000 = ((((11,(15,6)),(8,10)),(14,(5,((3,(2,9)),(4,(13,7)))))),12,1); tree rep.4440000 = (12,((((7,13),4),((14,5),((2,9),3))),(10,(8,((11,15),6)))),1); tree rep.4450000 = (12,(((((7,13),4),((2,9),3)),(5,14)),((11,(6,15)),(10,8))),1); tree rep.4460000 = (((10,8),((11,(6,15)),12)),((((7,13),4),14),(5,(9,(3,2)))),1); tree rep.4470000 = (12,((((11,15),6),(10,8)),((5,((14,(7,13)),4)),((2,9),3))),1); tree rep.4480000 = (((8,10),((6,15),11)),(12,(((5,14),((7,13),4)),(3,(2,9)))),1); tree rep.4490000 = (((((2,9),3),((8,10),((15,6),11))),((5,(14,4)),(13,7))),12,1); tree rep.4500000 = ((((8,((6,11),15)),10),((14,((((9,2),3),5),(13,7))),4)),12,1); tree rep.4510000 = ((5,(12,((2,9),3))),((((6,15),11),(10,8)),(14,((13,7),4))),1); tree rep.4520000 = (((8,(10,(6,(15,11)))),(((5,((((9,2),13),7),14)),4),3)),12,1); tree rep.4530000 = (((15,6),11),(12,((((((9,2),3),(14,(4,(13,7)))),5),8),10)),1); tree rep.4540000 = (12,((((((((2,9),3),5),((13,7),4)),14),10),8),((6,15),11)),1); tree rep.4550000 = (12,(((((2,9),5),3),((13,7),(14,4))),(((6,15),11),(10,8))),1); tree rep.4560000 = (((((2,9),3),((4,(13,7)),(14,5))),((10,(6,(15,11))),8)),12,1); tree rep.4570000 = ((((10,(8,(6,(15,11)))),((2,9),(3,12))),5),(14,(4,(7,13))),1); tree rep.4580000 = (((((14,((13,7),4)),5),(3,(9,2))),((8,(6,(15,11))),10)),12,1); tree rep.4590000 = (12,(((5,14),((3,(9,2)),((7,13),4))),(((15,6),11),(8,10))),1); tree rep.4600000 = (12,((((3,(2,9)),((13,7),(4,14))),5),((15,(11,6)),(10,8))),1); tree rep.4610000 = ((((7,(13,4)),((8,10),((15,11),6))),((5,((2,9),3)),14)),12,1); tree rep.4620000 = (((10,(((6,11),15),8)),((5,((9,2),3)),((4,14),(7,13)))),12,1); tree rep.4630000 = ((((5,((4,((13,7),(3,(9,2)))),14)),((11,15),6)),(8,10)),12,1); tree rep.4640000 = (((10,8),(6,(15,11))),(5,(14,(((7,13),4),(((9,2),3),12)))),1); tree rep.4650000 = (((7,13),(((((((11,15),6),(8,10)),5),(2,(9,3))),4),14)),12,1); tree rep.4660000 = ((((2,9),3),(((14,(7,(13,4))),5),((6,(15,11)),(8,10)))),12,1); tree rep.4670000 = (12,(((3,(2,9)),(((7,13),4),(5,14))),((10,((15,6),11)),8)),1); tree rep.4680000 = (((((9,2),3),(5,(14,((7,4),13)))),((10,(11,(15,6))),8)),12,1); tree rep.4690000 = (12,(((14,5),(((13,7),4),(3,(2,9)))),((8,((6,15),11)),10)),1); tree rep.4700000 = (12,((10,(((15,11),6),8)),((4,(13,7)),((5,14),((2,9),3)))),1); tree rep.4710000 = (((10,(((6,15),11),8)),12),((3,(2,9)),((((13,4),7),14),5)),1); tree rep.4720000 = (12,(((((4,(7,13)),14),5),((2,9),3)),((6,(11,15)),(10,8))),1); tree rep.4730000 = (12,((8,((11,(15,6)),10)),(5,((3,(9,2)),((4,(13,7)),14)))),1); tree rep.4740000 = (12,(((11,(6,15)),(8,10)),((5,((14,4),(7,13))),(3,(2,9)))),1); tree rep.4750000 = (12,((((2,9),3),(((13,7),4),(5,14))),(((15,11),6),(8,10))),1); tree rep.4760000 = (((((15,6),11),(8,10)),(((14,5),((13,7),4)),(3,(2,9)))),12,1); tree rep.4770000 = (((14,((13,7),4)),(((2,9),3),5)),(12,((10,(6,(15,11))),8)),1); tree rep.4780000 = (((((10,8),(((13,7),4),(5,(11,(6,15))))),14),(3,(9,2))),12,1); tree rep.4790000 = ((((3,(2,9)),((13,7),4)),(14,5)),(12,((10,(6,(15,11))),8)),1); tree rep.4800000 = ((((((3,(9,2)),((7,13),4)),14),5),((10,(11,(15,6))),8)),12,1); tree rep.4810000 = (12,((((11,15),6),(10,8)),((((3,(2,9)),((7,13),4)),14),5)),1); tree rep.4820000 = (12,(((8,(6,(15,11))),10),(((2,9),3),((4,(7,13)),(14,5)))),1); tree rep.4830000 = (12,(5,((((8,10),(11,(6,15))),(14,(13,(7,4)))),((9,2),3))),1); tree rep.4840000 = (((10,(((11,6),15),8)),(((14,5),((7,13),4)),(3,(2,9)))),12,1); tree rep.4850000 = (12,((((4,(13,7)),(14,5)),((9,2),3)),((10,(11,(6,15))),8)),1); tree rep.4860000 = (((((3,(9,2)),5),((14,4),(13,7))),((((6,15),11),8),10)),12,1); tree rep.4870000 = ((((((10,8),((6,11),15)),((9,2),3)),(4,(14,(7,13)))),5),12,1); tree rep.4880000 = ((((8,10),(11,(6,15))),(5,((((4,7),13),14),((2,9),3)))),12,1); tree rep.4890000 = ((((((9,2),14),(3,(5,4))),(7,13)),((10,8),(6,(15,11)))),12,1); tree rep.4900000 = (((10,((15,6),11)),8),(12,((((2,9),(14,(4,(13,7)))),3),5)),1); tree rep.4910000 = ((((((15,11),6),10),8),((4,((7,13),14)),((3,(9,2)),5))),12,1); tree rep.4920000 = (((8,10),((15,11),6)),((((7,13),(14,4)),(((9,2),3),5)),12),1); tree rep.4930000 = (((14,((5,(3,(9,2))),(4,(13,7)))),(10,(((6,15),11),8))),12,1); tree rep.4940000 = (12,((((14,4),(7,13)),((8,10),(6,(11,15)))),(((9,2),3),5)),1); tree rep.4950000 = (12,((((15,6),11),(8,10)),((3,(((4,(7,13)),14),5)),(2,9))),1); tree rep.4960000 = (12,((8,(10,((15,6),11))),(((3,(9,2)),5),(((7,13),4),14))),1); tree rep.4970000 = ((3,(9,2)),(((5,14),((6,(11,15)),(10,8))),((13,7),(12,4))),1); tree rep.4980000 = (12,(((3,(2,9)),(5,(((13,7),14),4))),(((11,15),6),(10,8))),1); tree rep.4990000 = (((((6,11),15),(10,8)),12),((3,(((14,(7,13)),4),(2,9))),5),1); tree rep.5000000 = ((((((2,9),3),((10,8),((15,11),6))),(5,14)),(4,(7,13))),12,1); tree rep.5010000 = (((((((4,(7,13)),14),3),(((11,15),6),(8,10))),(2,9)),5),12,1); tree rep.5020000 = (((((2,9),3),(4,(5,((13,7),14)))),(8,(10,(11,(6,15))))),12,1); tree rep.5030000 = ((((6,(15,11)),(10,8)),((14,5),(((7,13),4),(3,(2,9))))),12,1); tree rep.5040000 = (12,(((((13,7),14),5),(4,(3,(2,9)))),(((11,15),6),(10,8))),1); tree rep.5050000 = ((4,(((3,(2,9)),(7,13)),((5,(8,(10,((15,11),6)))),14))),12,1); tree rep.5060000 = ((((4,(14,(7,13))),(((2,9),3),5)),((8,10),(11,(6,15)))),12,1); tree rep.5070000 = ((((((15,11),6),(10,8)),(3,(9,2))),((13,7),((5,14),4))),12,1); tree rep.5080000 = (12,(10,((((15,11),6),(((3,(9,2)),14),((4,(13,7)),5))),8)),1); tree rep.5090000 = ((((8,10),((15,6),11)),((4,((13,7),14)),((3,(9,2)),5))),12,1); tree rep.5100000 = (((((6,15),11),(10,8)),((5,14),((4,(7,13)),((2,9),3)))),12,1); tree rep.5110000 = ((((((13,7),4),14),(((2,9),3),5)),((((15,11),6),8),10)),12,1); tree rep.5120000 = (12,((((14,4),(7,13)),((3,(9,2)),5)),(((11,15),6),(10,8))),1); tree rep.5130000 = (12,(((5,((9,2),3)),(4,((13,7),14))),(10,((11,(15,6)),8))),1); tree rep.5140000 = (((10,((6,(11,15)),8)),(5,((3,(9,2)),((4,14),(13,7))))),12,1); tree rep.5150000 = ((((((15,6),11),10),8),((((13,7),4),((9,2),3)),(14,5))),12,1); tree rep.5160000 = ((((3,((((7,13),4),14),5)),(9,2)),(8,(((15,11),6),10))),12,1); tree rep.5170000 = ((((7,13),((5,(4,14)),((9,2),3))),((10,((6,15),11)),8)),12,1); tree rep.5180000 = ((((((9,2),3),((4,(13,7)),14)),5),(10,(8,((15,6),11)))),12,1); tree rep.5190000 = ((12,(8,(((15,6),11),10))),(((9,2),3),((14,((13,7),4)),5)),1); tree rep.5200000 = (((((7,13),(4,14)),(5,(3,(9,2)))),((8,(6,(15,11))),10)),12,1); tree rep.5210000 = (12,((10,(8,(6,(15,11)))),((3,(9,2)),(5,(4,((13,7),14))))),1); tree rep.5220000 = ((((((4,(7,13)),((9,2),3)),14),5),(((11,6),15),(8,10))),12,1); tree rep.5230000 = (12,(((8,(11,(6,15))),10),(((4,(13,7)),(14,5)),(3,(9,2)))),1); tree rep.5240000 = ((((((4,(13,7)),14),(3,(9,2))),5),(10,(8,((6,15),11)))),12,1); tree rep.5250000 = ((12,((10,8),(6,(11,15)))),(((14,4),((13,7),5)),(3,(9,2))),1); tree rep.5260000 = (12,(((8,10),(11,(15,6))),((14,((4,(13,7)),((9,2),3))),5)),1); tree rep.5270000 = (12,((14,(((2,9),3),(((7,13),4),5))),((8,(11,(6,15))),10)),1); tree rep.5280000 = (((((6,15),11),(10,8)),(((14,5),(4,(7,13))),((2,9),3))),12,1); tree rep.5290000 = (((((13,7),(14,4)),(((2,9),3),5)),(((6,15),11),(8,10))),12,1); tree rep.5300000 = ((14,(13,7)),((4,(((2,9),3),5)),(12,((8,((6,11),15)),10))),1); tree rep.5310000 = (12,(((10,8),(6,(15,11))),(5,(((3,2),9),(14,(4,(7,13)))))),1); tree rep.5320000 = (12,((((9,2),3),((4,5),(14,(13,7)))),(8,(10,(11,(15,6))))),1); tree rep.5330000 = ((((((7,13),4),(5,14)),(3,(9,2))),((15,(6,11)),(8,10))),12,1); tree rep.5340000 = ((((((13,7),4),14),(5,(3,(9,2)))),((8,(6,(15,11))),10)),12,1); tree rep.5350000 = ((((4,(13,7)),((5,14),((2,9),3))),((8,(11,(6,15))),10)),12,1); tree rep.5360000 = (((((6,15),11),(10,8)),((3,(2,9)),(((14,(13,7)),4),5))),12,1); tree rep.5370000 = ((((8,10),(11,(15,6))),(((((7,13),4),14),((9,2),3)),5)),12,1); tree rep.5380000 = ((((5,(((7,13),4),14)),(3,(9,2))),(((6,(11,15)),8),10)),12,1); tree rep.5390000 = (((5,((3,(9,2)),((4,(13,7)),14))),(8,(10,((15,11),6)))),12,1); tree rep.5400000 = (12,(((7,13),((14,5),(((2,9),3),4))),((8,10),((15,6),11))),1); tree rep.5410000 = (12,((((2,9),3),((((13,7),4),14),5)),(10,(((11,6),15),8))),1); tree rep.5420000 = (12,((5,(((13,(4,7)),14),(3,(2,9)))),(10,((11,(15,6)),8))),1); tree rep.5430000 = (12,(((3,(((4,(7,13)),(2,9)),14)),5),((10,(6,(15,11))),8)),1); tree rep.5440000 = (((10,(8,(11,(6,15)))),(((5,(3,(9,2))),(4,(13,7))),14)),12,1); tree rep.5450000 = ((((10,(6,(11,15))),8),((14,5),(((2,9),3),((7,4),13)))),12,1); tree rep.5460000 = ((((2,9),3),((((6,(15,11)),(10,8)),5),(((13,7),4),14))),12,1); tree rep.5470000 = (12,(((5,(14,((7,13),4))),(3,(2,9))),(((6,15),11),(10,8))),1); tree rep.5480000 = ((8,(10,(11,(6,15)))),(12,(5,(((2,9),3),(((7,13),4),14)))),1); tree rep.5490000 = (((((6,15),11),(10,8)),(((((7,13),4),14),5),(3,(9,2)))),12,1); tree rep.5500000 = ((5,((14,((9,2),3)),(4,(13,7)))),((8,((6,(11,15)),10)),12),1); tree rep.5510000 = ((12,((((4,(13,7)),14),((9,2),3)),5)),((10,((6,15),11)),8),1); tree rep.5520000 = ((12,((10,(6,(15,11))),8)),(((14,(4,(7,13))),5),(3,(9,2))),1); tree rep.5530000 = (12,(((3,(9,2)),((13,7),(4,(5,14)))),(((6,15),11),(10,8))),1); tree rep.5540000 = (12,((8,(10,(6,(11,15)))),(((4,(7,13)),(3,(9,2))),(5,14))),1); tree rep.5550000 = (((((15,(11,6)),8),10),(5,((14,((7,13),4)),((9,2),3)))),12,1); tree rep.5560000 = (12,(((14,5),(((7,13),4),((9,2),3))),((((6,15),11),8),10)),1); tree rep.5570000 = (((((2,9),3),((5,14),(4,(13,7)))),(((15,6),11),(8,10))),12,1); tree rep.5580000 = (((((4,((13,7),14)),(3,(9,2))),5),((((11,6),15),8),10)),12,1); tree rep.5590000 = (12,(((((5,((8,10),(11,(6,15)))),9),(2,3)),14),((7,13),4)),1); tree rep.5600000 = (12,(((10,8),((15,6),11)),((((3,5),14),(2,9)),((13,7),4))),1); tree rep.5610000 = (12,((((((((8,10),((6,11),15)),14),(13,7)),4),5),(2,9)),3),1); tree rep.5620000 = (12,((8,(10,((11,15),6))),(4,((13,7),((3,(9,2)),(14,5))))),1); tree rep.5630000 = (12,((5,(((2,9),3),((4,(13,7)),14))),((11,(6,15)),(10,8))),1); tree rep.5640000 = (((((3,(9,2)),((14,((7,13),4)),5)),(11,(6,15))),(8,10)),12,1); tree rep.5650000 = (12,(((3,(8,(10,((15,6),11)))),(((4,(7,13)),(2,9)),14)),5),1); tree rep.5660000 = (12,((((2,9),3),((7,(13,4)),(5,14))),((10,(11,(6,15))),8)),1); tree rep.5670000 = (((((11,(6,15)),(10,8)),(5,14)),(((2,9),3),(4,(7,13)))),12,1); tree rep.5680000 = (((((15,11),6),(10,8)),(4,((7,13),((3,(2,9)),(5,14))))),12,1); tree rep.5690000 = (12,((((15,11),6),(8,10)),((5,(14,((7,13),4))),(3,(9,2)))),1); tree rep.5700000 = (((((6,15),11),(10,8)),(5,(((9,2),3),(4,(14,(7,13)))))),12,1); tree rep.5710000 = (12,((8,(10,((11,15),6))),((5,4),(((7,13),14),((2,9),3)))),1); tree rep.5720000 = (((((15,11),6),(8,10)),12),(((2,9),3),((7,13),((5,14),4))),1); tree rep.5730000 = ((((15,(11,6)),(8,10)),(3,((5,((13,7),(4,14))),(2,9)))),12,1); tree rep.5740000 = (12,(((8,10),((15,11),6)),((((2,9),3),5),(14,((7,13),4)))),1); tree rep.5750000 = (12,((((4,((7,13),((2,9),3))),14),5),(((15,11),6),(8,10))),1); tree rep.5760000 = (((((2,9),3),((7,13),((5,14),4))),(8,((11,(15,6)),10))),12,1); tree rep.5770000 = ((((2,9),3),(((5,(13,7)),(4,14)),((((6,15),11),10),8))),12,1); tree rep.5780000 = (((10,8),((15,6),11)),(12,((((4,14),(7,13)),(3,(9,2))),5)),1); tree rep.5790000 = (((((9,2),3),((14,(4,(7,13))),5)),((((6,15),11),10),8)),12,1); tree rep.5800000 = (((3,(2,9)),(12,(((7,13),(4,14)),(((6,(15,11)),8),10)))),5,1); tree rep.5810000 = (12,(((11,(15,6)),(((((7,13),14),4),((3,5),(2,9))),8)),10),1); tree rep.5820000 = ((((3,(9,2)),((10,8),(11,(15,6)))),(5,((4,(7,13)),14))),12,1); tree rep.5830000 = (12,(((3,(9,2)),((4,(14,(13,7))),5)),(((11,(15,6)),8),10)),1); tree rep.5840000 = (12,(((10,(6,(11,15))),8),((4,((13,7),14)),(5,((9,2),3)))),1); tree rep.5850000 = (12,(((11,(6,15)),(8,10)),((4,(7,13)),((14,5),(2,(9,3))))),1); tree rep.5860000 = (12,(((4,((14,5),(7,13))),((9,2),3)),((11,(15,6)),(10,8))),1); tree rep.5870000 = (12,((14,(9,2)),((5,3),(((10,8),((15,6),11)),((7,13),4)))),1); tree rep.5880000 = ((12,((3,(9,2)),((((7,13),14),4),5))),(((11,(6,15)),8),10),1); tree rep.5890000 = (((((11,15),6),8),10),(12,((((13,7),4),14),(5,(3,(9,2))))),1); tree rep.5900000 = (((3,(2,9)),(5,(4,((7,13),14)))),(12,((10,8),((15,6),11))),1); tree rep.5910000 = (12,(((5,((13,7),(14,4))),((2,9),3)),((8,10),((15,11),6))),1); tree rep.5920000 = ((((15,(11,6)),(8,10)),12),((((13,7),14),((3,(2,9)),5)),4),1); tree rep.5930000 = (((3,(2,9)),((4,((7,13),14)),(5,((11,(15,6)),(10,8))))),12,1); tree rep.5940000 = (12,(((13,7),4),((3,(2,9)),(((10,8),((11,15),6)),(5,14)))),1); tree rep.5950000 = (12,(((((6,15),11),8),10),(((4,14),(7,13)),((3,(9,2)),5))),1); tree rep.5960000 = (12,(((5,(14,(4,(13,7)))),((2,9),3)),((10,8),(11,(15,6)))),1); tree rep.5970000 = ((14,((5,((9,2),(((8,((15,6),11)),10),12))),3)),(13,(7,4)),1); tree rep.5980000 = (12,(((((9,2),3),5),(14,((7,13),4))),((((15,11),6),8),10)),1); tree rep.5990000 = ((((8,10),(6,(15,11))),(((14,(9,2)),(5,3)),(4,(13,7)))),12,1); tree rep.6000000 = ((((5,((9,2),3)),(14,(4,(13,7)))),12),((10,8),(11,(15,6))),1); tree rep.6010000 = ((((4,(13,7)),((9,2),3)),(14,5)),((((15,11),6),(8,10)),12),1); tree rep.6020000 = ((10,(8,(6,(15,11)))),((((3,(2,9)),((4,(7,13)),5)),14),12),1); tree rep.6030000 = (12,((((2,9),3),((14,5),(4,(7,13)))),((8,10),(6,(15,11)))),1); tree rep.6040000 = (12,((((15,(6,11)),10),8),((((13,7),4),(3,(2,9))),(5,14))),1); tree rep.6050000 = ((((6,(15,11)),(8,10)),(((13,7),(14,4)),((3,(2,9)),5))),12,1); tree rep.6060000 = ((((8,(11,(15,6))),10),((((2,9),3),(5,14)),(13,(4,7)))),12,1); tree rep.6070000 = (12,((((4,(14,5)),(13,7)),((9,2),3)),((8,10),(11,(6,15)))),1); tree rep.6080000 = (12,(((10,8),(11,(15,6))),(((9,2),3),((14,5),(4,(13,7))))),1); tree rep.6090000 = (12,(((((2,9),3),(14,4)),((13,7),5)),(((11,15),6),(10,8))),1); tree rep.6100000 = ((((((8,(10,(11,(15,6)))),3),(12,(9,2))),(4,(7,13))),14),5,1); tree rep.6110000 = (((8,10),(11,(15,6))),(((((9,2),3),((12,14),4)),(7,13)),5),1); tree rep.6120000 = (((5,((3,(9,2)),(14,(4,(13,7))))),(8,((6,(15,11)),10))),12,1); tree rep.6130000 = (((12,((14,((13,7),4)),5)),((9,2),3)),(((11,15),6),(10,8)),1); tree rep.6140000 = (((8,(10,((15,11),6))),(5,((3,(9,2)),(14,(4,(13,7)))))),12,1); tree rep.6150000 = ((((10,(11,(15,6))),8),((5,(((7,13),4),14)),((2,9),3))),12,1); tree rep.6160000 = (12,(((8,((15,6),11)),10),(((14,5),((2,9),3)),((13,7),4))),1); tree rep.6170000 = (12,(((((2,9),3),(14,(4,(13,7)))),5),((10,8),((11,15),6))),1); tree rep.6180000 = ((((3,(9,2)),((((13,7),4),14),5)),((11,(6,15)),(10,8))),12,1); tree rep.6190000 = ((((((10,8),(11,(6,15))),((2,9),3)),(7,(13,4))),(14,5)),12,1); tree rep.6200000 = (12,(((10,8),((15,6),11)),((14,(4,(13,7))),(((9,2),3),5))),1); tree rep.6210000 = (((5,(((2,9),3),((7,13),(14,4)))),(10,(8,(15,(6,11))))),12,1); tree rep.6220000 = (((((13,7),4),((5,14),((2,9),3))),(((11,15),6),(10,8))),12,1); tree rep.6230000 = (12,(((5,((3,(2,9)),14)),(8,((6,(15,11)),10))),(4,(13,7))),1); tree rep.6240000 = (12,(((((2,9),3),((14,(7,4)),13)),5),((8,10),((15,11),6))),1); tree rep.6250000 = (((((15,6),11),10),8),((12,(3,(2,9))),(5,((14,(13,7)),4))),1); tree rep.6260000 = (12,(((3,(9,2)),(((4,(7,13)),14),5)),((((15,6),11),8),10)),1); tree rep.6270000 = ((((((6,11),15),8),10),(((4,((9,2),3)),((13,7),14)),5)),12,1); tree rep.6280000 = (((((15,6),11),(8,10)),12),(((3,(9,2)),((4,(13,7)),14)),5),1); tree rep.6290000 = (((((14,4),((9,2),3)),((7,13),5)),12),(10,(8,((15,11),6))),1); tree rep.6300000 = (12,((5,(((4,13),7),14)),((3,(9,2)),((8,10),(11,(15,6))))),1); tree rep.6310000 = (12,((((11,(15,6)),(10,8)),(14,(((13,7),4),((9,2),3)))),5),1); tree rep.6320000 = (12,(((((2,9),3),5),((4,(7,13)),14)),((10,8),(15,(6,11)))),1); tree rep.6330000 = ((10,8),(((5,(3,(9,2))),(12,((4,(13,7)),14))),(6,(11,15))),1); tree rep.6340000 = ((((10,(11,(6,15))),8),((5,((13,7),(4,14))),((2,9),3))),12,1); tree rep.6350000 = (12,((((13,7),(14,((3,(2,9)),4))),5),((8,10),(6,(15,11)))),1); tree rep.6360000 = ((((8,(11,(6,15))),10),((14,((7,13),4)),(5,(3,(2,9))))),12,1); tree rep.6370000 = ((((((7,13),4),(3,(9,2))),(14,5)),(((6,(15,11)),8),10)),12,1); tree rep.6380000 = ((8,((6,(15,11)),10)),(((14,((13,7),4)),((3,(9,2)),5)),12),1); tree rep.6390000 = ((((6,(15,11)),(10,8)),(((3,(2,9)),((13,7),4)),(5,14))),12,1); tree rep.6400000 = (((((13,7),4),14),(5,(3,(2,9)))),((((11,(15,6)),10),8),12),1); tree rep.6410000 = (12,((10,(8,((11,6),15))),(((14,(4,(7,13))),5),((2,9),3))),1); tree rep.6420000 = (12,(((5,(4,((7,13),14))),((9,2),3)),((((15,11),6),8),10)),1); tree rep.6430000 = ((((10,((11,15),6)),8),(3,((9,2),((4,(13,7)),(14,5))))),12,1); tree rep.6440000 = (((8,(10,((15,6),11))),(5,(3,(14,(((13,7),4),(9,2)))))),12,1); tree rep.6450000 = (12,(((14,4),(((7,13),5),((9,2),3))),(((6,15),11),(8,10))),1); tree rep.6460000 = ((((((13,7),4),((3,(2,9)),5)),14),(10,(8,((6,15),11)))),12,1); tree rep.6470000 = (12,(((6,(15,11)),(10,8)),(((13,7),(4,(14,5))),((2,9),3))),1); tree rep.6480000 = ((((3,(2,9)),((14,5),(4,(7,13)))),((6,(11,15)),(10,8))),12,1); tree rep.6490000 = ((((3,(2,9)),(((4,14),(13,7)),5)),((10,8),(11,(15,6)))),12,1); tree rep.6500000 = (((8,10),((15,6),11)),(5,((14,((13,7),4)),(12,(3,(9,2))))),1); tree rep.6510000 = (((8,((6,(11,15)),10)),(((9,2),3),((14,5),((7,13),4)))),12,1); tree rep.6520000 = ((((((9,2),3),5),((14,4),(13,7))),((8,((11,15),6)),10)),12,1); tree rep.6530000 = (12,((((3,(9,2)),(14,(4,(13,7)))),5),(8,(10,((6,15),11)))),1); tree rep.6540000 = (12,((5,(((13,7),(14,4)),(3,(9,2)))),(((11,(15,6)),8),10)),1); tree rep.6550000 = ((8,(10,((15,6),11))),(12,(5,((((7,13),14),4),((9,2),3)))),1); tree rep.6560000 = (12,(((6,(11,15)),(8,10)),(((13,7),(14,4)),(5,((9,2),3)))),1); tree rep.6570000 = (((((6,15),11),(10,8)),12),((14,5),((4,(7,13)),(3,(9,2)))),1); tree rep.6580000 = ((((8,10),((15,11),6)),12),((14,4),((7,13),(5,(3,(9,2))))),1); tree rep.6590000 = (12,(((5,(3,(9,2))),(14,((7,13),4))),((8,10),((11,15),6))),1); tree rep.6600000 = (12,((((6,(11,15)),10),8),(((13,7),(4,(5,14))),((9,2),3))),1); tree rep.6610000 = (12,((((11,15),6),(8,10)),(5,((14,(4,(13,7))),((9,2),3)))),1); tree rep.6620000 = (((((((2,9),3),14),(4,(13,7))),5),((8,10),(6,(15,11)))),12,1); tree rep.6630000 = ((12,((10,8),(11,(6,15)))),(((14,(9,2)),((4,(13,7)),5)),3),1); tree rep.6640000 = ((12,((8,(6,(11,15))),10)),(5,((14,(4,(13,7))),((9,2),3))),1); tree rep.6650000 = ((((8,10),(6,(11,15))),(((14,5),((7,13),4)),(3,(2,9)))),12,1); tree rep.6660000 = ((((6,(15,11)),(10,8)),(((5,(4,(7,13))),14),(3,(2,9)))),12,1); tree rep.6670000 = ((((12,((8,((15,11),6)),10)),5),((9,2),3)),(4,((7,13),14)),1); tree rep.6680000 = (12,((((15,6),11),(10,8)),(((4,(14,(7,13))),((9,2),3)),5)),1); tree rep.6690000 = ((((6,(11,15)),(8,10)),((3,(9,2)),((((13,7),14),4),5))),12,1); tree rep.6700000 = (12,(((11,(6,15)),(8,10)),((((2,9),3),5),(4,(14,(7,13))))),1); tree rep.6710000 = (12,((10,((15,11),6)),(8,((((13,7),4),(3,(9,2))),(14,5)))),1); tree rep.6720000 = ((14,((((9,2),3),5),((13,7),4))),((((15,6),11),(8,10)),12),1); tree rep.6730000 = ((((10,8),((11,15),6)),((((2,9),3),((14,4),5)),(7,13))),12,1); tree rep.6740000 = (((((13,7),(14,4)),(3,(9,2))),5),(12,((8,10),(11,(15,6)))),1); tree rep.6750000 = ((((14,((9,2),3)),((4,(7,13)),5)),(8,(((6,15),11),10))),12,1); tree rep.6760000 = (12,(((((9,2),(14,5)),((7,13),4)),3),(10,(((15,6),11),8))),1); tree rep.6770000 = (((((((11,15),6),(8,10)),5),(3,(9,2))),(14,((7,13),4))),12,1); tree rep.6780000 = (((10,8),(11,(6,15))),((5,(((2,9),3),(4,(14,(13,7))))),12),1); tree rep.6790000 = (((((3,(2,9)),(((13,7),14),4)),5),((10,(11,(6,15))),8)),12,1); tree rep.6800000 = ((((8,10),(11,(15,6))),((((2,9),3),((13,7),5)),(4,14))),12,1); tree rep.6810000 = (12,(((8,(11,(15,6))),10),((((14,(9,2)),((7,13),4)),5),3)),1); tree rep.6820000 = ((8,(((15,6),11),10)),(12,((13,7),(((5,(3,(9,2))),14),4))),1); tree rep.6830000 = (12,(((((11,15),6),8),10),((14,5),(((7,13),((2,9),4)),3))),1); tree rep.6840000 = ((((8,(11,(15,6))),10),((5,(((2,9),3),(4,14))),(7,13))),12,1); tree rep.6850000 = (((((10,8),(6,(11,15))),4),(((7,13),(((2,9),3),12)),14)),5,1); tree rep.6860000 = (12,(((((4,((7,13),14)),3),(9,2)),5),(((15,(11,6)),8),10)),1); tree rep.6870000 = (12,(((6,(15,11)),((9,2),((14,(((13,7),4),3)),5))),(10,8)),1); tree rep.6880000 = (12,((((6,15),11),(8,10)),((3,(9,2)),(5,(14,(4,(13,7)))))),1); tree rep.6890000 = (((((6,(15,11)),8),10),((((7,13),14),5),(4,((2,3),9)))),12,1); tree rep.6900000 = ((((8,10),(15,(11,6))),(3,((4,((9,2),((7,13),14))),5))),12,1); tree rep.6910000 = (12,(((8,(11,(6,15))),10),((3,(9,2)),(((13,7),(4,14)),5))),1); tree rep.6920000 = ((((((6,15),11),10),8),(((2,9),(((5,(13,7)),4),3)),14)),12,1); tree rep.6930000 = ((5,(((8,10),(11,(6,15))),((((13,7),4),14),(3,(2,9))))),12,1); tree rep.6940000 = (12,((5,((14,(4,(7,13))),(3,(2,9)))),((6,(11,15)),(10,8))),1); tree rep.6950000 = (12,((((2,9),3),(14,((4,(7,13)),5))),(8,(((15,6),11),10))),1); tree rep.6960000 = ((((8,10),(6,(15,11))),(((5,14),(3,(9,2))),((7,13),4))),12,1); tree rep.6970000 = (((((11,(6,15)),8),10),(((9,2),3),((4,(13,7)),(5,14)))),12,1); tree rep.6980000 = (12,(((14,((2,9),(3,5))),((7,13),4)),((8,10),(15,(11,6)))),1); tree rep.6990000 = (12,((8,((6,(11,15)),10)),((5,14),(((4,13),7),((9,2),3)))),1); tree rep.7000000 = ((((8,10),((3,(2,9)),(5,((7,13),(4,14))))),(6,(11,15))),12,1); tree rep.7010000 = ((8,(((3,((((4,7),13),14),(5,(2,9)))),(6,(11,15))),10)),12,1); tree rep.7020000 = ((((14,5),((13,7),4)),((8,(10,(11,(15,6)))),((9,2),3))),12,1); tree rep.7030000 = (12,(((4,((3,(2,9)),(5,14))),(7,13)),(10,(((11,15),6),8))),1); tree rep.7040000 = (((((13,7),(((11,(6,15)),(8,10)),(5,((2,9),3)))),4),14),12,1); tree rep.7050000 = ((((8,10),((15,11),6)),(((3,5),(13,7)),((14,4),(2,9)))),12,1); tree rep.7060000 = (12,((((4,(7,13)),(5,14)),((9,2),3)),((11,(15,6)),(8,10))),1); tree rep.7070000 = (12,(((10,((15,6),11)),8),((((7,13),4),14),(5,(3,(2,9))))),1); tree rep.7080000 = (12,(((8,10),((11,15),6)),(5,(((2,9),3),((4,(13,7)),14)))),1); tree rep.7090000 = ((((6,(15,11)),(10,8)),(5,(((9,2),3),((13,7),(14,4))))),12,1); tree rep.7100000 = (((5,(((9,2),3),(14,((13,7),4)))),(8,(((15,6),11),10))),12,1); tree rep.7110000 = (12,((((6,15),11),(8,10)),((4,((14,5),((2,9),3))),(7,13))),1); tree rep.7120000 = ((5,(((9,2),3),(4,(14,(7,13))))),((((15,11),6),(10,8)),12),1); tree rep.7130000 = ((((11,15),6),(8,10)),((4,(14,(7,13))),((3,(2,9)),(12,5))),1); tree rep.7140000 = (((((4,((7,13),9)),(14,5)),(3,2)),(((15,11),6),(10,8))),12,1); tree rep.7150000 = (((((6,(11,15)),10),8),12),(4,(((9,2),3),((7,13),(14,5)))),1); tree rep.7160000 = (12,((((11,15),6),(8,10)),(((3,(9,2)),(((13,7),4),14)),5)),1); tree rep.7170000 = (((10,((((4,(13,7)),14),(((2,9),3),5)),(6,(15,11)))),8),12,1); tree rep.7180000 = ((14,((13,7),(((10,8),((15,11),6)),((3,(9,2)),(5,4))))),12,1); tree rep.7190000 = (((6,(15,11)),12),(((3,(2,9)),((4,14),5)),((7,13),(10,8))),1); tree rep.7200000 = (((((13,7),5),14),(((10,8),((11,15),6)),(4,((9,2),3)))),12,1); tree rep.7210000 = ((12,((((4,14),(7,13)),5),(3,(2,9)))),((10,8),(11,(6,15))),1); tree rep.7220000 = ((((10,((15,11),6)),8),(((5,((13,7),4)),14),((9,2),3))),12,1); tree rep.7230000 = ((8,(((15,11),6),10)),(12,((((9,2),3),(14,((7,13),4))),5)),1); tree rep.7240000 = ((12,((5,4),((14,(7,13)),((9,2),3)))),(10,((11,(15,6)),8)),1); tree rep.7250000 = ((((3,(9,2)),(((13,7),4),(5,14))),(8,(10,(11,(15,6))))),12,1); tree rep.7260000 = (((((9,2),3),14),(5,((4,7),13))),(12,((8,10),(6,(11,15)))),1); tree rep.7270000 = (((((6,15),11),(10,8)),(14,((13,7),((4,(3,(2,9))),5)))),12,1); tree rep.7280000 = (((10,8),(11,(15,6))),(12,(((14,(7,13)),(4,((9,2),3))),5)),1); tree rep.7290000 = ((((((11,15),6),10),8),((((13,7),(3,(9,2))),4),(14,5))),12,1); tree rep.7300000 = (12,((14,((3,(2,9)),(5,(4,(13,7))))),((((6,15),11),10),8)),1); tree rep.7310000 = (12,(((14,5),(((3,(2,9)),(7,4)),13)),((((6,15),11),10),8)),1); tree rep.7320000 = ((((3,(2,9)),(((7,13),4),(5,14))),((15,(6,11)),(8,10))),12,1); tree rep.7330000 = (((5,((2,9),3)),(((13,7),14),4)),((((11,(15,6)),10),8),12),1); tree rep.7340000 = (12,(4,(((14,(13,7)),(5,((15,(6,11)),(8,10)))),((3,9),2))),1); tree rep.7350000 = (12,(((9,2),3),((5,(((15,6),11),(10,8))),((4,(7,13)),14))),1); tree rep.7360000 = (12,((3,(9,2)),(5,(((4,(13,7)),14),((10,8),(11,(6,15)))))),1); tree rep.7370000 = (12,((((7,13),((9,2),3)),(5,(14,4))),((((6,15),11),8),10)),1); tree rep.7380000 = (12,((((4,((7,13),14)),5),((2,9),3)),(((15,11),6),(8,10))),1); tree rep.7390000 = ((((10,8),((15,6),11)),(14,((4,(13,7)),(5,(3,(9,2)))))),12,1); tree rep.7400000 = (12,(((14,(4,(7,13))),(5,(3,(9,2)))),((8,10),((6,15),11))),1); tree rep.7410000 = (12,((3,(((4,(5,14)),(13,7)),(9,2))),(8,((11,(15,6)),10))),1); tree rep.7420000 = (((((11,6),15),(8,10)),(((2,9),3),((13,7),(5,(14,4))))),12,1); tree rep.7430000 = ((5,(((7,13),(14,4)),((9,2),3))),(12,(8,(10,(11,(15,6))))),1); tree rep.7440000 = ((((5,14),(3,(2,9))),(((((11,15),6),10),8),12)),((4,7),13),1); tree rep.7450000 = (12,(((5,(((7,13),4),14)),((9,2),3)),(((6,15),11),(10,8))),1); tree rep.7460000 = ((((5,((3,(9,2)),(4,(13,7)))),14),(8,(10,(6,(15,11))))),12,1); tree rep.7470000 = ((10,8),(((((4,(13,7)),((12,3),(2,9))),(6,(15,11))),14),5),1); tree rep.7480000 = ((((3,(2,9)),(((4,14),(13,7)),5)),(10,(((15,11),6),8))),12,1); tree rep.7490000 = (12,(5,((((7,13),14),4),(((6,(15,11)),(10,8)),(3,(9,2))))),1); tree rep.7500000 = (((5,(((2,9),3),((14,4),(13,7)))),(((6,(11,15)),10),8)),12,1); tree rep.7510000 = (((((6,(11,15)),8),10),12),(((2,9),3),(((4,(7,13)),5),14)),1); tree rep.7520000 = (12,(((3,(9,2)),(5,(14,((7,13),4)))),((11,(6,15)),(8,10))),1); tree rep.7530000 = (((((6,15),11),(10,8)),((3,(9,2)),(((14,4),(13,7)),5))),12,1); tree rep.7540000 = ((((4,5),(14,(7,13))),((12,3),(2,9))),((8,(11,(15,6))),10),1); tree rep.7550000 = (12,((8,(10,(15,(11,6)))),(5,((((9,2),3),14),(4,(13,7))))),1); tree rep.7560000 = ((((((6,15),11),8),10),(5,(((4,14),(7,13)),(3,(2,9))))),12,1); tree rep.7570000 = (12,(((5,(3,(9,2))),(4,(14,(7,13)))),((11,(6,15)),(10,8))),1); tree rep.7580000 = ((((((11,15),6),8),10),(((7,13),((5,(3,(9,2))),14)),4)),12,1); tree rep.7590000 = ((((6,11),15),(10,8)),(((3,(((14,4),(13,7)),12)),(2,9)),5),1); tree rep.7600000 = (12,((8,(((5,((4,(13,14)),7)),(3,(2,9))),(11,(6,15)))),10),1); tree rep.7610000 = ((5,((3,(9,2)),((4,(14,(7,13))),(((11,(15,6)),10),8)))),12,1); tree rep.7620000 = (12,((10,(((15,6),11),8)),((5,((14,(13,7)),4)),(3,(9,2)))),1); tree rep.7630000 = ((((6,(15,11)),8),10),(12,(((2,9),3),((4,(13,7)),(5,14)))),1); tree rep.7640000 = ((14,((5,(4,(7,13))),(3,(2,9)))),(((8,10),((11,15),6)),12),1); tree rep.7650000 = ((((2,9),((5,3),(14,((7,13),4)))),((8,10),(6,(11,15)))),12,1); tree rep.7660000 = (((((5,3),(4,((7,13),14))),(2,9)),((8,10),(6,(11,15)))),12,1); tree rep.7670000 = (12,((((7,4),13),((((2,9),3),14),5)),((10,(11,(15,6))),8)),1); tree rep.7680000 = (12,((10,((6,(11,15)),8)),(14,((((7,13),4),((2,9),3)),5))),1); tree rep.7690000 = (((((6,(11,15)),10),8),((4,((((9,2),3),5),14)),(7,13))),12,1); tree rep.7700000 = (((((2,9),3),(((10,8),(15,(6,11))),(7,13))),(4,(14,5))),12,1); tree rep.7710000 = (((((15,6),11),(10,8)),((3,(2,9)),(5,(14,(4,(7,13)))))),12,1); tree rep.7720000 = (12,(((7,13),(4,(14,(((9,2),3),5)))),((8,(11,(15,6))),10)),1); tree rep.7730000 = ((((5,(6,(11,15))),(((14,4),(13,7)),((9,2),3))),(8,10)),12,1); tree rep.7740000 = (12,((8,(10,(6,(11,15)))),((4,((5,14),(3,(2,9)))),(13,7))),1); tree rep.7750000 = ((((10,8),(6,(15,11))),((((3,(2,9)),5),14),(4,(13,7)))),12,1); tree rep.7760000 = (12,((((14,(7,13)),((3,(9,2)),5)),4),(((6,(11,15)),8),10)),1); tree rep.7770000 = (5,(4,(((12,((11,(15,6)),(8,10))),(13,7)),(14,((9,2),3)))),1); tree rep.7780000 = (((((9,2),(((11,(15,6)),(10,8)),3)),(14,5)),((7,13),4)),12,1); tree rep.7790000 = (12,(((4,(13,7)),(5,(14,(3,(9,2))))),((((15,6),11),10),8)),1); tree rep.7800000 = (((8,10),(((5,14),(((9,2),3),(13,(4,7)))),(6,(15,11)))),12,1); tree rep.7810000 = (((((6,15),11),8),10),(12,(((9,2),3),(((14,(7,13)),4),5))),1); tree rep.7820000 = ((((9,2),3),(((7,13),(4,14)),((((6,(11,15)),8),10),5))),12,1); tree rep.7830000 = ((8,(((((5,((2,9),3)),14),((4,7),13)),((11,15),6)),10)),12,1); tree rep.7840000 = (12,((((4,(13,7)),14),(((2,9),3),5)),((8,((15,11),6)),10)),1); tree rep.7850000 = (12,((5,(((7,13),4),(14,(3,(2,9))))),(((11,(15,6)),10),8)),1); tree rep.7860000 = ((12,(3,((2,9),(14,((13,7),(5,4)))))),((10,(11,(6,15))),8),1); tree rep.7870000 = (((10,(((6,15),11),8)),(((9,2),3),(5,((14,4),(7,13))))),12,1); tree rep.7880000 = (12,(((9,2),3),((((((15,11),6),8),10),((5,14),(7,13))),4)),1); tree rep.7890000 = (((5,((((13,7),14),4),((9,2),3))),12),(10,(8,(6,(15,11)))),1); tree rep.7900000 = ((((12,(14,5)),((9,2),3)),((13,7),4)),((8,10),(11,(15,6))),1); tree rep.7910000 = (((3,((9,2),((14,5),(4,(7,13))))),(((6,(11,15)),10),8)),12,1); tree rep.7920000 = ((12,((10,8),(11,(15,6)))),((5,(14,((13,7),(3,(9,2))))),4),1); tree rep.7930000 = ((((6,(11,15)),(10,8)),(((9,2),3),(5,((7,4),(14,13))))),12,1); tree rep.7940000 = (12,(((15,(11,6)),(8,10)),((14,((3,((13,7),4)),5)),(2,9))),1); tree rep.7950000 = (12,(((4,(13,7)),5),((((8,10),((15,6),11)),((2,9),3)),14)),1); tree rep.7960000 = ((((((14,4),(7,13)),(3,(2,9))),5),((10,8),((15,11),6))),12,1); tree rep.7970000 = (((3,(2,9)),((5,(10,(((6,15),11),8))),(14,(4,(7,13))))),12,1); tree rep.7980000 = (12,((((6,(15,11)),8),10),((((3,2),9),5),((13,(7,4)),14))),1); tree rep.7990000 = ((((((7,13),14),4),5),(3,(2,9))),(12,((10,8),((11,15),6))),1); tree rep.8000000 = (((((11,15),6),(8,10)),((3,(2,9)),(5,((7,13),(14,4))))),12,1); tree rep.8010000 = (12,(((4,(9,(3,2))),((((10,8),((11,15),6)),14),5)),(13,7)),1); tree rep.8020000 = (((3,(((2,9),5),14)),((13,7),4)),(12,(((15,11),6),(8,10))),1); tree rep.8030000 = (12,(((3,(2,9)),((14,((7,13),4)),5)),((10,8),((6,15),11))),1); tree rep.8040000 = ((((10,8),((11,15),6)),(((2,9),3),(14,(4,(5,(7,13)))))),12,1); tree rep.8050000 = ((((10,8),((6,11),15)),(((5,14),((7,13),4)),(3,(2,9)))),12,1); tree rep.8060000 = (12,(((8,10),((15,6),11)),(5,(((7,13),(4,14)),((9,2),3)))),1); tree rep.8070000 = ((((4,(7,13)),((14,5),(3,(2,9)))),(10,(8,((11,15),6)))),12,1); tree rep.8080000 = (((8,(10,((6,15),11))),(((3,(9,2)),5),(14,((7,13),4)))),12,1); tree rep.8090000 = ((((((7,13),4),(5,14)),(3,(9,2))),(10,(8,(11,(15,6))))),12,1); tree rep.8100000 = (((14,((4,(13,7)),(5,((2,9),3)))),((((6,11),15),8),10)),12,1); tree rep.8110000 = (((((6,15),11),(10,8)),(((14,(4,(13,7))),((2,9),3)),5)),12,1); tree rep.8120000 = (((5,(((9,2),3),(14,((7,13),4)))),(8,((15,(6,11)),10))),12,1); tree rep.8130000 = ((((8,10),((6,15),11)),(((13,7),4),((5,(3,(9,2))),14))),12,1); tree rep.8140000 = (12,(((8,((6,15),11)),10),((4,(13,(14,7))),((3,(9,2)),5))),1); tree rep.8150000 = (((5,((((7,13),4),(3,(2,9))),14)),((((11,15),6),10),8)),12,1); tree rep.8160000 = (12,((((((7,13),5),4),((9,2),3)),14),(8,(10,(6,(15,11))))),1); tree rep.8170000 = (((((9,2),3),(((13,7),4),14)),((((11,6),15),(8,10)),5)),12,1); tree rep.8180000 = (12,(((((6,15),11),8),10),(((3,(5,(2,9))),(4,14)),(13,7))),1); tree rep.8190000 = (12,(((8,(6,(15,11))),10),((5,(3,(2,9))),((4,(13,7)),14))),1); tree rep.8200000 = (12,((5,((3,(9,2)),((4,(13,7)),14))),(((15,11),6),(10,8))),1); tree rep.8210000 = (((2,9),(5,(((((6,15),11),(8,10)),(((13,4),7),14)),3))),12,1); tree rep.8220000 = ((((3,(2,9)),((((7,13),14),4),5)),12),((8,10),(6,(11,15))),1); tree rep.8230000 = (((((3,(2,9)),(14,((7,13),4))),5),(8,((11,(15,6)),10))),12,1); tree rep.8240000 = (((((11,(6,15)),10),8),(((3,(2,9)),12),5)),(((7,13),4),14),1); tree rep.8250000 = ((((((13,7),14),4),(5,(3,(2,9)))),((((6,15),11),8),10)),12,1); tree rep.8260000 = (12,(((11,(6,15)),(10,8)),((((13,7),(4,14)),5),((2,9),3))),1); tree rep.8270000 = (((((4,14),(13,7)),(((2,9),3),5)),((8,(6,(11,15))),10)),12,1); tree rep.8280000 = (12,(((((6,(11,15)),10),8),(((13,7),(14,4)),(3,(2,9)))),5),1); tree rep.8290000 = (((10,(8,((11,15),6))),((2,(9,3)),(5,(((7,13),4),14)))),12,1); tree rep.8300000 = ((((8,10),((11,15),6)),(5,((((13,7),4),(3,(9,2))),14))),12,1); tree rep.8310000 = (12,(((5,14),(((13,7),4),(3,(2,9)))),((10,8),(6,(11,15)))),1); tree rep.8320000 = (12,((10,(8,(11,(15,6)))),(((3,(2,9)),((13,7),4)),(14,5))),1); tree rep.8330000 = ((14,((8,(10,(6,(11,15)))),(((13,7),4),((3,(9,2)),5)))),12,1); tree rep.8340000 = ((((((2,9),3),(((7,13),4),14)),5),(8,(10,(11,(15,6))))),12,1); tree rep.8350000 = (12,((10,((6,(15,11)),8)),(14,((4,(((2,9),3),5)),(7,13)))),1); tree rep.8360000 = (12,((8,(10,((11,15),6))),(((3,(9,2)),(14,(13,(4,7)))),5)),1); tree rep.8370000 = (12,((10,(((6,15),11),8)),((4,(14,(13,7))),(5,((9,2),3)))),1); tree rep.8380000 = ((((9,2),3),((4,(13,7)),(5,14))),(12,((((6,11),15),10),8)),1); tree rep.8390000 = (12,(((((14,5),4),(7,13)),((9,2),3)),(((11,15),6),(8,10))),1); tree rep.8400000 = (12,((8,((11,(6,15)),10)),((((14,(7,13)),4),5),((2,9),3))),1); tree rep.8410000 = (((((11,15),6),(8,10)),(((((7,13),14),4),5),(3,(9,2)))),12,1); tree rep.8420000 = (12,(((10,8),(11,(15,6))),(((13,(7,4)),14),((3,(9,2)),5))),1); tree rep.8430000 = (12,((((7,13),4),((5,((2,9),3)),14)),(((11,(6,15)),8),10)),1); tree rep.8440000 = (5,((14,((7,13),4)),((12,(3,(9,2))),(((11,15),6),(10,8)))),1); tree rep.8450000 = ((4,(((7,13),((12,14),5)),((2,9),3))),((((15,11),6),10),8),1); tree rep.8460000 = (((3,(2,9)),((4,(13,7)),((5,14),((8,10),((15,11),6))))),12,1); tree rep.8470000 = (12,(((5,((9,2),3)),(14,(7,(13,4)))),((6,(11,15)),(8,10))),1); tree rep.8480000 = (12,(((((15,6),11),10),8),((3,((14,(13,7)),4)),(5,(2,9)))),1); tree rep.8490000 = ((((5,4),(((3,(2,9)),(((6,15),11),(8,10))),(13,7))),14),12,1); tree rep.8500000 = (12,(((10,8),(6,(15,11))),((5,(3,((4,14),(7,13)))),(9,2))),1); tree rep.8510000 = (((14,((((6,(11,15)),(10,8)),((2,9),3)),5)),(4,(7,13))),12,1); tree rep.8520000 = ((((4,((13,7),14)),((3,(9,2)),5)),(((6,(15,11)),8),10)),12,1); tree rep.8530000 = (12,(((5,(((7,13),4),14)),((2,3),9)),((8,10),((11,15),6))),1); tree rep.8540000 = (12,(((8,10),(11,(15,6))),((5,(((13,7),4),14)),(3,(2,9)))),1); tree rep.8550000 = ((((6,(15,11)),(10,8)),(((13,7),4),(12,((9,2),3)))),(14,5),1); tree rep.8560000 = ((((10,8),((15,11),6)),((((9,2),3),5),((13,7),(4,14)))),12,1); tree rep.8570000 = (12,(((10,((11,15),6)),8),(5,((3,(9,2)),(14,(4,(7,13)))))),1); tree rep.8580000 = (((((15,6),11),10),8),(12,(((4,(13,7)),14),(5,((9,2),3)))),1); tree rep.8590000 = ((((3,((13,7),(2,9))),(14,(5,4))),((((6,15),11),10),8)),12,1); tree rep.8600000 = (((5,((2,9),3)),(4,((8,10),((6,15),11)))),(((7,13),14),12),1); tree rep.8610000 = (((((7,13),4),(5,(((2,9),3),14))),((10,(6,(11,15))),8)),12,1); tree rep.8620000 = (((10,(11,(15,6))),8),((5,((14,(4,(13,7))),(3,(2,9)))),12),1); tree rep.8630000 = ((((((15,11),6),8),10),(((5,(3,(2,9))),14),((4,7),13))),12,1); tree rep.8640000 = ((((5,((8,10),(6,(15,11)))),(((7,13),4),14)),((2,9),3)),12,1); tree rep.8650000 = (12,(((((9,2),3),(4,(5,(13,7)))),14),((10,8),((11,15),6))),1); tree rep.8660000 = (((12,5),(9,2)),(((3,((8,((11,15),6)),10)),(14,4)),(13,7)),1); tree rep.8670000 = (12,(((8,10),((15,6),11)),(((14,4),(7,13)),(5,(3,(9,2))))),1); tree rep.8680000 = ((((14,((13,7),4)),(5,(3,(2,9)))),((6,(11,15)),(10,8))),12,1); tree rep.8690000 = (((((3,(9,2)),(4,((13,7),14))),5),((10,8),((15,6),11))),12,1); tree rep.8700000 = (12,((10,((11,(6,15)),8)),(5,(((7,13),(14,4)),(3,(9,2))))),1); tree rep.8710000 = (12,((10,8),((((9,2),3),((14,((13,7),4)),5)),(11,(6,15)))),1); tree rep.8720000 = (12,(((3,(9,2)),(14,5)),(((7,13),4),((10,(11,(6,15))),8))),1); tree rep.8730000 = (12,(5,(((4,14),((7,13),((9,2),3))),(((6,15),11),(8,10)))),1); tree rep.8740000 = (((((((13,7),4),14),(3,(9,2))),5),((6,(11,15)),(8,10))),12,1); tree rep.8750000 = (((10,(8,(11,(15,6)))),((((13,7),4),((2,9),3)),(5,14))),12,1); tree rep.8760000 = ((((((2,9),3),((7,13),(4,14))),5),((10,((6,15),11)),8)),12,1); tree rep.8770000 = (((((11,(6,15)),(10,8)),((((7,13),4),((2,9),3)),14)),5),12,1); tree rep.8780000 = (12,(((5,((14,((7,13),4)),((10,8),((15,11),6)))),3),(2,9)),1); tree rep.8790000 = (12,(((10,8),((15,6),11)),(((5,14),(3,(9,2))),((7,13),4))),1); tree rep.8800000 = (12,((((5,14),(4,(13,7))),(3,(9,2))),((8,10),(11,(6,15)))),1); tree rep.8810000 = (((((15,6),11),(8,10)),((((4,(13,7)),14),5),((2,9),3))),12,1); tree rep.8820000 = (12,((3,(2,9)),(((14,5),((6,(15,11)),(10,8))),((7,13),4))),1); tree rep.8830000 = (12,(((3,(2,9)),(((14,5),4),(13,7))),((10,((6,15),11)),8)),1); tree rep.8840000 = ((12,((((3,(2,9)),4),5),(14,(7,13)))),((((11,15),6),8),10),1); tree rep.8850000 = (12,((3,(9,2)),(((((15,11),6),8),10),(5,(14,(13,(4,7)))))),1); tree rep.8860000 = (12,((3,(5,((9,2),((13,7),(14,4))))),(8,(((6,15),11),10))),1); tree rep.8870000 = (12,((((2,9),3),((14,5),((10,(6,(11,15))),8))),(4,(7,13))),1); tree rep.8880000 = (12,((((9,2),3),((14,4),(13,7))),(5,((8,10),((6,15),11)))),1); tree rep.8890000 = (12,(((((13,7),4),5),14),(((9,2),3),(((15,6),11),(8,10)))),1); tree rep.8900000 = ((((6,(11,15)),(8,10)),(((3,(2,9)),(13,(7,(4,14)))),5)),12,1); tree rep.8910000 = ((5,(((8,10),(6,(11,15))),(((7,13),(14,4)),((2,9),3)))),12,1); tree rep.8920000 = (12,(((((7,13),4),(14,5)),((9,2),3)),(((11,(6,15)),10),8)),1); tree rep.8930000 = (12,(((8,((15,6),11)),10),(((14,(4,5)),(13,7)),(3,(2,9)))),1); tree rep.8940000 = (12,(((3,(2,9)),((13,7),(14,(4,5)))),(((6,(15,11)),10),8)),1); tree rep.8950000 = (12,(((3,(2,9)),((5,4),(14,(13,7)))),(((11,15),6),(10,8))),1); tree rep.8960000 = ((12,((10,(11,(15,6))),8)),((3,(2,9)),(5,(14,((7,13),4)))),1); tree rep.8970000 = (12,(((3,(2,9)),((14,((7,13),5)),4)),(10,(8,((15,6),11)))),1); tree rep.8980000 = (12,((((14,5),(13,(4,7))),((2,9),3)),((((15,6),11),8),10)),1); tree rep.8990000 = ((((11,(6,15)),(8,10)),12),((((((9,2),3),5),14),4),(13,7)),1); tree rep.9000000 = ((((3,(9,2)),((((13,7),4),14),5)),((10,8),((15,11),6))),12,1); tree rep.9010000 = ((((8,(11,(6,15))),10),(((3,(9,2)),14),(((13,7),4),5))),12,1); tree rep.9020000 = ((((3,(5,(9,2))),14),(4,(7,13))),(12,((8,10),(6,(11,15)))),1); tree rep.9030000 = ((((13,7),(4,14)),(5,(3,(9,2)))),(((10,((6,15),11)),8),12),1); tree rep.9040000 = ((((7,13),4),(((2,9),3),(14,5))),((10,(8,((11,15),6))),12),1); tree rep.9050000 = ((((((9,2),3),4),((10,(8,((6,15),11))),(14,(13,7)))),5),12,1); tree rep.9060000 = (12,((((6,15),11),(10,8)),(((9,2),3),(((14,(7,13)),4),5))),1); tree rep.9070000 = (((10,8),((((14,4),(7,13)),5),((11,(15,6)),((2,9),3)))),12,1); tree rep.9080000 = (12,((((13,7),(14,4)),((8,10),(11,(15,6)))),((2,9),(5,3))),1); tree rep.9090000 = (((((2,9),3),((((7,13),14),4),(((15,6),11),(8,10)))),5),12,1); tree rep.9100000 = ((((((2,9),3),(4,(7,13))),(5,14)),(((15,11),6),(10,8))),12,1); tree rep.9110000 = (12,(((3,(9,2)),(((13,7),4),(14,5))),(8,(10,((11,15),6)))),1); tree rep.9120000 = (12,(14,((2,9),(((7,13),(5,((((15,6),11),(8,10)),4))),3))),1); tree rep.9130000 = ((((14,(7,13)),(4,((3,(9,2)),5))),((((6,15),11),10),8)),12,1); tree rep.9140000 = (((((((6,15),11),(8,10)),(14,(4,(13,7)))),(9,(2,3))),5),12,1); tree rep.9150000 = (((8,((11,(15,6)),10)),(((3,(2,9)),((7,13),(14,4))),5)),12,1); tree rep.9160000 = ((12,((10,(11,(15,6))),8)),(((((7,13),4),14),(3,(2,9))),5),1); tree rep.9170000 = (((((6,(15,11)),10),8),((((4,(7,13)),14),((2,9),3)),5)),12,1); tree rep.9180000 = ((((9,2),3),((((11,15),6),(8,10)),((13,7),(4,(5,14))))),12,1); tree rep.9190000 = ((((11,6),15),(10,8)),(12,(5,(((2,9),3),(14,((7,13),4))))),1); tree rep.9200000 = (((((2,9),3),((6,(15,11)),(10,8))),((14,4),((7,13),5))),12,1); tree rep.9210000 = ((10,(8,((15,11),6))),((5,(((7,13),(14,4)),((2,9),3))),12),1); tree rep.9220000 = (((10,(8,((((13,7),(((9,2),3),4)),14),5))),((6,15),11)),12,1); tree rep.9230000 = (((((((7,13),(14,4)),(((2,9),3),5)),12),8),10),((15,6),11),1); tree rep.9240000 = (12,((10,(8,(15,(6,11)))),((5,((13,7),(14,4))),((9,2),3))),1); tree rep.9250000 = (((((4,(13,7)),5),(((9,2),3),14)),((6,(15,11)),(8,10))),12,1); tree rep.9260000 = (((((15,6),11),(8,10)),(((7,13),14),(4,(5,(3,(2,9)))))),12,1); tree rep.9270000 = ((14,(5,((4,(13,7)),((((11,(15,6)),8),10),((9,2),3))))),12,1); tree rep.9280000 = (((((3,(2,9)),((4,14),(7,13))),5),(8,(10,((11,15),6)))),12,1); tree rep.9290000 = (12,(((11,(6,15)),(8,10)),(((2,9),3),(4,((13,(5,14)),7)))),1); tree rep.9300000 = (((8,10),(11,(6,15))),(12,(((3,(2,9)),5),((14,(13,7)),4))),1); tree rep.9310000 = ((((15,6),11),((((13,7),4),14),((2,9),(3,(12,5))))),(10,8),1); tree rep.9320000 = (12,(((8,10),(6,(15,11))),((13,(((14,((9,2),3)),7),5)),4)),1); tree rep.9330000 = (12,(((5,14),((3,(2,9)),(((15,11),6),(10,8)))),(4,(7,13))),1); tree rep.9340000 = (12,(((((2,9),3),5),((14,(13,7)),4)),(((15,6),11),(10,8))),1); tree rep.9350000 = (((((((13,7),14),4),5),(3,(9,2))),((8,10),(11,(15,6)))),12,1); tree rep.9360000 = (12,((5,((3,(9,2)),((4,(7,13)),14))),((10,((11,15),6)),8)),1); tree rep.9370000 = ((((3,(9,2)),(14,(5,((13,7),4)))),(((11,15),6),(8,10))),12,1); tree rep.9380000 = ((((6,(15,11)),(10,8)),(((4,(7,13)),(5,14)),(3,(9,2)))),12,1); tree rep.9390000 = (12,(((((3,(2,9)),5),4),((13,7),14)),((8,10),(6,(11,15)))),1); tree rep.9400000 = ((((3,(9,2)),(((13,7),14),4)),5),(12,((10,((6,15),11)),8)),1); tree rep.9410000 = ((((3,(2,9)),(((7,13),4),14)),5),(12,(8,(10,((11,15),6)))),1); tree rep.9420000 = (12,((8,(((15,6),11),10)),((4,((7,13),14)),(5,((9,2),3)))),1); tree rep.9430000 = (((3,(9,2)),((((11,(6,15)),(10,8)),((4,14),(7,13))),5)),12,1); tree rep.9440000 = ((12,(8,10)),(((15,6),11),((((2,9),3),4),((14,5),(13,7)))),1); tree rep.9450000 = (12,((8,10),(((11,6),15),(5,((3,(2,9)),((4,(13,7)),14))))),1); tree rep.9460000 = (((14,(((2,9),3),4)),(((((11,15),6),8),(12,10)),(7,13))),5,1); tree rep.9470000 = (12,((((7,13),4),((10,8),(15,(6,11)))),(5,(((2,9),3),14))),1); tree rep.9480000 = ((3,(((((8,10),(11,(15,6))),5),(14,((7,13),4))),(2,9))),12,1); tree rep.9490000 = (12,(((3,(9,2)),((((7,13),4),14),5)),(((15,6),11),(10,8))),1); tree rep.9500000 = (12,((((3,(2,9)),(4,(14,(13,7)))),5),((10,((6,15),11)),8)),1); tree rep.9510000 = ((((5,14),(((2,9),3),((13,7),4))),(((6,15),11),(10,8))),12,1); tree rep.9520000 = (((((9,2),3),12),(5,((4,(7,13)),14))),(((6,15),11),(8,10)),1); tree rep.9530000 = (12,((5,(((14,4),(13,7)),((2,9),3))),((11,(15,6)),(10,8))),1); tree rep.9540000 = (12,((10,(8,((11,15),6))),(((((13,7),4),14),(3,(2,9))),5)),1); tree rep.9550000 = (((((2,9),3),((4,(13,7)),14)),(5,(((11,(6,15)),8),10))),12,1); tree rep.9560000 = ((12,(10,(8,(15,(6,11))))),((3,(2,9)),(((4,(7,13)),14),5)),1); tree rep.9570000 = (((10,((6,15),11)),((((5,14),(7,13)),(4,(3,(2,9)))),8)),12,1); tree rep.9580000 = (((14,((13,7),4)),((3,(2,9)),5)),(12,(((6,(15,11)),8),10)),1); tree rep.9590000 = (((((((2,9),3),5),(4,(7,13))),14),12),(((11,15),6),(10,8)),1); tree rep.9600000 = ((((8,10),(15,(11,6))),(5,((4,((7,13),14)),((9,2),3)))),12,1); tree rep.9610000 = (((((6,15),11),(8,10)),((3,(9,2)),(((13,7),4),(5,14)))),12,1); tree rep.9620000 = (((((14,(4,(7,13))),((9,2),3)),5),(((6,(11,15)),10),8)),12,1); tree rep.9630000 = (((5,((9,2),3)),(((6,(15,11)),(8,10)),(14,((7,13),4)))),12,1); tree rep.9640000 = (12,((5,(((7,(13,4)),(((11,(15,6)),8),10)),14)),((9,2),3)),1); tree rep.9650000 = ((((5,(3,(2,9))),(14,(4,(7,13)))),12),((8,((15,6),11)),10),1); tree rep.9660000 = (12,((8,((6,(15,11)),10)),((3,(2,9)),(((5,(13,7)),4),14))),1); tree rep.9670000 = (12,(((6,(15,11)),(8,10)),((4,(7,13)),((14,5),(3,(2,9))))),1); tree rep.9680000 = ((((6,(11,15)),(10,8)),((14,((13,7),4)),(((2,9),3),5))),12,1); tree rep.9690000 = (((((2,9),3),(((13,7),4),14)),(((6,(15,11)),(10,8)),5)),12,1); tree rep.9700000 = ((((6,(11,15)),8),10),((5,(3,(9,2))),((((13,7),4),12),14)),1); tree rep.9710000 = ((((13,7),((5,14),(4,((9,2),3)))),((8,10),(6,(15,11)))),12,1); tree rep.9720000 = (12,(((8,10),(11,(6,15))),(((5,(3,(9,2))),(4,(7,13))),14)),1); tree rep.9730000 = (((10,8),((15,6),11)),(((2,9),((5,(13,7)),3)),(12,(4,14))),1); tree rep.9740000 = (12,(((8,10),(6,(15,11))),(((3,(2,9)),5),(14,(4,(13,7))))),1); tree rep.9750000 = (((((9,2),3),(5,(4,((7,13),14)))),12),((8,10),((6,15),11)),1); tree rep.9760000 = (12,(((5,((9,2),3)),((4,14),(13,7))),((((11,15),6),10),8)),1); tree rep.9770000 = ((((10,((6,15),11)),8),((9,(2,3)),((4,((7,13),14)),5))),12,1); tree rep.9780000 = ((((14,(5,(3,(2,9)))),(7,(4,13))),12),(10,(((11,15),6),8)),1); tree rep.9790000 = (12,(((((9,2),3),(4,(13,7))),(14,5)),((6,(11,15)),(8,10))),1); tree rep.9800000 = (12,(((10,8),((6,15),11)),((5,((4,(13,7)),14)),((9,2),3))),1); tree rep.9810000 = ((((6,(15,11)),(8,10)),((5,14),((7,(13,4)),(3,(2,9))))),12,1); tree rep.9820000 = (((((3,(9,2)),((7,13),(4,14))),((6,(15,11)),(10,8))),5),12,1); tree rep.9830000 = ((12,((3,(9,2)),(((4,(7,13)),5),14))),((((15,11),6),10),8),1); tree rep.9840000 = (12,(((((11,15),6),10),8),(((14,((13,7),4)),5),((9,2),3))),1); tree rep.9850000 = ((12,(((4,((2,9),3)),(14,(7,13))),5)),(10,(8,((6,15),11))),1); tree rep.9860000 = ((((11,(6,15)),(10,8)),(14,(((7,4),13),(5,(3,(9,2)))))),12,1); tree rep.9870000 = (((8,((6,15),11)),10),(12,(3,((14,4),(((9,2),5),(13,7))))),1); tree rep.9880000 = (((14,(((4,(13,7)),5),((2,9),3))),((((6,15),11),8),10)),12,1); tree rep.9890000 = (12,(((5,((13,7),(4,14))),((2,9),3)),((((11,15),6),10),8)),1); tree rep.9900000 = (12,(((10,8),(6,(15,11))),(((14,((7,13),4)),5),(3,(9,2)))),1); tree rep.9910000 = (12,(((((14,4),(7,13)),((8,((11,15),6)),10)),(3,(9,2))),5),1); tree rep.9920000 = (12,((10,((6,(11,15)),8)),((5,14),((3,(9,2)),(4,(7,13))))),1); tree rep.9930000 = (((3,(9,2)),(((14,(7,13)),4),(5,(10,(8,(11,(6,15))))))),12,1); tree rep.9940000 = ((((5,((9,2),3)),((13,7),(14,4))),(8,(((11,15),6),10))),12,1); tree rep.9950000 = (12,(((10,((15,6),11)),8),(5,(((13,(14,7)),4),(3,(2,9))))),1); tree rep.9960000 = (12,((((4,((7,13),14)),((9,2),3)),((8,10),(11,(15,6)))),5),1); tree rep.9970000 = ((((3,(2,9)),(10,(8,((11,6),15)))),(4,((5,14),(7,13)))),12,1); tree rep.9980000 = (((8,(10,((11,6),15))),((((14,(7,13)),4),5),((2,9),3))),12,1); tree rep.9990000 = (12,((8,(10,((15,11),6))),((((13,7),(14,4)),((9,2),3)),5)),1); tree rep.10000000 = (((((6,(11,15)),10),8),(((4,(13,7)),(5,(3,(2,9)))),14)),12,1); end; phangorn/inst/extdata/trees/RAxML_bootstrap.woodmouse0000644000175100001440000042331012761653173022636 0ustar hornikusers(((No1007S,(No0909S,No1208S)),(((No0906S,(No0910S,No1202S)),((No0912S,No1103S),(No0908S,No1206S))),(No306,(No0913S,No304)))),No1114S,No305); (((No306,((No0906S,(No0913S,(No0910S,No1202S))),(((No1208S,(No1007S,No0909S)),(No0912S,No1103S)),No304))),(No0908S,No1206S)),No1114S,No305); (((No0909S,(No1208S,No1007S)),((No0912S,No1103S),((No0908S,No1206S),((No304,No306),(No0906S,(No0913S,(No0910S,No1202S))))))),No1114S,No305); (((No1007S,(No0909S,No1208S)),((No0906S,(No0908S,(((((No0913S,No304),No306),No1103S),No1206S),No0912S))),(No0910S,No1202S))),No1114S,No305); (((((No0908S,No1206S),(No304,No306)),((No0913S,(No0910S,No1202S)),No0906S)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); (No1114S,(((No0908S,((No0910S,No1202S),(No1206S,No0906S))),(No306,(No0913S,No304))),((No1208S,(No1007S,No0909S)),(No0912S,No1103S))),No305); ((No1114S,(No306,(No0913S,No304))),(((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No0910S,No1202S),((No0908S,No1206S),No0906S))),No305); ((((No0906S,(No0910S,No1202S)),No1206S),(No0908S,((No0909S,(No1208S,No1007S)),(No0912S,No1103S)))),(No306,(No0913S,(No1114S,No304))),No305); ((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No304,No0913S),No306),(((No0906S,(No0910S,No1202S)),No1206S),No0908S))),No1114S,No305); (((No0912S,No1103S),((No1007S,(No0909S,No1208S)),(((No304,No0913S),No306),((No0906S,(No0910S,No1202S)),(No1206S,No0908S))))),No1114S,No305); ((((No1103S,No0912S),(((((No1202S,No0910S),No0906S),No1206S),No0908S),(No306,(No304,No0913S)))),((No1208S,No0909S),No1007S)),No1114S,No305); (((((((No1202S,No0910S),No0906S),No1206S),No0908S),(No306,(No304,No0913S))),(No1103S,(((No1208S,No0909S),No1007S),No0912S))),No1114S,No305); (((((((No1202S,No0910S),No0906S),No1206S),No0908S),(No306,(No304,No0913S))),(No1103S,(((No1208S,No0909S),No1007S),No0912S))),No1114S,No305); ((((No0906S,(No1206S,No0908S)),(No306,(No304,((No1202S,No0910S),No0913S)))),((((No1208S,No0909S),No1007S),No0912S),No1103S)),No1114S,No305); ((((No0908S,(((No1202S,No0910S),No0906S),No1206S)),(No306,(No304,No0913S))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((No306,((No304,No0913S),(No0908S,(((No1202S,No0910S),No0906S),No1206S)))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); (((((No306,(No0913S,(((No1202S,No0910S),No0906S),No1206S))),No0908S),No304),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); ((((((No0913S,No304),No306),No0908S),(((No1202S,No0910S),No0906S),No1206S)),(((No1007S,(No1208S,No0909S)),No0912S),No1103S)),No1114S,No305); ((((((No0913S,No304),No306),No0908S),(((No1202S,No0910S),No0906S),No1206S)),(((No0909S,(No1007S,No1208S)),No0912S),No1103S)),No1114S,No305); ((((((No304,No0908S),(No306,(((No0913S,(No1202S,No0910S)),No0906S),No1206S))),(No0909S,(No1007S,No1208S))),No0912S),No1103S),No1114S,No305); (((No1103S,(No0912S,(No0909S,(No1007S,No1208S)))),(((((No1202S,No0910S),No0906S),No1206S),No0908S),(No306,(No304,No0913S)))),No1114S,No305); (((No1007S,(No1208S,No0909S)),(No1103S,No0912S)),((No0908S,(((No1202S,No0910S),No0906S),No1206S)),(No306,(No1114S,(No304,No0913S)))),No305); (((No1103S,No0912S),(((No0908S,(((No1202S,No0910S),No0906S),No1206S)),(No306,(No304,No0913S))),(No1007S,(No1208S,No0909S)))),No1114S,No305); ((No1103S,(((((No1202S,No0910S),No0906S),(No1206S,No0908S)),(No306,(No304,No0913S))),(No0912S,(No1007S,(No1208S,No0909S))))),No1114S,No305); ((((No306,(No304,No0913S)),(No0908S,(((No1202S,No0910S),No0906S),No1206S))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); ((((No306,(No304,No0913S)),(No0908S,(((No1202S,No0910S),No0906S),No1206S))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); ((((No306,(No304,No0913S)),(No0908S,(((No1202S,No0910S),No0906S),No1206S))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); (((No306,(No0908S,((((No1202S,No0910S),No0906S),No1206S),(No304,No0913S)))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No1114S,No305); ((No0908S,((No306,(No304,No0913S)),((((No1202S,No0910S),No0906S),No1206S),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(((No0908S,No1206S),((No1202S,No0910S),No0906S)),(No306,(No304,No0913S)))),No1114S,No305); (((No0913S,No304),(No306,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(No0908S,((No0906S,(No0910S,No1202S)),No1206S))))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0913S,No304),No306))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0913S,No304),No306))),No1114S,No305); ((((No0912S,No1103S),(No0909S,(No1007S,No1208S))),(((No1206S,(No0910S,(No1202S,No0906S))),No0908S),((No0913S,No304),No306))),No1114S,No305); (((No0909S,(No1007S,No1208S)),(No0912S,(No1103S,(No306,(((No0913S,No0910S),No1202S),(No304,(No0906S,(No0908S,No1206S)))))))),No1114S,No305); (((No306,((No304,No0908S),(No0913S,(No1206S,(No0906S,(No0910S,No1202S)))))),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No1114S,No305); (((No1103S,No0912S),(((No0908S,(No306,(No304,No0913S))),(No0906S,((No1206S,No1202S),No0910S))),(No0909S,(No1007S,No1208S)))),No1114S,No305); (((No1103S,No1114S),No0912S),((((No1206S,No0908S),(No306,(No304,No0913S))),(No0906S,(No1202S,No0910S))),(No0909S,(No1007S,No1208S))),No305); (No1114S,((No0912S,((No1007S,(No1208S,No0909S)),No1103S)),((((No306,(No304,No0913S)),((No1202S,No0910S),No0906S)),No1206S),No0908S)),No305); (No1114S,((No0912S,((No0909S,(No1007S,No1208S)),No1103S)),((No0908S,(No1206S,((No1202S,No0910S),No0906S))),(No306,(No304,No0913S)))),No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),((No306,((No0910S,((No0913S,No304),No1202S)),No0906S)),(No0908S,No1206S))),No1114S,No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),((((No0913S,No304),No306),(((No1202S,No0910S),No1206S),No0906S)),No0908S)),No1114S,No305); ((((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(((No1202S,No0910S),No0906S),(((No0913S,No304),No306),(No0908S,No1206S)))),No1114S,No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(((No1202S,No0910S),No0906S),(((No0913S,No304),No306),(No0908S,No1206S)))),No1114S,No305); ((((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No0908S,No1206S),(No304,(No306,(No0913S,((No1202S,No0910S),No0906S)))))),No1114S,No305); (((No1208S,(No0909S,No1007S)),((No0912S,No1103S),((No0908S,(No1206S,((No1202S,No0910S),No0906S))),((No0913S,No304),No306)))),No1114S,No305); ((((No0908S,(No1206S,((No1202S,No0910S),No0906S))),((No0913S,No304),No306)),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); ((((No0908S,(No1206S,((No1202S,No0910S),No0906S))),((No0913S,No304),No306)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((No1007S,(No1208S,No0909S)),((No0912S,No1103S),((No0908S,((No0906S,(No1202S,No0910S)),No1206S)),((No0913S,No304),No306)))),No1114S,No305); ((((No0908S,((No1202S,No0910S),(No1206S,No0906S))),((No0913S,No304),No306)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No1114S,No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),(((No306,(No304,No0913S)),(No1206S,No0908S)),(No0906S,(No0910S,No1202S)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No306,(No304,No0913S)),(No0908S,(No1206S,(No0906S,(No0910S,No1202S)))))),No305); (No1114S,((No304,No0913S),(((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(No0908S,No1206S)),(No0906S,(No0910S,No1202S))),No306)),No305); (((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),(No306,(No1114S,(No304,No0913S)))),((No0912S,No1103S),(No1007S,(No1208S,No0909S))),No305); (No1114S,((No306,((((No304,No0913S),(No0910S,No1202S)),No0906S),(No0908S,No1206S))),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),No305); (No1114S,((No306,(No304,No0913S)),((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(No0908S,(No1206S,((No1202S,No0910S),No0906S))))),No305); (No1114S,((No1103S,No0912S),((No306,(No304,No0913S)),((((No1202S,No0910S),No0906S),(No0909S,(No1007S,No1208S))),(No0908S,No1206S)))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),((No0908S,(No306,(No304,No0913S))),(No1206S,((No1202S,No0910S),No0906S)))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),((No0908S,(No306,(No304,No0913S))),(No1206S,((No1202S,No0910S),No0906S)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No306,(No304,No0913S)),((No1202S,No0910S),((No0908S,No1206S),No0906S)))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No1103S,No0912S)),(((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),((No0913S,No304),No306))),No305); (No1114S,((No0912S,No1103S),(No0906S,((((No1202S,No0910S),(No1007S,(No1208S,No0909S))),((No0913S,No304),No306)),(No0908S,No1206S)))),No305); (No1114S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(((No0913S,No304),No306),(No0908S,(No1206S,(No0906S,(No1202S,No0910S)))))),No305); (No1114S,((No0912S,No1103S),(((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),((No0913S,No304),No306)),(No0909S,(No1007S,No1208S)))),No305); (No1114S,(((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),((No0913S,No304),No306)),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); (No1114S,(((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),((No0913S,No304),No306)),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); ((((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),(No1114S,((No0913S,No304),No306))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),No305); (((((No0913S,No304),No306),(No0908S,(((No1202S,No0910S),No0906S),No1206S))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); (((((No0913S,No304),No306),(No0908S,(((No1202S,No0910S),No0906S),No1206S))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No1114S,No305); ((No0908S,((((No1202S,No0910S),No0906S),No1206S),((No0913S,(No1114S,No304)),No306))),(No1103S,((No0909S,(No1007S,No1208S)),No0912S)),No305); (No1114S,((No1007S,(No1208S,No0909S)),(((((No1103S,No0912S),No1206S),(No0906S,(No1202S,No0910S))),No0908S),((No0913S,No304),No306))),No305); ((((((No1206S,No0908S),No0912S),(No0906S,(No1202S,No0910S))),No1103S),((No0913S,No304),No306)),((No1114S,(No1007S,No1208S)),No0909S),No305); (No1114S,((((No1206S,(No0906S,(No1202S,No0910S))),No0908S),((No0913S,No304),No306)),(((No1007S,No1208S),No0909S),(No0912S,No1103S))),No305); (No1114S,((((No1206S,(No0906S,(No1202S,No0910S))),No0908S),((No0913S,No304),No306)),(((No1007S,No1208S),No0909S),(No0912S,No1103S))),No305); (No1114S,(((No1007S,No1208S),No0909S),((No306,(No1103S,(((No0906S,(No1202S,No0910S)),No1206S),(No0908S,No0912S)))),(No0913S,No304))),No305); (No1114S,(No0912S,(No1103S,((No306,((No0906S,((No1202S,No0910S),No1206S)),((No1007S,(No1208S,No0909S)),No0908S))),(No304,No0913S)))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),((No0908S,No1206S),(((No304,No0913S),No306),(No0906S,(No1202S,No0910S))))),(No0912S,No1103S)),No305); (No1114S,(((No0910S,(((No0908S,No1206S),No1202S),((No304,No0913S),No306))),No0906S),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No305); (((No0912S,No1103S),(((No1206S,(No0906S,(No0910S,No1202S))),(No0908S,((No304,No0913S),No306))),(No1007S,(No1208S,No0909S)))),No1114S,No305); ((No304,(No306,((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(No0908S,No1206S)),(No0906S,(No0913S,(No0910S,No1202S)))))),No1114S,No305); (((((No1208S,No0909S),No1007S),(No0912S,No1103S)),((((No0906S,(No0910S,No1202S)),No1206S),No0908S),(No306,(No304,No0913S)))),No1114S,No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(((No0906S,(No0910S,No1202S)),(No1206S,No0908S)),(No306,(No304,No0913S)))),No1114S,No305); ((No0912S,(((((No0906S,(No0910S,No1202S)),No0908S),((No0909S,No1208S),No1007S)),((No306,(No304,No0913S)),No1206S)),No1103S)),No1114S,No305); ((((((No0906S,(No0910S,No1202S)),No1206S),(No306,(No304,No0913S))),No0908S),((((No0909S,No1208S),No1007S),No1103S),No0912S)),No1114S,No305); (((((((No0913S,No1202S),No0910S),No0906S),(No0908S,No1206S)),(No306,No304)),(No1103S,(No0912S,((No0909S,No1208S),No1007S)))),No1114S,No305); (((No304,((No1206S,((No0913S,(No1202S,No0910S)),No0906S)),(No306,No0908S))),(((No0909S,No1208S),No1007S),(No1103S,No0912S))),No1114S,No305); (((((No1206S,((No0913S,(No1202S,No0910S)),No0906S)),No0908S),(No304,No306)),(((No0909S,No1208S),No1007S),(No1103S,No0912S))),No1114S,No305); ((((No0908S,(No306,(No1206S,((No0913S,(No1202S,No0910S)),No0906S)))),(((No0909S,No1208S),No1007S),(No1103S,No0912S))),No304),No1114S,No305); (((((No0909S,No1208S),No1007S),(No1103S,No0912S)),((No306,(No0913S,No304)),(No0908S,(No1206S,((No1202S,No0910S),No0906S))))),No1114S,No305); (((No1103S,No0912S),(((No0909S,No1208S),No1007S),((No0908S,(No1206S,((No1202S,No0910S),No0906S))),(No306,(No0913S,No304))))),No1114S,No305); (No1114S,(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No1202S,No0910S)))),(No0912S,(No1103S,(No1007S,(No0909S,No1208S))))),No305); (No1114S,(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No1202S,No0910S)))),(No0912S,(No1103S,(No1007S,(No0909S,No1208S))))),No305); (No1114S,(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No1202S,No0910S)))),(No0912S,(No1103S,(No1007S,(No0909S,No1208S))))),No305); (No1114S,(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No1202S,No0910S)))),(No0912S,(No1103S,(No0909S,(No1208S,No1007S))))),No305); (No1114S,(No304,((((No0906S,(No0913S,(No1202S,No0910S))),No306),(No0908S,No1206S)),(No0912S,(No1103S,(No1208S,(No1007S,No0909S)))))),No305); (No1114S,(((No0908S,((No0906S,(No1202S,No0910S)),No1206S)),(No0912S,(No1103S,(No1007S,(No0909S,No1208S))))),((No304,No0913S),No306)),No305); (((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),((((No1114S,(No304,No0913S)),No306),No0908S),((No0906S,(No1202S,No0910S)),No1206S)),No305); (No0912S,(No1103S,((((No306,((No1114S,No304),No0913S)),(No0906S,(No1202S,No0910S))),(No1206S,(No1007S,(No0909S,No1208S)))),No0908S)),No305); (((((No1206S,No0908S),(No306,No304)),(((No1202S,No0910S),No0913S),No0906S)),No1114S),((No0912S,No1103S),(No1208S,(No1007S,No0909S))),No305); (No1114S,((((No306,No304),(((No1202S,No0910S),No0913S),No0906S)),(No1206S,No0908S)),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No1202S,No0910S)))),(No0912S,(No1103S,(No1007S,(No0909S,No1208S))))),No305); (No1114S,((((No304,No0913S),No306),((No0908S,No1206S),(No0906S,(No1202S,No0910S)))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((No0912S,(((No0913S,(No306,No304)),No1103S),(No0908S,(No1206S,(No0906S,(No1202S,No0910S)))))),(No1007S,(No0909S,No1208S))),No305); (No1114S,(((No0908S,No1206S),(((No0913S,(No306,No304)),No0906S),(No1202S,No0910S))),(((No0909S,(No1208S,No1007S)),No0912S),No1103S)),No305); (No1114S,(((No0908S,No1206S),((No306,(No304,No0913S)),(No0906S,(No1202S,No0910S)))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (No1114S,(((No0908S,No1206S),((No306,(No304,No0913S)),(No0906S,(No1202S,No0910S)))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (((No1114S,(No306,(No304,No0913S))),((No1206S,(No0906S,(No1202S,No0910S))),No0908S)),((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),No305); (No1114S,((No0909S,(No1208S,No1007S)),(((No306,(No304,No0913S)),((No0906S,((No1202S,No0910S),No1206S)),No0908S)),(No0912S,No1103S))),No305); (No1114S,(((No306,(No304,No0913S)),((No0908S,No1206S),(No0906S,(No1202S,No0910S)))),((No1007S,(No0909S,No1208S)),(No1103S,No0912S))),No305); (No1114S,(((((No1202S,No0910S),No0906S),No1206S),(No0908S,(No306,(No304,No0913S)))),((No1007S,(No0909S,No1208S)),(No1103S,No0912S))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),(No306,(No304,No0913S)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((((No0906S,(No1202S,No0910S)),No1206S),No0908S),(No306,(No304,No0913S)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((((No0906S,(No1202S,No0910S)),No1206S),No0908S),(No306,(No304,No0913S)))),No305); (No1114S,(((No0912S,No1103S),((No1007S,No1208S),No0909S)),((No0906S,(No1202S,No0910S)),((No306,(No304,No0913S)),(No1206S,No0908S)))),No305); (No1114S,(((No0912S,No1103S),((No1007S,No1208S),No0909S)),((No1206S,(No0906S,(No1202S,No0910S))),((No306,(No304,No0913S)),No0908S))),No305); (No1114S,(((No0912S,No1103S),((No1007S,No1208S),No0909S)),((No1206S,(No0906S,(No1202S,No0910S))),((No306,(No304,No0913S)),No0908S))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(No0908S,((No1206S,(No0906S,(No1202S,No0910S))),(No306,(No304,No0913S))))),No305); ((No1208S,(No0909S,No1007S)),((No1114S,(No306,(No304,No0913S))),((No1206S,No0908S),(No1103S,((No0906S,(No1202S,No0910S)),No0912S)))),No305); ((((((No1202S,No0910S),No0906S),(No1206S,No0908S)),(((No1208S,No1007S),No0909S),(No0912S,No1103S))),(No306,(No304,No0913S))),No1114S,No305); ((((((No1202S,No0910S),No0906S),(No1206S,No0908S)),(((No1208S,No1007S),No0909S),(No0912S,No1103S))),(No306,(No304,No0913S))),No1114S,No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),(No306,(No304,No0913S)))),No305); (No1114S,((No1007S,(No0909S,No1208S)),(((((No306,(No304,No0913S)),(No0908S,No1206S)),(No1202S,No0910S)),No0906S),(No0912S,No1103S))),No305); ((((No1114S,((No304,No0913S),No306)),No0906S),((No0908S,No1206S),(No1202S,No0910S))),((No0912S,No1103S),(No1208S,(No1007S,No0909S))),No305); (No1114S,((No1007S,(No0909S,No1208S)),((((No304,No0913S),No306),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))),(No0912S,No1103S))),No305); ((((No1206S,(No0906S,(No1202S,No0910S))),No0908S),(No1114S,((No304,No0913S),No306))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),No305); (No1114S,((((No304,No0913S),No306),((No0908S,No1206S),((No1202S,No0910S),No0906S))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); (No1114S,(((No1206S,((No1202S,No0910S),No0906S)),(No0908S,((No304,No0913S),No306))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((No0912S,No1103S),((((No1206S,((No1202S,No0910S),No0906S)),No0908S),((No304,No0913S),No306)),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((((No1206S,((No1202S,No0910S),No0906S)),No0908S),((No304,No0913S),No306)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); (No1114S,(((((No1202S,No0910S),No0906S),(No0908S,No1206S)),((No304,No0913S),No306)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); (((((No0906S,(No1202S,No0910S)),No1206S),(No306,((No0913S,No304),No0908S))),(No0912S,(No1103S,(No1208S,(No1007S,No0909S))))),No1114S,No305); (((No0906S,((No306,(No0913S,No304)),((No0908S,No1206S),(No1202S,No0910S)))),((No0912S,(No0909S,(No1208S,No1007S))),No1103S)),No1114S,No305); (((((No1206S,(No0906S,(No1202S,No0910S))),No0908S),(No306,(No0913S,No304))),((No0912S,(No1007S,(No0909S,No1208S))),No1103S)),No1114S,No305); (((((No1206S,(No0906S,(No1202S,No0910S))),No0908S),(No306,(No0913S,No304))),((No0912S,(No1007S,(No0909S,No1208S))),No1103S)),No1114S,No305); ((((((No1206S,No0908S),No0906S),(No1202S,No0910S)),(No306,(No0913S,No304))),(No0912S,((No1007S,(No0909S,No1208S)),No1103S))),No1114S,No305); ((No304,((((No0908S,((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),(No1206S,(No0906S,(No1202S,No0910S)))),No306),No0913S)),No1114S,No305); (((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),(((No0908S,No1206S),(No0906S,(No1202S,No0910S))),(No306,(No1114S,(No0913S,No304)))),No305); ((((No1103S,No0912S),(No0909S,(No1208S,No1007S))),(((No1206S,(No0906S,(No1202S,No0910S))),(No306,(No0913S,No304))),No0908S)),No1114S,No305); ((((No1103S,No0912S),(No0909S,(No1208S,No1007S))),(((No1206S,(No0906S,(No1202S,No0910S))),(No306,(No0913S,No304))),No0908S)),No1114S,No305); (((No0906S,(No1202S,No0910S)),(((No1103S,No0912S),(No1007S,(No0909S,No1208S))),(No0908S,No1206S))),(No1114S,(No306,(No0913S,No304))),No305); ((No304,(No306,(((No0912S,(No1103S,(No0909S,(No1208S,No1007S)))),(No0908S,No1206S)),(No0906S,(No0913S,(No1202S,No0910S)))))),No1114S,No305); ((No304,((No306,((((No1202S,No0910S),No0906S),(No0912S,(No1103S,(No0909S,(No1208S,No1007S))))),(No0908S,No1206S))),No0913S)),No1114S,No305); ((((No0912S,No1103S),(No0909S,(No1208S,No1007S))),((No0908S,No1206S),(((No0913S,No304),No306),((No1202S,No0910S),No0906S)))),No1114S,No305); ((No0908S,(((No0913S,No304),No306),(((No0912S,No1103S),(No0909S,(No1208S,No1007S))),((No1202S,No0910S),(No1206S,No0906S))))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No0913S,No304),No306),((No0906S,(No1202S,No0910S)),(No0908S,No1206S)))),No1114S,No305); (((No1007S,(No0909S,No1208S)),((((((No1202S,No0910S),No0913S),No304),No306),((No0908S,No1206S),No0906S)),(No0912S,No1103S))),No1114S,No305); ((No304,(No306,(((No0908S,((((No1208S,(No1007S,No0909S)),No0912S),No1103S),No1206S)),No0906S),((No1202S,No0910S),No0913S)))),No1114S,No305); ((No304,(No306,(((((No1208S,(No1007S,No0909S)),No0912S),No1103S),(No1206S,No0908S)),(No0906S,(No1202S,(No0910S,No0913S)))))),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),(((No304,No306),(No1206S,No0908S)),(No0906S,(No0913S,(No1202S,No0910S))))),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No306,(No0913S,No304)),((No1206S,No0908S),(No0906S,(No1202S,No0910S))))),No1114S,No305); ((((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No0908S,No1206S),(No0906S,(No1202S,No0910S)))),(No1114S,(No306,(No0913S,No304))),No305); (No1114S,(((No306,(No0913S,No304)),((No0908S,No1206S),(No0906S,(No1202S,No0910S)))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); (No1114S,((No0912S,(((No306,(No0913S,No304)),((No1206S,(No0906S,(No1202S,No0910S))),No0908S)),(No1007S,(No1208S,No0909S)))),No1103S),No305); (No1114S,(No304,(((((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),(((No1007S,(No1208S,No0909S)),No0912S),No1103S)),No306),No0913S)),No305); (No1114S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),((No306,(No0913S,No304)),((No0906S,(No1202S,No0910S)),(No0908S,No1206S)))),No305); (No1114S,((No0909S,(No1007S,No1208S)),((No0912S,No1103S),((No306,(No0913S,No304)),((No0908S,No1206S),(No0906S,(No1202S,No0910S)))))),No305); (No1114S,((No306,(No0913S,No304)),((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),(No1103S,(No0912S,(No1007S,(No1208S,No0909S)))))),No305); (No1114S,((No0913S,(((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),(((No1007S,(No1208S,No0909S)),No0912S),No1103S)),No306)),No304),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),(No0908S,((No306,(No304,No0913S)),(No1206S,(No0906S,(No1202S,No0910S)))))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),(No0908S,((No306,(No304,No0913S)),(No1206S,(No0906S,(No1202S,No0910S)))))),No305); ((((No0912S,(No0909S,(No1007S,No1208S))),No1103S),(((No304,No0913S),No306),(No0908S,(No1206S,(No0906S,(No0910S,No1202S)))))),No1114S,No305); ((((No0912S,(No0909S,(No1007S,No1208S))),No1103S),(((No304,No0913S),No306),(No0908S,(No1206S,(No0906S,(No0910S,No1202S)))))),No1114S,No305); ((((No1007S,(No1208S,No0909S)),(No1103S,No0912S)),(((No304,No0913S),No306),(No0908S,(No0906S,((No0910S,No1202S),No1206S))))),No1114S,No305); ((((No1007S,(No1208S,No0909S)),(No1103S,No0912S)),((((No304,No0913S),No306),(No0906S,(No0910S,No1202S))),(No0908S,No1206S))),No1114S,No305); (((No0909S,(No1007S,No1208S)),((((No304,No0913S),No306),((No1206S,(No0906S,(No0910S,No1202S))),No0908S)),(No1103S,No0912S))),No1114S,No305); ((No304,(No306,((No0913S,(No0906S,(No0910S,No1202S))),(((No1103S,No0912S),(No0909S,(No1007S,No1208S))),(No0908S,No1206S))))),No1114S,No305); ((((No0909S,(No1007S,No1208S)),(No0908S,(((No306,No304),No0913S),(No0906S,((No0910S,No1202S),No1206S))))),(No0912S,No1103S)),No1114S,No305); (No0908S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(((No0906S,(No1202S,No0910S)),No1206S),(No306,(No1114S,(No0913S,No304))))),No305); ((((No1103S,No0912S),(No1208S,(No0909S,No1007S))),((No306,No304),((No0908S,No1206S),(No0906S,(No0913S,(No1202S,No0910S)))))),No1114S,No305); (((No0909S,(No1007S,No1208S)),(((No0908S,No1206S),((No0906S,(No1202S,No0910S)),(No306,(No0913S,No304)))),(No0912S,No1103S))),No1114S,No305); (((No0912S,(((No0906S,((No0910S,No1202S),No1206S)),(((No304,No0913S),No306),No0908S)),No1103S)),(No1007S,(No1208S,No0909S))),No1114S,No305); (((No0912S,No1103S),((No1007S,(No1208S,No0909S)),(((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No304,No0913S),No306)))),No1114S,No305); (((((No0908S,No1206S),(No0906S,(No0910S,No1202S))),((No304,No0913S),No306)),((No0912S,No1103S),(No1208S,(No0909S,No1007S)))),No1114S,No305); (((No0908S,(((No1206S,No0906S),(No0910S,No1202S)),((No304,No0913S),No306))),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),No1114S,No305); (No0908S,((No1206S,(No0906S,(No0910S,No1202S))),(((No1114S,(No304,No0913S)),No306),((No0912S,No1103S),(No0909S,(No1007S,No1208S))))),No305); (No1114S,(((((No0909S,No1007S),No1208S),No0912S),No1103S),((No306,(No304,No0913S)),((No0908S,No1206S),(No0906S,(No0910S,No1202S))))),No305); (No1114S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(No0908S,((No306,(No304,No0913S)),(No1206S,((No0910S,No1202S),No0906S))))),No305); (No1114S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(((No0910S,No1202S),No0906S),((No306,(No304,No0913S)),(No0908S,No1206S)))),No305); (No1114S,((No0909S,(No1007S,No1208S)),((No0912S,No1103S),((No306,(No304,No0913S)),(No0908S,(No1206S,((No0910S,No1202S),No0906S)))))),No305); (No1114S,(((((No1206S,((No0910S,No1202S),No0906S)),No0908S),No306),(No304,No0913S)),(((No0909S,(No1007S,No1208S)),No0912S),No1103S)),No305); (((No0912S,(No1103S,(No1007S,(No0909S,No1208S)))),(((No0913S,No304),No306),((No0906S,(No0910S,No1202S)),(No1206S,No0908S)))),No1114S,No305); ((No0912S,(No1103S,(No1007S,(No0909S,No1208S)))),((No1114S,((No0913S,No304),No306)),(No0908S,((No0906S,(No0910S,No1202S)),No1206S))),No305); (No1114S,((((No0913S,No304),No306),(No0908S,((No0906S,(No0910S,No1202S)),No1206S))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (No1114S,((No0912S,No1103S),((((No0913S,No304),No306),((No0906S,(No0910S,No1202S)),(No1206S,No0908S))),(No1007S,(No0909S,No1208S)))),No305); (No1114S,(((((No0906S,(No0910S,No1202S)),No1206S),(No0908S,((No0913S,No304),No306))),(No1007S,(No0909S,No1208S))),(No0912S,No1103S)),No305); (No1114S,(No304,((No306,(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),(((No0906S,(No0910S,No1202S)),No1206S),No0908S))),No0913S)),No305); (No1114S,((((No1007S,(No0909S,No1208S)),No0912S),No1103S),((((No0906S,(No0913S,(No0910S,No1202S))),No1206S),(No304,No0908S)),No306)),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No304,No306),(No0906S,((No0913S,(No0910S,No1202S)),(No0908S,No1206S))))),No305); (No1114S,(((No1206S,(No0906S,(No0910S,No1202S))),No0908S),(((No304,No0913S),No306),((No1007S,(No0909S,No1208S)),(No0912S,No1103S)))),No305); (No1114S,(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),((((No304,No0913S),No306),(No1206S,(No0906S,(No0910S,No1202S)))),No0908S)),No305); ((((No0912S,No1103S),(No0908S,(((No0913S,No304),No306),(No1206S,(No0906S,(No0910S,No1202S)))))),(No1007S,(No0909S,No1208S))),No1114S,No305); ((((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),((No0913S,No304),No306)),No1114S,No305); ((((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),((No0913S,No304),No306)),No1114S,No305); ((No0912S,No1103S),(((No1206S,((No0906S,(No0910S,No1202S)),((No1114S,(No0913S,No304)),No306))),No0908S),(No1007S,(No0909S,No1208S))),No305); (No1114S,((No0913S,No304),(No306,((No0908S,((No1206S,No0906S),(No0910S,No1202S))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))))),No305); (No1114S,((No1007S,(No0909S,No1208S)),((No0912S,No1103S),((((No0910S,No1202S),No0906S),(No1206S,No0908S)),(No306,(No304,No0913S))))),No305); (No1114S,((No0912S,No1103S),(((((No1206S,No0908S),No0910S),(No1202S,(No306,(No304,No0913S)))),No0906S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((((No0913S,(No0906S,(No0910S,No1202S))),No306),((No1206S,No0908S),(((No0909S,(No1208S,No1007S)),No0912S),No1103S))),No304),No305); ((((((No0906S,(No0910S,No1202S)),(No304,No0913S)),(No1206S,No0908S)),No306),(((No1007S,(No0909S,No1208S)),No0912S),No1103S)),No1114S,No305); ((((((No1206S,No0906S),(No0910S,No1202S)),((No304,No0913S),No0908S)),No306),((No1208S,(No1007S,No0909S)),(No0912S,No1103S))),No1114S,No305); (No1114S,(((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(No0906S,((No0910S,No1202S),No1206S))),((No0913S,No304),No306)),No0908S),No305); (No1114S,(((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),No0908S),(No1206S,(No0906S,(No0910S,No1202S)))),((No0913S,No304),No306)),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No0908S,No1206S),((No0906S,(No0910S,No1202S)),(No0913S,No304))),No306)),No305); (No1114S,(No0912S,((((((No0908S,No1206S),No0906S),(No0910S,No1202S)),(No306,(No0913S,No304))),(No1007S,(No0909S,No1208S))),No1103S)),No305); (No1114S,(((No1206S,(No0906S,(No0910S,No1202S))),No0908S),(No306,(((No0912S,(No1007S,(No0909S,No1208S))),No1103S),(No0913S,No304)))),No305); (No1114S,(((No1208S,(No1007S,No0909S)),(No1103S,No0912S)),(No0908S,((No306,(No304,No0913S)),(No1206S,(No0906S,(No0910S,No1202S)))))),No305); (No1114S,((No1103S,No0912S),(((No0908S,(No306,(No304,No0913S))),(No1206S,(No0906S,(No0910S,No1202S)))),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((No1007S,(No0909S,No1208S)),((No1103S,No0912S),(((No1206S,(No0906S,(No0910S,No1202S))),No0908S),(No306,(No304,No0913S))))),No305); (No1114S,((No1007S,(No0909S,No1208S)),((No1103S,No0912S),(((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No306,(No304,No0913S))))),No305); (No1114S,((((No0913S,(No0906S,(No0910S,No1202S))),(No0908S,No1206S)),(No306,No304)),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No305); ((((No1103S,(No0912S,((No1208S,No0909S),No1007S))),No0908S),(No1206S,(No0906S,(No0910S,No1202S)))),(((No0913S,No304),No306),No1114S),No305); (((((No1208S,No0909S),No1007S),(No1103S,No0912S)),((No0908S,No1206S),(((No0913S,No304),No306),(No0906S,(No0910S,No1202S))))),No1114S,No305); ((((No0909S,(No1007S,No1208S)),(No1103S,No0912S)),(((No0913S,No304),No306),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),No1114S,No305); (((No0909S,(No1007S,No1208S)),(No0912S,(((No1114S,(No0913S,No304)),No306),(((No0910S,No1202S),(No0908S,No1206S)),No0906S)))),No1103S,No305); (No1114S,(((No0913S,No304),No306),(((No0908S,No1206S),((No0910S,No1202S),No0906S)),((No1208S,(No0909S,No1007S)),(No1103S,No0912S)))),No305); (No1114S,(((((No0913S,No304),No306),((No1206S,(No1202S,No0910S)),No0906S)),No0908S),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No305); (No1114S,(((((No1202S,No0910S),No0906S),(No0908S,No1206S)),((No0913S,No304),No306)),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No305); (No1114S,(((((No1202S,No0910S),No0906S),(No0908S,No1206S)),((No0913S,No304),No306)),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No305); (No1114S,(((((No1202S,No0910S),No0906S),(No0908S,No1206S)),((No0913S,No304),No306)),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No305); (No1114S,((No0909S,(No1007S,No1208S)),((No1103S,No0912S),((((No1202S,No0910S),No0906S),(No0908S,No1206S)),((No0913S,No304),No306)))),No305); ((((No1103S,No0912S),(((No0913S,No304),No306),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),((No1208S,No0909S),No1007S)),No1114S,No305); (((No1103S,No0912S),(((No1206S,(No0906S,(No0910S,No1202S))),(No0908S,(No1208S,(No0909S,No1007S)))),((No0913S,No304),No306))),No1114S,No305); ((((No1206S,(No0906S,(No0910S,No1202S))),(((No0913S,No304),No306),((No1103S,No0912S),(No1007S,(No1208S,No0909S))))),No0908S),No1114S,No305); (((No0912S,((No0909S,(No1007S,No1208S)),No1103S)),(((No0908S,No1206S),(No0906S,(No0910S,No1202S))),((No0913S,No304),No306))),No1114S,No305); (((No0912S,((No1007S,(No1208S,No0909S)),No1103S)),(((No1206S,(No0906S,(No0910S,No1202S))),((No0913S,No304),No306)),No0908S)),No1114S,No305); (((No1007S,(No1208S,No0909S)),((No1103S,No0912S),(((No1206S,(No0906S,(No0910S,No1202S))),((No0913S,No304),No306)),No0908S))),No1114S,No305); ((((No0908S,No1206S),((No0910S,No1202S),No0906S)),(No1114S,((No0913S,No304),No306))),((No1103S,No0912S),(No0909S,(No1007S,No1208S))),No305); (No1114S,(((((No0908S,No1206S),No0906S),(((No0913S,No304),No306),(No0910S,No1202S))),(No1208S,(No0909S,No1007S))),(No1103S,No0912S)),No305); (No1114S,((No0906S,(((No0913S,No304),No306),((No0908S,No1206S),(No0910S,No1202S)))),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No305); (No1114S,((((No0913S,No304),No306),((No0908S,No1206S),(No0906S,(No0910S,No1202S)))),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No305); (((No0912S,No1103S),((No1007S,(No1208S,No0909S)),(((No306,(No304,No0913S)),No1206S),((No0906S,(No1202S,No0910S)),No0908S)))),No1114S,No305); ((((No306,(No304,No0913S)),((No0908S,No1206S),(No0906S,(No1202S,No0910S)))),((No0912S,No1103S),(No1208S,(No0909S,No1007S)))),No1114S,No305); (((No1007S,(No1208S,No0909S)),((((No0908S,No1206S),(No0906S,(No1202S,No0910S))),(No306,(No304,No0913S))),(No0912S,No1103S))),No1114S,No305); (((((No0908S,No1206S),(No0906S,(No1202S,No0910S))),(No306,(No304,No0913S))),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),No1114S,No305); (((No1007S,(No1208S,No0909S)),(((((((No1202S,No0910S),No0906S),No1206S),No0908S),(No306,(No304,No0913S))),No0912S),No1103S)),No1114S,No305); (((No0908S,(No306,((No304,No0913S),((No0906S,No1202S),(No0910S,No1206S))))),(((No1007S,(No1208S,No0909S)),No0912S),No1103S)),No1114S,No305); ((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(No1114S,(((No1206S,No0908S),(No0913S,((No1202S,No0910S),No0906S))),(No306,No304))),No305); (No1114S,((((No1206S,No0906S),No0908S),(((No1202S,No0910S),(No304,No0913S)),No306)),(No0912S,(No1103S,(No1007S,(No1208S,No0909S))))),No305); ((((No1114S,(No304,No0913S)),No306),((No1206S,No0908S),(No0906S,(No1202S,No0910S)))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),No305); ((((No304,No0913S),No306),((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(No0908S,((No1206S,No0906S),(No1202S,No0910S))))),No1114S,No305); (No1114S,((No0912S,(No1103S,(No0909S,(No1007S,No1208S)))),((((No0910S,No1202S),No0906S),(No0908S,No1206S)),(No306,(No0913S,No304)))),No305); (No1114S,((No0912S,(No1103S,(No0909S,(No1007S,No1208S)))),(((((No0910S,No1202S),No0906S),No1206S),No0908S),(No306,(No0913S,No304)))),No305); (No1114S,((No0912S,(No1103S,(No0909S,(No1007S,No1208S)))),((((No0910S,No1202S),No0906S),(No1206S,No0908S)),(No306,(No0913S,No304)))),No305); ((No0912S,No1103S),((No0909S,(No1007S,No1208S)),((((No0910S,No1202S),No0906S),(No1206S,No0908S)),(No1114S,(No306,(No0913S,No304))))),No305); (No1114S,(No306,((No0908S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No0910S,No1202S),(No0913S,No304)))),(No1206S,No0906S))),No305); (No1114S,(No306,(((No0908S,(No1206S,((No0910S,No1202S),No0906S))),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),(No0913S,No304))),No305); (No1114S,((No1007S,(No1208S,No0909S)),(((((No0913S,No304),No306),No0908S),(No1206S,((No0910S,No1202S),No0906S))),(No0912S,No1103S))),No305); (No1114S,((((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S)))),((No0912S,No1103S),(No1208S,(No0909S,No1007S)))),No305); (No1114S,((((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S)))),((No0912S,No1103S),(No1208S,(No0909S,No1007S)))),No305); (No1114S,((((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S)))),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No305); (No1114S,((No0912S,(No1103S,(No1007S,(No1208S,No0909S)))),(((((No0910S,No1202S),No0906S),No1206S),((No0913S,No304),No306)),No0908S)),No305); (No1114S,((No1007S,(No1208S,No0909S)),(No0912S,(((((No0910S,No1202S),No0906S),(No0908S,No1206S)),((No0913S,No304),No306)),No1103S))),No305); (No1114S,((((No1206S,((No0910S,No1202S),No0906S)),No0908S),((No0913S,No304),No306)),(((No1007S,(No1208S,No0909S)),No1103S),No0912S)),No305); (No1114S,((((No1206S,((No0910S,No1202S),No0906S)),No0908S),((No0913S,No304),No306)),(((No1007S,(No1208S,No0909S)),No1103S),No0912S)),No305); (No1114S,((((No1206S,((No0910S,No1202S),No0906S)),No0908S),((No0913S,No304),No306)),(((No1007S,(No1208S,No0909S)),No1103S),No0912S)),No305); (No1114S,((((No1206S,((No0910S,No1202S),No0906S)),No0908S),((No0913S,No304),No306)),(((No0909S,(No1007S,No1208S)),No1103S),No0912S)),No305); (No1114S,((No306,(No0913S,No304)),(((No0910S,No1202S),No0906S),(((No1007S,(No1208S,No0909S)),(No1103S,No0912S)),(No0908S,No1206S)))),No305); (No1114S,(((No0913S,No306),((((No0906S,(No0910S,No1202S)),No1206S),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No0908S)),No304),No305); (No1114S,((No1103S,(No0912S,(No1007S,(No1208S,No0909S)))),(((No304,No0913S),No306),(No0908S,((No0906S,(No0910S,No1202S)),No1206S)))),No305); (No1114S,((No1103S,(No0912S,(No0909S,(No1007S,No1208S)))),(No306,((No0908S,((No0906S,(No0910S,No1202S)),No1206S)),(No304,No0913S)))),No305); (No1114S,((No1103S,(No0912S,(No1007S,(No0909S,No1208S)))),(No0908S,((No1206S,(No0906S,(No1202S,No0910S))),((No0913S,No304),No306)))),No305); (((No0909S,(No1208S,No1007S)),No1114S),(((((No0908S,No1206S),((No0910S,No1202S),No0906S)),((No0913S,No304),No306)),No1103S),No0912S),No305); ((((No1103S,(No0912S,(No1007S,(No0909S,No1208S)))),((No0910S,No1202S),No0906S)),(No1206S,No0908S)),(No1114S,((No0913S,No304),No306)),No305); ((((No1103S,No0912S),(No1007S,(No0909S,No1208S))),(((No0910S,No1202S),(No1206S,No0906S)),(No0908S,((No0913S,No304),No306)))),No1114S,No305); (((((No1007S,(No0909S,No1208S)),No0912S),No1103S),((((No0906S,No0910S),No1202S),(No0908S,No1206S)),((No0913S,No304),No306))),No1114S,No305); ((((No0909S,(No1208S,No1007S)),No0912S),No1103S),(((((No0910S,No1202S),No1206S),No0906S),No0908S),((No0913S,(No1114S,No304)),No306)),No305); (((No0912S,(((No0908S,(((No0906S,No1202S),No0910S),No1206S)),((No0913S,No304),No306)),(No0909S,(No1208S,No1007S)))),No1103S),No1114S,No305); ((No0908S,(((No0913S,No304),No306),(((No0906S,(No1007S,(No0909S,No1208S))),(No0912S,(No1202S,No0910S))),(No1206S,No1103S)))),No1114S,No305); (((No0912S,No1103S),(No0908S,(((No0913S,No304),No306),(No0906S,((No1202S,No0910S),No1206S))))),((No0909S,(No1208S,No1007S)),No1114S),No305); (No1114S,((No0912S,No1103S),(((No0906S,(No1202S,No0910S)),((No0908S,No1206S),((No0913S,No304),No306))),(No0909S,(No1208S,No1007S)))),No305); (No1114S,((No1103S,(No0912S,(No1007S,(No0909S,No1208S)))),(No0908S,((No1206S,(No0906S,(No1202S,No0910S))),((No0913S,No304),No306)))),No305); (No1114S,(((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),(((No0908S,No1206S),(No0906S,(No1202S,No0910S))),((No0913S,No304),No306))),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),(((No0908S,No1206S),(No0906S,(No1202S,No0910S))),((No0913S,No304),No306))),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),(((No0908S,No1206S),(No0906S,(No1202S,No0910S))),((No0913S,No304),No306))),No305); (No1114S,((No1103S,No0912S),(((No0906S,(No1202S,No0910S)),((No0909S,(No1208S,No1007S)),(No0908S,No1206S))),((No0913S,No304),No306))),No305); (No1114S,(No1103S,(No0912S,((No0909S,(No1208S,No1007S)),(No0908S,(((No0913S,No304),No306),(No1206S,(No0906S,(No1202S,No0910S)))))))),No305); (No1114S,(((No1007S,(No0909S,No1208S)),((((((No1202S,No0910S),No0913S),No304),No306),No0906S),(No0908S,No1206S))),(No1103S,No0912S)),No305); (No1114S,((No0908S,(No1206S,(((No0913S,No304),No306),((No1202S,No0910S),No0906S)))),((No1103S,No0912S),(No0909S,(No1208S,No1007S)))),No305); (No1114S,(((((No0913S,No304),No306),((No1202S,No0910S),No0906S)),(No0908S,No1206S)),((No1103S,No0912S),(No0909S,(No1208S,No1007S)))),No305); ((((No0908S,(No1206S,((No1202S,No0910S),No0906S))),((No0913S,No304),No306)),(No0912S,No1103S)),(No1114S,(No1007S,(No0909S,No1208S))),No305); (((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No0908S,(No1206S,((No1202S,No0910S),No0906S))),(No1114S,(No306,(No0913S,No304)))),No305); (No1114S,(((No1206S,No0908S),(((No1202S,No0910S),No0906S),(No306,(No0913S,No304)))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (No1114S,(((No306,(No0913S,No304)),(No0908S,(No1206S,((No1202S,No0910S),No0906S)))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (No1114S,((No0909S,(No1208S,No1007S)),((No0912S,No1103S),((No0908S,(No1206S,((No1202S,No0910S),No0906S))),(No306,(No0913S,No304))))),No305); (No1114S,((((No306,No0913S),(((No1202S,No0910S),No0906S),(No0908S,No1206S))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No304),No305); (No1114S,((((No0909S,No1208S),No1007S),(No0912S,No1103S)),((No306,(No304,No0913S)),((((No1202S,No0910S),No0906S),No1206S),No0908S))),No305); (No1114S,((((No0909S,No1208S),No1007S),(No0912S,No1103S)),((No306,(No304,No0913S)),((((No1202S,No0910S),No0906S),No1206S),No0908S))),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No306,(No304,No0913S)),(((No1202S,No0910S),No0906S),(No1206S,No0908S)))),No305); (((No1103S,No0912S),(((No306,(No304,No0913S)),(((No1202S,No0910S),No0906S),(No1206S,No0908S))),(No0909S,(No1208S,No1007S)))),No1114S,No305); (((No1103S,No0912S),(((No306,(No304,No0913S)),(((No1202S,No0910S),No0906S),(No1206S,No0908S))),(No0909S,(No1208S,No1007S)))),No1114S,No305); ((((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),((No0908S,(No1206S,((No1202S,No0910S),No0906S))),(No306,(No0913S,No304)))),No1114S,No305); ((((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),((No0908S,(No1206S,((No1202S,No0910S),No0906S))),(No306,(No0913S,No304)))),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),(((No0908S,No1206S),No0906S),(No306,(((No1202S,No0910S),No0913S),No304)))),No1114S,No305); (((No0913S,(No306,((((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),(No0908S,No1206S)),(No0906S,(No1202S,No0910S))))),No304),No1114S,No305); ((((No1206S,(No0913S,(No0906S,(No1202S,No0910S)))),(No306,(No0908S,((No0909S,(No1208S,No1007S)),(No0912S,No1103S))))),No304),No1114S,No305); (((No0908S,((No306,(No304,(No0913S,(No1202S,No0910S)))),(No0906S,No1206S))),((No0912S,No1103S),(No1208S,(No1007S,No0909S)))),No1114S,No305); ((No304,((No306,((((No1202S,No0910S),No0906S),No1206S),(((No0912S,No1103S),(No0909S,(No1208S,No1007S))),No0908S))),No0913S)),No1114S,No305); (((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((No0908S,(((No1202S,No0910S),No0906S),No1206S)),(No306,((No0913S,No304),No1114S))),No305); (No1114S,((((((((No1202S,No0910S),No0906S),No1206S),No306),No0908S),((No0912S,No1103S),(No1208S,(No1007S,No0909S)))),No0913S),No304),No305); (No1114S,(((No0912S,No1103S),(No0909S,(No1208S,No1007S))),((No306,(No0913S,No304)),((No1202S,((No1206S,No0906S),No0910S)),No0908S))),No305); (((No306,((No304,(No1206S,(No0906S,((No0910S,No1202S),No0913S)))),No0908S)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); ((((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0913S,No304),No306)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); ((((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0913S,No304),No306)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); ((No304,((((No0906S,(No0913S,(No0910S,No1202S))),(No0908S,No1206S)),No306),((No1208S,(No1007S,No0909S)),(No0912S,No1103S)))),No1114S,No305); (((No1007S,(No0909S,No1208S)),(No0912S,(No1103S,((No0908S,((No1206S,(No0910S,No1202S)),No0906S)),((No304,No0913S),No306))))),No1114S,No305); ((((No1007S,(No0909S,No1208S)),((No0908S,((No1206S,(No0910S,No1202S)),No0906S)),((No304,No0913S),No306))),(No0912S,No1103S)),No1114S,No305); ((((((No0906S,(No0913S,(No0910S,No1202S))),No306),(No1206S,No0908S)),(((No1007S,No1208S),No0909S),(No0912S,No1103S))),No304),No1114S,No305); ((((((No0913S,No304),No306),(No1206S,(No0906S,(No0910S,No1202S)))),No0908S),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No1114S,No305); ((((((No0913S,No304),No306),(No1206S,(No0906S,(No0910S,No1202S)))),No0908S),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No1114S,No305); ((((((No0913S,No304),No306),No0906S),((No0910S,No1202S),(No0908S,No1206S))),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No1114S,No305); (No1114S,(No306,((((No0913S,No304),No0908S),((No1103S,(No1007S,(No0909S,No1208S))),No0912S)),(No1206S,(No0906S,(No0910S,No1202S))))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(No0908S,((No1206S,(No0906S,(No0910S,No1202S))),(No306,(No0913S,No304))))),No305); (No1114S,((No0912S,No1103S),((No1007S,(No0909S,No1208S)),((No306,(No0913S,No304)),(No0908S,(No1206S,(No0906S,(No0910S,No1202S))))))),No305); (No1114S,((No0912S,No1103S),((No1007S,(No0909S,No1208S)),((No306,(No0913S,No304)),(No0908S,(No1206S,(No0906S,(No0910S,No1202S))))))),No305); (No1114S,(((No306,(No0913S,No304)),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),No305); (No1114S,(((No306,(No0913S,No304)),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),No305); (No1114S,((No0909S,(No1208S,No1007S)),((((No306,(No0913S,No304)),(((No0910S,No1202S),No0906S),No1206S)),No0908S),(No0912S,No1103S))),No305); (No1114S,((No0912S,No1103S),((No0909S,(No1208S,No1007S)),((((No306,(No0913S,No304)),No0906S),(No0908S,(No0910S,No1202S))),No1206S))),No305); (No1114S,(((No0908S,(No306,(No0913S,No304))),(((No0910S,No1202S),No0906S),No1206S)),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((((No1206S,No0908S),((No0910S,No1202S),No0906S)),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),(No306,(No0913S,No304))),No305); (No1114S,(((((No0913S,No304),No306),((No0908S,No1206S),(No0906S,(No0910S,No1202S)))),(No1103S,No0912S)),((No1208S,No1007S),No0909S)),No305); ((No304,((No0913S,No306),(No0908S,((No1206S,(No0906S,(No0910S,No1202S))),(((No1208S,No1007S),No0909S),(No0912S,No1103S)))))),No1114S,No305); (((No0912S,No1103S),((((No1206S,(No0906S,(No0910S,No1202S))),((No304,No0913S),No306)),No0908S),(No1007S,(No0909S,No1208S)))),No1114S,No305); (((((No1114S,No304),No0913S),No306),(No0908S,(No1206S,(No0906S,(No0910S,No1202S))))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),No305); ((((((No0910S,No1202S),(No1206S,No0906S)),No0908S),((No304,No0913S),No306)),(((No1007S,(No0909S,No1208S)),No0912S),No1103S)),No1114S,No305); ((No0908S,((((No304,No0913S),No1114S),No306),((No0910S,No1202S),(No1206S,No0906S)))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),No305); ((((No0908S,((No0906S,(No0910S,No1202S)),No1206S)),((No304,No0913S),No306)),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No1114S,No305); (((No1007S,(No0909S,No1208S)),((((No0906S,(No0910S,No1202S)),(No1206S,No0908S)),((No304,No0913S),No306)),(No0912S,No1103S))),No1114S,No305); (((No1007S,(No0909S,No1208S)),((((No0906S,(No0910S,No1202S)),(No1206S,No0908S)),((No304,No0913S),No306)),(No0912S,No1103S))),No1114S,No305); (((((No1206S,(No0906S,(No0910S,No1202S))),No0908S),((No304,No0913S),No306)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); ((((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),((No306,(No304,No0913S)),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),No1114S,No305); ((((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),((No306,(No304,No0913S)),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),No1114S,No305); ((((No306,(No304,No0913S)),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),(((No1007S,(No0909S,No1208S)),No0912S),No1103S)),No1114S,No305); ((((No306,(No304,No0913S)),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),(((No1007S,(No0909S,No1208S)),No0912S),No1103S)),No1114S,No305); ((No0908S,(((((No1007S,(No0909S,No1208S)),No0912S),No1103S),(No306,(No1114S,(No304,No0913S)))),(No0906S,(No0910S,No1202S)))),No1206S,No305); (No1114S,((No306,(No304,No0913S)),(((((No1007S,(No0909S,No1208S)),No0912S),No1103S),No1206S),(No0908S,(No0906S,(No0910S,No1202S))))),No305); (No1114S,((No0912S,(No1103S,(No1007S,(No0909S,No1208S)))),((No0908S,(No1206S,(No0910S,(No0906S,No1202S)))),(No306,(No304,No0913S)))),No305); (No1114S,(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),(No0908S,((No306,No304),(No1206S,(No0906S,(No0913S,(No1202S,No0910S))))))),No305); (No1114S,((((((No1206S,(No0906S,(No0913S,(No1202S,No0910S)))),No0912S),(No1103S,No306)),No0908S),(No1007S,(No0909S,No1208S))),No304),No305); ((((No304,No1114S),No0913S),No306),(((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),((No1206S,(No0906S,(No1202S,No0910S))),No0908S)),No305); ((((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),(((No306,(No304,No0913S)),((No0910S,No1202S),No0906S)),(No1206S,No0908S))),No1114S,No305); ((No1103S,(No0912S,(No0909S,(No1208S,No1007S)))),((No1114S,(No306,(No304,No0913S))),((No1206S,((No0910S,No1202S),No0906S)),No0908S)),No305); (No1114S,((No306,(No304,No0913S)),(((No0910S,No1202S),No0906S),((((No1208S,(No1007S,No0909S)),No0912S),No1103S),(No0908S,No1206S)))),No305); (No1114S,(((((((No0908S,No1206S),No0906S),(No306,(No304,No0913S))),(No0910S,No1202S)),(No0909S,(No1208S,No1007S))),No0912S),No1103S),No305); (No1114S,((No0909S,(No1208S,No1007S)),((No0912S,No1103S),((No304,No0913S),(((No0908S,No1206S),(No0906S,(No0910S,No1202S))),No306)))),No305); (No1114S,((((No0906S,((No0910S,No1202S),No1206S)),No0908S),((No304,No0913S),No306)),(((No0909S,(No1208S,No1007S)),No0912S),No1103S)),No305); (No1114S,((((No0906S,((No0910S,No1202S),No1206S)),No0908S),((No304,No0913S),No306)),(((No0909S,(No1208S,No1007S)),No0912S),No1103S)),No305); (No1114S,((((No0906S,((No0910S,No1202S),No1206S)),No0908S),((No304,No0913S),No306)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((((No0906S,((No0910S,No1202S),No1206S)),No0908S),((No304,No0913S),No306)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((No0912S,No1103S),((((((No0906S,No0910S),No1202S),No1206S),No0908S),((No304,No0913S),No306)),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((No1103S,(No0912S,(No0909S,(No1208S,No1007S)))),(((No0913S,No304),No306),(No0908S,(No1206S,((No1202S,No0910S),No0906S))))),No305); (No1114S,((No1103S,No0912S),((No0909S,(No1208S,No1007S)),(((No0913S,No304),No306),(No0908S,(No1206S,((No1202S,No0910S),No0906S)))))),No305); ((((No0913S,No304),No306),No1114S),(((No1103S,No0912S),((No0909S,No1208S),No1007S)),(No0908S,(No1206S,((No1202S,No0910S),No0906S)))),No305); (No1114S,(((No0912S,No1103S),((No0909S,No1208S),No1007S)),(No0908S,((No0906S,(No1206S,(No1202S,No0910S))),((No0913S,No304),No306)))),No305); (No1114S,((No0912S,No1103S),(((No0909S,No1208S),No1007S),(((No0906S,(No1202S,No0910S)),No1206S),(((No0913S,No304),No306),No0908S)))),No305); (No1114S,((((No0913S,No304),No306),(No0908S,((No0906S,(No1202S,No0910S)),No1206S))),((No0912S,No1103S),((No0909S,No1208S),No1007S))),No305); (No1114S,((((No0913S,No304),No306),(No0908S,((No0906S,(No1202S,No0910S)),No1206S))),((No0912S,No1103S),((No0909S,No1208S),No1007S))),No305); (No1114S,((((No0913S,No304),No306),(((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),(No1103S,((No0909S,No1208S),No1007S)))),No0912S),No305); (No1114S,(((No0912S,No1103S),((No0909S,No1208S),No1007S)),((No0913S,(No304,No306)),(No0908S,((No0906S,(No1202S,No0910S)),No1206S)))),No305); (No1114S,(((No0912S,No1103S),((No0909S,No1208S),No1007S)),(((No306,(No0913S,No304)),(No0906S,(No1206S,No0908S))),(No1202S,No0910S))),No305); (No1114S,(No304,((No0913S,No306),(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),((No1206S,((No0910S,No1202S),No0906S)),No0908S)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No0906S,(No1206S,(No0910S,No1202S))),No0908S),((No304,No0913S),No306))),No305); (No1114S,((No0912S,No1103S),((No0909S,(No1208S,No1007S)),(((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),((No304,No0913S),No306)))),No305); (No1114S,(((No1206S,((No1202S,No0910S),No0906S)),(No0908S,((No304,No0913S),No306))),((No0912S,No1103S),(No1208S,(No1007S,No0909S)))),No305); (No1114S,(((No1206S,((No1202S,No0910S),No0906S)),(No0908S,((No304,No0913S),No306))),((No0912S,No1103S),((No1208S,No0909S),No1007S))),No305); (No1114S,(((No1206S,((No1202S,No0910S),No0906S)),(No0908S,((No304,No0913S),No306))),((No0912S,No1103S),((No1208S,No0909S),No1007S))),No305); (No1114S,(((No1206S,((No1202S,No0910S),No0906S)),(No0908S,((No304,No0913S),No306))),((No0912S,No1103S),((No1208S,No0909S),No1007S))),No305); (No1114S,(((No0906S,((No0908S,No1206S),(No1202S,No0910S))),((No304,No0913S),No306)),((No0912S,No1103S),((No1208S,No0909S),No1007S))),No305); (No1114S,(No304,((No0913S,No306),(No0908S,(((((No1208S,No0909S),No1007S),No0912S),No1103S),(No1206S,((No1202S,No0910S),No0906S)))))),No305); (No1114S,(((No304,No0913S),No306),((((No1202S,No0910S),No0906S),No1206S),(No0908S,((((No1208S,No0909S),No1007S),No0912S),No1103S)))),No305); (No1114S,((((No304,No0913S),No306),((No1206S,No0908S),((No0910S,No1202S),No0906S))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((No0912S,No1103S),(((((No0910S,No1202S),(No1206S,No0906S)),No0908S),((No304,No0913S),No306)),(No0909S,(No1208S,No1007S)))),No305); (No1114S,((((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),((No304,No0913S),No306)),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (No1114S,((((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),((No304,No0913S),No306)),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (No1114S,(((No1206S,((No1202S,No0910S),No0906S)),(No0908S,((No304,No0913S),No306))),(No0912S,(No1103S,(No0909S,(No1208S,No1007S))))),No305); (No1114S,(((No304,No0913S),No306),(((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No0908S,No1206S),((No1202S,No0910S),No0906S)))),No305); (No1114S,((No1208S,(No1007S,No0909S)),((No0912S,No1103S),(No306,((No0908S,((No1202S,No0910S),((No304,No0913S),No1206S))),No0906S)))),No305); (((No0912S,((No1007S,(No0909S,No1208S)),No1103S)),((No1206S,((No1202S,No0910S),No0906S)),(No0908S,((No304,No0913S),No306)))),No1114S,No305); ((((No0912S,No1103S),((((No1202S,No0910S),No0906S),(No0908S,No1206S)),((No304,No0913S),No306))),(No1007S,(No0909S,No1208S))),No1114S,No305); (No1114S,((No0912S,No1103S),((((No0908S,No1206S),((No1202S,No0910S),No0906S)),((No304,No0913S),No306)),(No0909S,(No1208S,No1007S)))),No305); ((((((No0908S,(No0909S,(No1007S,No1208S))),((No0906S,(No0910S,No1202S)),No1206S)),((No0913S,No304),No306)),No0912S),No1103S),No1114S,No305); (((((No0910S,No1202S),No0906S),(((No0913S,No304),No306),(No0908S,No1206S))),((No1103S,No0912S),(No0909S,(No1007S,No1208S)))),No1114S,No305); (((((No0910S,No1202S),No0906S),(((No0913S,No304),No306),(No0908S,No1206S))),((No1103S,No0912S),(No0909S,(No1007S,No1208S)))),No1114S,No305); (((No1103S,No0912S),((No0909S,(No1007S,No1208S)),(((No0913S,No304),No306),((No0908S,No1206S),((No0910S,No1202S),No0906S))))),No1114S,No305); (((((No0913S,No304),No306),((No0908S,No1206S),((No0910S,No1202S),No0906S))),((No1103S,No0912S),(No1007S,(No1208S,No0909S)))),No1114S,No305); (((No1103S,No0912S),((No0909S,(No1007S,No1208S)),((((No0913S,(No0910S,No1202S)),No0906S),(No304,(No0908S,No1206S))),No306))),No1114S,No305); (((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0913S,No304),No306)),((No1103S,No0912S),(No0909S,(No1007S,No1208S)))),No1114S,No305); (((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0913S,No304),No306)),((No1103S,No0912S),(No0909S,(No1007S,No1208S)))),No1114S,No305); (((No1103S,No0912S),((No0909S,(No1007S,No1208S)),(((((No0910S,No1202S),No0906S),No1206S),No0908S),((No0913S,No304),No306)))),No1114S,No305); (((No0913S,No304),(No306,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),(((No0910S,No1202S),No0906S),(No1206S,No0908S))))),No1114S,No305); (((No0912S,No1103S),((((No0913S,No304),No306),(No0908S,(No0906S,((No0910S,No1202S),No1206S)))),(No1208S,(No0909S,No1007S)))),No1114S,No305); (((((No0913S,No304),No306),(No0908S,(No1206S,(No0906S,(No0910S,No1202S))))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((((No0913S,No304),No306),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),((((No1007S,No1208S),No0912S),No1103S),No0909S)),No1114S,No305); ((((No1208S,(No0909S,No1007S)),(No1103S,No0912S)),((No0913S,(No1114S,No304)),No306)),((No0908S,No1206S),(No0906S,(No0910S,No1202S))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),(((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0913S,No304),No306))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0913S,No304),No306))),No305); ((No1007S,(No1208S,No0909S)),((No1103S,((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),No0912S)),((No0913S,(No1114S,No304)),No306)),No305); ((No304,((No1206S,(No0906S,(No0913S,(No0910S,No1202S)))),((((No1103S,No0912S),(No0909S,(No1007S,No1208S))),No0908S),No306))),No1114S,No305); ((No0912S,((((No304,No0913S),No306),((No0908S,No1206S),(No0906S,(No0910S,((No1007S,(No1208S,No0909S)),No1202S))))),No1103S)),No1114S,No305); ((((No1202S,No0910S),(((No304,No0913S),No306),((No0908S,No1206S),No0906S))),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No1114S,No305); ((((No306,(No304,No0913S)),(((No0910S,No1202S),No0906S),(No0908S,No1206S))),(((No1007S,(No1208S,No0909S)),No1103S),No0912S)),No1114S,No305); ((((No306,(No304,No0913S)),(((No0910S,No1202S),No0906S),(No0908S,No1206S))),(((No1007S,(No1208S,No0909S)),No1103S),No0912S)),No1114S,No305); (((((((No0910S,No1202S),No0906S),No1206S),(No306,(No304,No0913S))),No0908S),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No1114S,No305); (((((((No0910S,No1202S),No0906S),No1206S),(No306,(No304,No0913S))),No0908S),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No1114S,No305); (((((No0910S,No1202S),No0906S),(No0908S,No1206S)),(No306,(No304,No0913S))),(((No0909S,(No1007S,(No1208S,No1114S))),No0912S),No1103S),No305); (No1114S,((No306,(No304,No0913S)),(((No0906S,(No0910S,No1202S)),No1206S),((((No1208S,(No1007S,No0909S)),No0912S),No1103S),No0908S))),No305); (No1114S,((((No1208S,(No1007S,No0909S)),No0912S),No1103S),(((No0910S,No1202S),No0908S),((No306,(No304,No0913S)),(No1206S,No0906S)))),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No306,(No304,No0913S)),((No0908S,No1206S),(No0906S,(No0910S,No1202S))))),No305); (No1114S,((No0912S,No1103S),(((No304,((No0906S,(No0910S,No1202S)),No0913S)),((No0908S,No1206S),No306)),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((((No1007S,(No0909S,No1208S)),No0912S),No1103S),(No0908S,(((No0913S,No304),No306),(No1206S,((No1202S,No0910S),No0906S))))),No305); (No1114S,((No0912S,No1103S),((No0909S,(No1007S,No1208S)),((No306,(No304,No0913S)),((No0906S,(No1202S,No0910S)),(No1206S,No0908S))))),No305); (No1114S,(((No0908S,((No0906S,(No1202S,No0910S)),No1206S)),(No306,(No304,No0913S))),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No305); (No1114S,((((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),(No306,(No304,No0913S))),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),No305); (No1114S,((No0912S,No1103S),((No1007S,(No1208S,No0909S)),(((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),(No0913S,(No306,No304))))),No305); ((((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),(No306,((No1114S,No304),No0913S))),((No0912S,No1103S),(No0909S,(No1007S,No1208S))),No305); ((No304,(((No0906S,((No1202S,No0910S),No0913S)),(((No0912S,No1103S),(No1208S,(No0909S,No1007S))),(No1206S,No0908S))),No306)),No1114S,No305); (((No0912S,No1103S),((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),(No306,(No0913S,No304)))),((No1007S,(No1208S,No0909S)),No1114S),No305); (((No1206S,No0908S),((No306,(No0913S,No304)),((No1202S,No0910S),No0906S))),(No1114S,((No1103S,No0912S),(No1007S,(No1208S,No0909S)))),No305); (No1114S,(((No0908S,((No306,(No0913S,No304)),((No1202S,No0910S),(No1206S,No0906S)))),(No0912S,No1103S)),(No1007S,(No1208S,No0909S))),No305); (No1114S,((No0908S,(((No306,(No0913S,No304)),((No1202S,No0910S),No1206S)),No0906S)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (((No0912S,No1103S),(No0909S,(No1007S,No1208S))),((No0908S,((No0906S,(No1202S,No0910S)),No1206S)),(No1114S,(No306,(No304,No0913S)))),No305); (No1114S,(((No0908S,(((No0906S,No0910S),No1202S),No1206S)),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),(No306,(No304,No0913S))),No305); (No1114S,((((No0910S,(No0906S,(No0908S,No1206S))),No1202S),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),(No304,(No0913S,No306))),No305); (No1114S,((No0912S,No1103S),((No0909S,(No1007S,No1208S)),((((No304,No0913S),No306),No0908S),(No1206S,((No1202S,No0910S),No0906S))))),No305); (((No0912S,No1103S),(No1208S,(No0909S,No1007S))),((No0908S,(No1206S,((No1202S,No0910S),No0906S))),(((No1114S,No304),No0913S),No306)),No305); ((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No1206S,((No1202S,No0910S),No0906S)),(((No304,No0913S),No306),No0908S))),No1114S,No305); (((No0912S,No1103S),((No0909S,(No1007S,No1208S)),((No306,((No0906S,(No1206S,(No1202S,No0910S))),No0908S)),(No304,No0913S)))),No1114S,No305); (((((No306,(No1103S,((No1202S,No0910S),No0913S))),((No0906S,(No1208S,(No0909S,No1007S))),(No0908S,No1206S))),No0912S),No304),No1114S,No305); ((((No306,((No0912S,No1103S),(No0908S,No1206S))),(No0906S,((No1202S,No0910S),No0913S))),No304),((No1007S,(No1208S,No0909S)),No1114S),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(((No0913S,No304),No306),((No0906S,(No1202S,No0910S)),(No0908S,No1206S)))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No1103S,No0912S)),(((No0913S,No304),No306),(No0908S,((No1206S,No0906S),(No0910S,No1202S))))),No305); (No1114S,((No0909S,(No1007S,No1208S)),((No1103S,No0912S),(((No0913S,No304),No306),(No0908S,(No0906S,((No0910S,No1202S),No1206S)))))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),(((No0906S,(No0910S,No1202S)),((No0913S,No304),No306)),(No0908S,No1206S))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),(((No0906S,(No0910S,No1202S)),((No0913S,No304),No306)),(No0908S,No1206S))),No305); (No1114S,(((No1103S,No0912S),(No0909S,(No1007S,No1208S))),(((No0913S,No304),No306),((No0908S,No1206S),(No0906S,(No0910S,No1202S))))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),((((No0913S,No304),No306),(((No0910S,No1202S),No0906S),No1206S)),No0908S)),No305); (No1114S,(((No1103S,No0912S),(No0909S,(No1007S,No1208S))),(((((No0913S,No304),No306),(No0910S,No1202S)),(No1206S,No0906S)),No0908S)),No305); (No1114S,(((No1103S,No0912S),(No0909S,(No1007S,No1208S))),((((No0913S,No304),No306),(No0906S,(No0910S,No1202S))),(No1206S,No0908S))),No305); (No1114S,(((No1103S,No0912S),(No0909S,(No1007S,No1208S))),((((No0913S,No304),No306),(No0906S,(No0910S,No1202S))),(No1206S,No0908S))),No305); (No1114S,(No304,(No0913S,(No306,(((No1206S,No0908S),(No0906S,(No0910S,No1202S))),((No1103S,No0912S),(No1007S,(No1208S,No0909S))))))),No305); ((((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0913S,No304),No306))),No1114S,No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),((No1206S,((No0908S,No0906S),(No0910S,No1202S))),((No0913S,No304),No306))),No1114S,No305); ((No0912S,((((No0906S,(No0910S,No1202S)),((No1206S,No0908S),((No0913S,No304),No306))),(No0909S,(No1007S,No1208S))),No1103S)),No1114S,No305); (((No0906S,(((No1206S,No0908S),((No0910S,No1202S),(No0913S,No304))),No306)),((No1103S,No0912S),(No0909S,(No1007S,No1208S)))),No1114S,No305); (((No1103S,No0912S),((No1007S,(No1208S,No0909S)),(((No1202S,(No0906S,No0910S)),(No306,(No0913S,No304))),(No1206S,No0908S)))),No1114S,No305); (((No0909S,(No1007S,No1208S)),((((No0906S,(No0913S,(No0910S,No1202S))),(No306,No304)),(No1206S,No0908S)),(No1103S,No0912S))),No1114S,No305); (((((No0906S,(No0910S,No1202S)),(No306,(No0913S,No304))),(No1206S,No0908S)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((((((No0906S,(No0910S,No1202S)),No0912S),No1103S),(No0908S,No1206S)),(No306,(No0913S,No304))),(No1007S,(No1208S,No0909S))),No1114S,No305); (((((No306,(No0913S,No304)),((No0910S,No1202S),((No0908S,No1206S),No0906S))),No1103S),No0912S),((No0909S,(No1007S,No1208S)),No1114S),No305); (((((No1206S,(No0906S,(No0910S,No1202S))),No0908S),(No306,(No0913S,No304))),((No1103S,No0912S),(No0909S,(No1007S,No1208S)))),No1114S,No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No306,(No304,No0913S)))),No1114S,No305); ((((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),(No306,(No304,No0913S)))),No1114S,No305); ((((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No306,(No304,No0913S)))),No1114S,No305); ((((No1208S,(No0909S,No1007S)),(No0912S,No1103S)),((((No0906S,(No0910S,No1202S)),No1206S),(No306,(No304,No0913S))),No0908S)),No1114S,No305); ((((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(((No0906S,(No0910S,No1202S)),No1206S),((No306,(No304,No0913S)),No0908S))),No1114S,No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(No306,((No304,No0913S),(No0908S,((No0906S,(No0910S,No1202S)),No1206S))))),No1114S,No305); (((No0912S,No1103S),((((No304,No0913S),No306),(No0908S,No1206S)),((No0906S,(No0910S,No1202S)),(No1007S,(No1208S,No0909S))))),No1114S,No305); (((No0909S,(No1007S,No1208S)),(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No0910S,No1202S)))),(No0912S,No1103S))),No1114S,No305); (((((No304,No0913S),No306),((No0908S,No1206S),(No0906S,(No0910S,No1202S)))),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No1114S,No305); (((((No1206S,(No0906S,(No0910S,No1202S))),No0908S),((No304,No0913S),No306)),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No1114S,No305); (No1114S,((((No0906S,(No0910S,No1202S)),((((No304,No0913S),No306),No0908S),(((No1208S,No1007S),No0909S),No1206S))),No1103S),No0912S),No305); ((No0909S,(No1208S,No1007S)),((No1103S,((No0908S,No0912S),((No0906S,(No0910S,No1202S)),No1206S))),(No1114S,((No304,No0913S),No306))),No305); (No1114S,((((No304,No0913S),No306),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),(No0912S,(No1103S,(No0909S,(No1208S,No1007S))))),No305); (No1114S,(((((No0906S,No1206S),(No1202S,No0910S)),(No0912S,(No1103S,(No1007S,(No0909S,No1208S))))),No0908S),((No304,No0913S),No306)),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((((No304,No0913S),No306),((No1202S,No0910S),No0906S)),(No0908S,No1206S))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((((No304,No0913S),No306),((No1202S,No0910S),No0906S)),(No0908S,No1206S))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((((No304,No0913S),No306),((No1202S,No0910S),No0906S)),(No0908S,No1206S))),No305); (((No1007S,(No0909S,No1208S)),No1114S),((No0912S,No1103S),(((No304,No0913S),No306),((No1206S,((No1202S,No0910S),No0906S)),No0908S))),No305); (No1114S,((No0912S,No1103S),((No0908S,(No1206S,((No1202S,No0910S),No0906S))),((No1007S,(No0909S,No1208S)),((No304,No0913S),No306)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No304,No0913S),No306),(No0908S,(No0906S,(No1206S,(No1202S,No0910S)))))),No305); (No1114S,((((No1208S,No1007S),No0909S),(No0912S,No1103S)),(((No1206S,No0908S),No0906S),((No0910S,No1202S),((No304,No0913S),No306)))),No305); (No1114S,(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),(((No1206S,No0908S),(No0906S,(No0913S,(No0910S,No1202S)))),(No304,No306))),No305); (No1114S,((No0912S,No1103S),(No306,((No1007S,(No0909S,No1208S)),((No0908S,No304),(No1206S,(No0906S,((No0910S,No1202S),No0913S))))))),No305); (No1114S,(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),((((No0913S,No304),No306),(No0906S,(No0910S,No1202S))),(No1206S,No0908S))),No305); (No1114S,((No0912S,No1103S),((((No0913S,No304),No306),(((No0906S,(No0910S,No1202S)),No1206S),No0908S)),(No0909S,(No1208S,No1007S)))),No305); (No1114S,((((No0913S,No304),No306),(((No0906S,(No0910S,No1202S)),No1206S),No0908S)),((No1208S,(No1007S,No0909S)),(No0912S,No1103S))),No305); (No1114S,((((No0910S,No1202S),No0906S),(((No0913S,No304),No306),(No1206S,No0908S))),((No1208S,(No1007S,No0909S)),(No0912S,No1103S))),No305); (No1114S,((((No0910S,No1202S),No0906S),(((No0913S,No304),No306),(No1206S,No0908S))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((((No0910S,No1202S),No0906S),(((No0913S,No304),No306),(No1206S,No0908S))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); ((No0908S,(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),(No1206S,((No0910S,No1202S),No0906S)))),(No1114S,((No0913S,No304),No306)),No305); (No1114S,(((No0912S,No1103S),((No1208S,No1007S),No0909S)),(((No304,No0913S),No306),((No1206S,No0908S),(No0906S,(No0910S,No1202S))))),No305); (No1114S,(((No0912S,No1103S),((No1208S,No1007S),No0909S)),(((No304,No0913S),No306),((No1206S,No0908S),(No0906S,(No0910S,No1202S))))),No305); (No1114S,(((No0912S,No1103S),((No1208S,No1007S),No0909S)),(((No304,No0913S),No306),(No0908S,((No0906S,(No0910S,No1202S)),No1206S)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No304,No0913S),No306),(No0908S,(((No0906S,No1202S),No1206S),No0910S)))),No305); (No1114S,((No0912S,No1103S),((((No304,No0913S),No306),(No0909S,(No1208S,No1007S))),(No0908S,((No0906S,(No1202S,No0910S)),No1206S)))),No305); ((((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),((No1114S,(No304,No0913S)),No306)),(No1103S,((No0909S,(No1208S,No1007S)),No0912S)),No305); ((((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),((No306,(No0913S,No304)),No1114S)),(No1103S,((No0909S,(No1208S,No1007S)),No0912S)),No305); (No1114S,((((No0906S,(No1202S,No0910S)),(No1206S,No0908S)),(No306,(No0913S,No304))),((No1208S,(No1007S,No0909S)),(No0912S,No1103S))),No305); (No1114S,(((No0906S,(No1206S,No0908S)),(No306,(((No1202S,No0910S),No0913S),No304))),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (No1114S,(((No0913S,No304),No306),(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),(No0908S,(((No1202S,No0910S),No0906S),No1206S)))),No305); (No1114S,(((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0913S,No304),No306)),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No305); (No1114S,(((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0913S,No304),No306)),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No305); (No1114S,(((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0913S,No304),No306)),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No305); (No1114S,((No0909S,(No1007S,No1208S)),((No1103S,No0912S),(No306,((No0908S,(No1206S,(No0906S,(No0913S,(No0910S,No1202S))))),No304)))),No305); (No1114S,((No306,((No0913S,No304),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((((No1206S,(No0906S,(No0910S,No1202S))),No0908S),(No306,(No304,No0913S))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((No0909S,(No1007S,No1208S)),((No0912S,No1103S),(((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No306,(No304,No0913S))))),No305); (No1114S,((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No306,(No304,No0913S))),(((No1208S,(No0909S,No1007S)),No0912S),No1103S)),No305); (No1114S,((((No1206S,((No0910S,No1202S),No0906S)),No0908S),(No306,(No304,No0913S))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((((No1206S,((No0910S,No1202S),No0906S)),No0908S),(No306,(No304,No0913S))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); (No1114S,(((No0906S,((No0910S,No1202S),((No304,No306),No0913S))),(No0908S,No1206S)),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No305); (No1114S,((((No304,No306),No0913S),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),((No1208S,(No0909S,No1007S)),(No1103S,No0912S))),No305); (No1114S,(No304,(((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No306),No0913S)),No305); (No1114S,((((No0909S,(No1007S,No1208S)),No0912S),No1103S),((No306,((No0913S,No304),((No1202S,(No1206S,No0910S)),No0906S))),No0908S)),No305); (No1114S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),((No306,(No0913S,No304)),(((No1202S,No0910S),No0906S),(No0908S,No1206S)))),No305); (No1114S,((No0912S,(No1208S,(No0909S,(No1103S,No1007S)))),(((No306,(No0913S,No304)),(No1206S,((No1202S,No0910S),No0906S))),No0908S)),No305); (No1114S,(((No0912S,(No0909S,(No1007S,No1208S))),No1103S),((((No1202S,No0910S),No0906S),(No1206S,No0908S)),(No306,(No0913S,No304)))),No305); (No1114S,((No0912S,((((((No1202S,No0910S),No1206S),No0906S),((No0913S,No304),No0908S)),No306),(No1007S,(No1208S,No0909S)))),No1103S),No305); ((No0909S,((No1114S,No1007S),No1208S)),((No0912S,No1103S),((No306,(No0913S,No304)),(No0908S,(No1206S,(No0906S,(No1202S,No0910S)))))),No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(((No1206S,(No0906S,(No1202S,No0910S))),No0908S),(No306,(No0913S,No304)))),No1114S,No305); (((No1103S,(No0912S,(No1007S,(No0909S,No1208S)))),(((No306,(No304,No0913S)),((No0906S,(No0910S,No1202S)),No1206S)),No0908S)),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(((No0906S,(No0910S,No1202S)),No1206S),(No0908S,(No306,(No304,No0913S))))),(No0912S,No1103S)),No1114S,No305); (((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No306,(No304,No0913S))),((No0912S,No1103S),(No1208S,(No1007S,No0909S)))),No1114S,No305); (((No0912S,No1103S),((No0909S,(No1208S,No1007S)),((((No0906S,(No0910S,No1202S)),No1206S),No0908S),(No306,(No304,No0913S))))),No1114S,No305); ((((((No0906S,(No0913S,(No0910S,No1202S))),(No1206S,No0908S)),No306),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No304),No1114S,No305); (((No0912S,No1103S),((No0909S,(No1208S,No1007S)),(((No304,No306),(No1206S,No0908S)),(No0906S,(No0913S,(No0910S,No1202S)))))),No1114S,No305); (((((No0910S,(No0906S,No1202S)),(No1206S,No0908S)),((No304,No0913S),No306)),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No1114S,No305); ((((No306,((((No0910S,No1202S),No1206S),No0906S),(No304,No0913S))),No0908S),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No1114S,No305); (((No0912S,No1103S),(((No0909S,No1208S),No1007S),((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No304,No0913S),No306)))),No1114S,No305); (((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),(((No304,No0913S),No306),No1114S),No305); (((No1103S,(No0912S,(No1007S,(No0909S,No1208S)))),(((No306,(No304,No0913S)),((No0906S,(No0910S,No1202S)),No1206S)),No0908S)),No1114S,No305); ((No1103S,(No0912S,(No0909S,(No1208S,No1007S)))),((No306,(No1114S,(No304,No0913S))),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),No305); ((No1103S,(No0912S,((No0909S,(No1208S,No1007S)),((No1206S,(No306,(No304,No0913S))),((No0906S,(No0910S,No1202S)),No0908S))))),No1114S,No305); (((((No0908S,No1206S),(No306,(No304,No0913S))),(No0906S,(No0910S,No1202S))),((No1103S,No0912S),(No1007S,(No0909S,No1208S)))),No1114S,No305); ((((No306,((No304,No0913S),No0908S)),((No1206S,No0906S),(No0910S,No1202S))),((No1103S,No0912S),(No1007S,(No0909S,No1208S)))),No1114S,No305); ((((No1103S,No0912S),(No1208S,(No1007S,No0909S))),(((No1206S,(No306,(No304,No0913S))),((No0908S,No1202S),No0906S)),No0910S)),No1114S,No305); ((No304,((No306,(((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),((No1206S,No0908S),((No0910S,No1202S),No0906S)))),No0913S)),No1114S,No305); (((No1103S,(No0912S,(No0909S,(No1208S,No1007S)))),(No0908S,(((No0910S,No1202S),No0906S),No1206S))),(No1114S,((No0913S,No304),No306)),No305); (No1114S,(((No0908S,(((No0910S,No1202S),No0906S),No1206S)),(((No1007S,(No0909S,No1208S)),No0912S),No1103S)),((No0913S,No304),No306)),No305); (No1114S,((((No0908S,((No0913S,(No0910S,No1202S)),No306)),(No1206S,(((No1208S,(No1007S,No0909S)),No0912S),No1103S))),No0906S),No304),No305); (No1114S,(((No0908S,No1206S),((No1202S,No0910S),((No306,(No304,No0913S)),No0906S))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); ((No0906S,(((No0908S,No1206S),(No1202S,No0910S)),(No306,(No304,No0913S)))),((No1114S,(No1007S,(No0909S,No1208S))),(No0912S,No1103S)),No305); (No1114S,((((No1007S,No0909S),No1208S),(No0912S,No1103S)),((No0908S,(No1206S,(No0906S,(No0913S,(No1202S,No0910S))))),(No306,No304))),No305); (No1114S,((((No1007S,(No0909S,No1208S)),(No0912S,No0908S)),((No1206S,(No0906S,(No1202S,No0910S))),No1103S)),(No306,(No0913S,No304))),No305); (((No0908S,(No306,(No1114S,(No0913S,No304)))),(No1206S,(No0906S,(No1202S,No0910S)))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),No305); ((((No0908S,No1206S),(((No306,(No0913S,No304)),No0906S),(No1202S,No0910S))),(((No0909S,(No1208S,No1007S)),No0912S),No1103S)),No1114S,No305); (((((((No0908S,No1206S),(No0906S,(No1202S,No0910S))),(No306,(No0913S,No304))),(No1007S,(No0909S,No1208S))),No0912S),No1103S),No1114S,No305); (((((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),((No0913S,No304),No306)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); (No1114S,((((No1206S,((No1202S,No0910S),No0906S)),(No0908S,((No0913S,No304),No306))),(No1208S,(No1007S,No0909S))),(No0912S,No1103S)),No305); (No1114S,(((No0912S,No1103S),(((No0913S,No304),No306),(No0908S,(No1206S,((No1202S,No0906S),No0910S))))),(No1007S,(No0909S,No1208S))),No305); (((((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),((No0912S,No1103S),((No1208S,No1007S),No0909S))),(No306,(No0913S,No304))),No1114S,No305); ((No0912S,(No1103S,(No1007S,(No0909S,No1208S)))),((No306,(No1114S,(No0913S,No304))),((No0906S,(No1202S,No0910S)),(No0908S,No1206S))),No305); (((No1007S,(No0909S,No1208S)),(((No0913S,No306),No304),((No0912S,(No0906S,(No1202S,No0910S))),(No1103S,(No0908S,No1206S))))),No1114S,No305); (No1114S,((No0912S,No1103S),((No1007S,(No0909S,No1208S)),(((No0908S,No1206S),(No0906S,(No1202S,No0910S))),((No0913S,No304),No306)))),No305); (No1114S,((No304,No306),((((No0912S,No1103S),(No0909S,(No1208S,No1007S))),No0908S),(No1206S,(No0906S,(No0913S,(No1202S,No0910S)))))),No305); (No1114S,(No304,(No306,((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),No0908S),(No1206S,(No0906S,(No0913S,(No1202S,No0910S))))))),No305); ((((No0912S,No1103S),(No0909S,(No1208S,No1007S))),(((No0913S,No304),No306),(No0908S,(No0906S,((No1202S,No0910S),No1206S))))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(No0908S,(((No1206S,No0906S),(No0910S,No1202S)),((No0913S,No304),No306)))),No1114S,No305); ((((No0912S,No1103S),(No0909S,(No1208S,No1007S))),(((No0906S,(No0910S,No1202S)),((No0913S,No304),No306)),(No0908S,No1206S))),No1114S,No305); ((((No0912S,No1103S),(No0909S,(No1208S,No1007S))),(((No0913S,No304),No306),(No0908S,(No1206S,(No0906S,(No0910S,No1202S)))))),No1114S,No305); (((((No0909S,No1208S),No1007S),(No0912S,No1103S)),((No306,(No0913S,No304)),((No0906S,(No1202S,No0910S)),(No0908S,No1206S)))),No1114S,No305); (((No306,(No0913S,No304)),(((No1202S,No0910S),((((No0909S,No1208S),No1007S),(No0912S,No1103S)),(No0908S,No1206S))),No0906S)),No1114S,No305); (((No0909S,(No1208S,No1007S)),(((No306,(No0913S,No304)),((No1206S,(No0906S,(No1202S,No0910S))),No0908S)),(No0912S,No1103S))),No1114S,No305); ((No1114S,(No306,(No0913S,No304))),(((No1206S,(No0906S,(No1202S,No0910S))),No0908S),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No305); ((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),(No306,(No0913S,No304)))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),(No306,(No0913S,No304)))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),(No306,(No0913S,No304)))),No1114S,No305); ((((No0912S,No1103S),(No1208S,(No1007S,No0909S))),((No0906S,(No1202S,No0910S)),((No1206S,No0908S),(No304,(No306,No0913S))))),No1114S,No305); ((No304,((((No0906S,((No0912S,No1103S),((No1007S,No0909S),No1208S))),(No1206S,No0908S)),No306),((No1202S,No0910S),No0913S))),No1114S,No305); (((((No0909S,No1208S),No1007S),((((No0906S,(No1202S,No0910S)),No1206S),No0908S),(No306,(No304,No0913S)))),(No0912S,No1103S)),No1114S,No305); (((((No0913S,No304),No306),(No0908S,((No0906S,(No0910S,No1202S)),No1206S))),(((No1007S,No1208S),No0909S),(No0912S,No1103S))),No1114S,No305); (((((No0913S,No304),No306),(No0908S,(No1206S,(No1202S,(No0906S,No0910S))))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); (((((No0913S,No304),No306),(No0908S,(No1206S,(No1202S,(No0906S,No0910S))))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); (((((No1206S,No0908S),(No0906S,(No1202S,No0910S))),((No0913S,No304),No306)),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); (((((No1206S,No0908S),(No0906S,(No1202S,No0910S))),((No0913S,No304),No306)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((((No0913S,No304),No306),((No1206S,(No0906S,(No1202S,No0910S))),No0908S)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((No1208S,(No0909S,No1007S)),((No0912S,No1103S),(((No0913S,No304),No306),((No0906S,(No1202S,No0910S)),(No0908S,No1206S))))),No1114S,No305); (((No1007S,(No1208S,No0909S)),((No0912S,No1103S),((No0913S,No304),(No306,(No0908S,(No1206S,(No0906S,(No1202S,No0910S)))))))),No1114S,No305); (((No1208S,(No0909S,No1007S)),((No0912S,No1103S),(((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),((No0913S,No304),No306)))),No1114S,No305); ((No1114S,((No0912S,No1103S),(No0908S,(((No0913S,No304),No306),(No1206S,(No0906S,(No1202S,No0910S))))))),(No1007S,(No0909S,No1208S)),No305); (((((No0913S,No304),No306),(No0908S,((No0906S,(No0910S,No1202S)),No1206S))),(((No1007S,No1208S),No0909S),(No0912S,No1103S))),No1114S,No305); ((((((No0913S,No304),No306),((No0910S,No1202S),(No1206S,No0908S))),No0906S),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); ((((((No0913S,No304),No306),((No0910S,No1202S),(No1206S,No0908S))),No0906S),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); (((((No0906S,(No0910S,No1202S)),No1206S),(No0908S,((No0913S,No304),No306))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); (((No0913S,(No306,(((No0906S,(No0910S,No1202S)),No1206S),(((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),No0908S)))),No304),No1114S,No305); (((No0912S,No1103S),(No1206S,((No0906S,No0908S),((No0910S,No1202S),((No304,No0913S),No306))))),(No1114S,(No1007S,(No1208S,No0909S))),No305); (No1114S,(((No0912S,No1103S),(No0909S,(No1007S,No1208S))),(((No304,No0913S),No306),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),No305); (No1114S,(((No0912S,No1103S),(No0909S,(No1007S,No1208S))),(((No304,No0913S),No306),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),No305); (No1114S,(((No0912S,No1103S),(No0909S,(No1007S,No1208S))),(((No304,No0913S),No306),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),No305); (No1114S,((No0909S,(No1007S,No1208S)),((((No304,No0913S),No306),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),(No0912S,No1103S))),No305); (No1114S,((No1103S,(No0912S,(No1007S,(No0909S,No1208S)))),((((No304,No0913S),No306),(No1206S,No0908S)),(No0906S,(No0910S,No1202S)))),No305); (No1114S,(No1103S,(No0912S,(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No0910S,No1202S)))),(No0909S,(No1208S,No1007S))))),No305); (No1114S,((((No0909S,No1208S),No1007S),(No1103S,No0912S)),(((No304,No0913S),(No306,No0908S)),(No1206S,(No0906S,(No0910S,No1202S))))),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),((No0906S,(No0910S,No1202S)),(((No304,No0913S),No306),(No0908S,No1206S)))),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),((No0906S,(No0910S,No1202S)),(((No304,No0913S),No306),(No0908S,No1206S)))),No305); (No1114S,(((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),((No0906S,((No0910S,No1202S),(No0908S,No1206S))),((No304,No0913S),No306))),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),(No306,((((No0910S,No1202S),(No304,No0913S)),No0906S),(No0908S,No1206S)))),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),(((No0908S,No1206S),((No1202S,No0910S),No0906S)),(No306,(No304,No0913S)))),No305); (((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),(No1114S,((No1206S,((No1202S,No0910S),No0906S)),(No306,(No0908S,(No304,No0913S))))),No305); (No1114S,((No1007S,(No0909S,No1208S)),((((No1206S,((No1202S,No0910S),No0906S)),(No306,(No304,No0913S))),No0908S),(No0912S,No1103S))),No305); (No1114S,((((No1206S,(No0906S,(No0910S,No1202S))),No0908S),((No0913S,No304),No306)),(No1103S,(No0912S,((No1208S,No0909S),No1007S)))),No305); (No1114S,((((No1206S,(No0906S,(No0910S,No1202S))),No0908S),((No0913S,No304),No306)),(No1103S,(No0912S,((No1208S,No0909S),No1007S)))),No305); (No1114S,((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0913S,No304),No306)),(No1103S,(No0912S,(No0909S,(No1007S,No1208S))))),No305); (No1114S,((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0913S,No304),No306)),(No1103S,(No0912S,(No0909S,(No1007S,No1208S))))),No305); (((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),(No1114S,(No0913S,(No304,No306)))),(No1103S,(No0912S,(No1208S,(No0909S,No1007S)))),No305); (No1114S,((No306,((((No0909S,(No1007S,No1208S)),(No1103S,No0912S)),(No1206S,No0908S)),(No0906S,(No0913S,(No0910S,No1202S))))),No304),No305); ((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(((No306,((No0913S,No304),No1114S)),No0908S),(No1206S,(No0906S,(No1202S,No0910S)))),No305); ((((((No0913S,No306),(No0906S,(No1202S,No0910S))),(No0908S,((No1007S,(No1208S,No0909S)),(No0912S,No1103S)))),No1206S),No304),No1114S,No305); (((No0908S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No0913S,No306),(No1206S,(No0906S,(No1202S,No0910S)))))),No304),No1114S,No305); (((((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),((No304,No0913S),No306)),(((No0909S,(No1007S,No1208S)),No0912S),No1103S)),No1114S,No305); (No1114S,((((No1206S,(No0906S,(No0910S,No1202S))),No0908S),((No0913S,No304),No306)),(No1103S,(No0912S,(No0909S,(No1007S,No1208S))))),No305); (No1114S,(((((No0906S,(No0910S,No1202S)),((No0908S,No1206S),(No0913S,No304))),No306),(No1208S,(No0909S,No1007S))),(No1103S,No0912S)),No305); (No1114S,(((No0908S,(((No1206S,No0906S),(No0910S,No1202S)),((No1007S,(No1208S,No0909S)),(No1103S,No0912S)))),No306),(No0913S,No304)),No305); (No1114S,(((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),(No306,(No0913S,No304))),((No1208S,(No0909S,No1007S)),(No1103S,No0912S))),No305); (No1114S,((No0906S,((No1206S,No0908S),((No306,(No0913S,No304)),(No0910S,No1202S)))),((No1103S,No0912S),((No1208S,No1007S),No0909S))),No305); (No1114S,((No1007S,(No0909S,No1208S)),((((No0906S,(No0910S,No1202S)),No1206S),((No306,(No0913S,No304)),No0908S)),(No0912S,No1103S))),No305); (No1114S,((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No306,(No0913S,No304))),((No1007S,(No0909S,No1208S)),(No1103S,No0912S))),No305); (No1114S,((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No306,(No0913S,No304))),((No1007S,(No0909S,No1208S)),(No1103S,No0912S))),No305); (No1114S,((No306,(No0913S,No304)),((((No0906S,No1206S),(No1202S,No0910S)),No0908S),((No1208S,(No1007S,No0909S)),(No1103S,No0912S)))),No305); (No1114S,(((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),(((No306,(No0913S,No304)),((No1202S,No0910S),No0906S)),(No0908S,No1206S))),No305); (((((((No304,No0913S),No0908S),(No306,((No1202S,No0910S),(No1206S,No0906S)))),(No0909S,(No1208S,No1007S))),No1103S),No0912S),No1114S,No305); (((No1007S,(No0909S,No1208S)),((No1103S,No0912S),((((No0906S,(No1206S,No0910S)),No1202S),((No306,No304),No0913S)),No0908S))),No1114S,No305); (((((No0906S,(No0910S,No1202S)),(No306,(No304,No0913S))),(No1206S,No0908S)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((((No0906S,((No0910S,No1202S),No1206S)),(No306,(No304,No0913S))),No0908S),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((((No0906S,((No0910S,No1202S),No1206S)),(No306,(No304,No0913S))),No0908S),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((No306,(No304,No0913S)),(((No0908S,(No0906S,(No0910S,No1202S))),No1206S),((No1007S,(No0909S,No1208S)),(No0912S,No1103S)))),No1114S,No305); (((No1007S,(No0909S,No1208S)),((No0912S,No1103S),((No306,((No0906S,(No0913S,(No0910S,No1202S))),(No304,No0908S))),No1206S))),No1114S,No305); (((No304,No306),(((((No1007S,(No0909S,No1208S)),No0912S),No1103S),(No0906S,(No0913S,(No0910S,No1202S)))),(No0908S,No1206S))),No1114S,No305); (((No0912S,(No1103S,(No1007S,(No0909S,No1208S)))),((No306,(No0913S,No304)),((No0906S,(No1206S,(No0910S,No1202S))),No0908S))),No1114S,No305); (((No0912S,(No1103S,(No1007S,(No0909S,No1208S)))),((No306,(No0913S,No304)),((No1206S,((No0910S,No1202S),No0906S)),No0908S))),No1114S,No305); (No0906S,(((No0912S,((No1208S,(No1007S,No0909S)),No1103S)),(No306,(No1114S,(No304,No0913S)))),((No1206S,No0908S),(No1202S,No0910S))),No305); ((((No0912S,No1103S),((No306,(No304,No0913S)),((No1206S,No0908S),((No1202S,No0910S),No0906S)))),(No1208S,(No1007S,No0909S))),No1114S,No305); (((No0912S,No1103S),(((No0908S,((No1202S,No0910S),(No1206S,No0906S))),(No306,(No304,No0913S))),(No0909S,(No1208S,No1007S)))),No1114S,No305); (((((No0913S,(No1202S,No0910S)),((((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),(No0908S,No1206S)),No0906S)),No306),No304),No1114S,No305); (((No0913S,(No306,(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),(((No0906S,(No1202S,No0910S)),No1206S),No0908S)))),No304),No1114S,No305); (((No0912S,No1103S),((((No0910S,(No0906S,No1202S)),(No0908S,No1206S)),(No306,(No0913S,No304))),(No0909S,(No1208S,No1007S)))),No1114S,No305); ((((((No1206S,(No1202S,No0910S)),No0906S),No0908S),((No306,No304),No0913S)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((((((((No1202S,No0910S),No1206S),No0906S),No306),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No0908S),No0913S),No304),No1114S,No305); (((No0913S,No304),(((((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),(((No1202S,No0910S),No1206S),No0906S)),No0908S),No306)),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),(((No1206S,(No0906S,(No1202S,No0910S))),(No306,(No304,No0913S))),No0908S)),No1114S,No305); (No1114S,((No1103S,((No1007S,(No1208S,No0909S)),No0912S)),(((No304,No0913S),No306),((No1206S,(No0906S,(No1202S,No0910S))),No0908S))),No305); ((No1114S,((No304,No0913S),No306)),(((No1206S,(No0906S,(No1202S,No0910S))),No0908S),(No0912S,(No1103S,(No1007S,(No1208S,No0909S))))),No305); (No1114S,(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No1202S,No0910S)))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (No1114S,(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No1202S,No0910S)))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (No1114S,(((((No304,No0913S),No306),No0908S),(No1206S,(No0906S,(No1202S,No0910S)))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((((No306,No0908S),(No1206S,(No0906S,(No0913S,(No0910S,No1202S))))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No304),No305); (No1114S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),((No306,No304),((No0906S,(No0913S,(No0910S,No1202S))),(No0908S,No1206S)))),No305); (No1114S,((((No1208S,(No0909S,No1007S)),No0912S),No1103S),((No306,No304),(No0908S,(No1206S,(No0906S,(No0913S,(No0910S,No1202S))))))),No305); (((No0908S,(((No0910S,No1202S),No0906S),No1206S)),(No306,(No0913S,(No1114S,No304)))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),No305); ((((No0908S,(((No0910S,No1202S),No0906S),No1206S)),(No306,(No0913S,No304))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No1114S,No305); ((((((No0908S,No1206S),((No0906S,(No0913S,(No1202S,No0910S))),No306)),(No1103S,No0912S)),((No0909S,No1007S),No1208S)),No304),No1114S,No305); (((No0906S,((((No1007S,(No1208S,No0909S)),(No1103S,No0912S)),(No0910S,(No0908S,No1206S))),No1202S)),(No306,(No304,No0913S))),No1114S,No305); ((((((No0906S,No1206S),(No0910S,No1202S)),(No306,(No304,No0913S))),No0908S),((No1208S,(No0909S,No1007S)),(No1103S,No0912S))),No1114S,No305); (((No306,(No304,No0913S)),((No1206S,((No0906S,No0908S),(No1103S,((No1202S,(No1007S,(No1208S,No0909S))),No0910S)))),No0912S)),No1114S,No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(((No1206S,No0908S),(No0906S,(No0910S,No1202S))),(No306,(No304,No0913S)))),No1114S,No305); (((No304,No0913S),((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(No0908S,(No1206S,(No0906S,(No0910S,No1202S))))),No306)),No1114S,No305); ((((No0912S,No1103S),((No0909S,No1208S),No1007S)),((No1206S,(No0906S,(No0910S,No1202S))),(No0908S,(No306,(No0913S,No304))))),No1114S,No305); ((No304,(((No0906S,(((No0910S,No1202S),((No0912S,No1103S),((No0909S,No1208S),No1007S))),(No0908S,No1206S))),No306),No0913S)),No1114S,No305); (((No0912S,No1103S),((No0909S,(No1208S,No1007S)),(((No0906S,No1206S),((No0913S,No1202S),No0910S)),(No0908S,(No306,No304))))),No1114S,No305); ((((No0908S,((No0913S,No304),(No0909S,(No1208S,No1007S)))),(No1206S,(No0906S,(No0910S,No1202S)))),No306),((No1103S,No1114S),No0912S),No305); ((((No1103S,No0912S),(No1007S,(No1208S,No0909S))),((No306,(No0913S,No304)),(((No1206S,No0906S),(No1202S,No0910S)),No0908S))),No1114S,No305); ((((No1103S,No0912S),(No1007S,(No1208S,No0909S))),((No306,(No0913S,No304)),(((No1206S,No0906S),(No1202S,No0910S)),No0908S))),No1114S,No305); ((((No0908S,No1206S),((No1103S,((No1202S,No0910S),No0906S)),(No0912S,(No306,(No0913S,No304))))),(No1007S,(No1208S,No0909S))),No1114S,No305); (((No0908S,((((No1202S,No0910S),No0906S),No1206S),(No306,(No0913S,No304)))),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No1114S,No305); (((No0908S,((((No1202S,No0910S),No0906S),No1206S),(No306,(No0913S,No304)))),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No1114S,No305); ((No304,((No306,((No0908S,No1206S),((No0912S,No1103S),(No1007S,(No1208S,No0909S))))),((No1202S,No0910S),(No0913S,No0906S)))),No1114S,No305); ((No304,(No306,((((No0908S,No1206S),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),(No0906S,(No1202S,No0910S))),No0913S))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No0908S,No1206S),((((No1202S,No0910S),No0906S),No0913S),(No304,No306)))),No1114S,No305); (((No1007S,(No1208S,No0909S)),(((No0906S,(No1202S,No0910S)),((No0912S,No1103S),(No0908S,No1206S))),((No0913S,No304),No306))),No1114S,No305); (((No1007S,(No1208S,No0909S)),((No0912S,No1103S),(((No0913S,No304),No306),((No0906S,(No1202S,No0910S)),(No0908S,No1206S))))),No1114S,No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(No306,((No0906S,(No1206S,((No304,No0913S),No0908S))),(No0910S,No1202S)))),No305); (No1114S,(No304,(((((No0906S,(No0913S,(No1202S,No0910S))),No1206S),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),No0908S),No306)),No305); ((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No0906S,(No1202S,No0910S)),((No0908S,No1206S),(No306,(No0913S,No304))))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No0906S,((No1202S,No0910S),No1206S)),(No0908S,(No306,(No0913S,No304))))),No1114S,No305); ((((No0912S,No1103S),(No0909S,(No1007S,No1208S))),((No306,(No0913S,No304)),((No0910S,(No0906S,No1202S)),(No1206S,No0908S)))),No1114S,No305); ((((No0912S,No1103S),(No0909S,(No1007S,No1208S))),((No306,(No0913S,No304)),(No0908S,((No0906S,(No1202S,No0910S)),No1206S)))),No1114S,No305); ((((No0912S,No1103S),(No0909S,(No1007S,No1208S))),((No306,(No0913S,No304)),(No0908S,((No0906S,(No1202S,No0910S)),No1206S)))),No1114S,No305); ((No1103S,(((No0909S,(No1007S,No1208S)),((No0908S,(No0906S,((No1202S,No0910S),No1206S))),(No306,(No0913S,No304)))),No0912S)),No1114S,No305); ((((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),(No306,(No0913S,No304))),(No1103S,(No0912S,(No1007S,(No1208S,No0909S))))),No1114S,No305); (((((No0906S,(No1202S,No0910S)),(No0908S,No1206S)),(No306,(No0913S,No304))),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No1114S,No305); (No1114S,(((No0912S,No1103S),(No0909S,(No1007S,No1208S))),(((No304,No0913S),No306),(((No0910S,No1202S),(No0908S,No1206S)),No0906S))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(((No304,No0913S),No306),((((No0910S,No1202S),No1206S),No0906S),No0908S))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(((No304,No0913S),No306),((((No0910S,No1202S),No1206S),No0906S),No0908S))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No0913S,(No306,No304)),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),No305); (No1114S,(((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0913S,No304),No306)),(No1007S,(No1208S,No0909S))),(No0912S,No1103S)),No305); (No1114S,((((No0906S,(No0910S,No1202S)),No1206S),(No0908S,((No0913S,No304),No306))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); (No1114S,((No0912S,No1103S),(((No304,(No306,No0913S)),((No0906S,((No0910S,No1202S),No1206S)),No0908S)),(No1007S,(No1208S,No0909S)))),No305); (No1114S,((No1007S,(No1208S,No0909S)),((No0912S,No1103S),(No0908S,((No306,(No0913S,No304)),(No0906S,((No0910S,No1202S),No1206S)))))),No305); (No1114S,((No0912S,No1103S),(((No0908S,(((No0906S,No0910S),No1202S),No1206S)),((No304,No0913S),No306)),(No0909S,(No1007S,No1208S)))),No305); (No1114S,(((No0908S,((No0906S,(No0910S,No1202S)),No1206S)),((No304,No0913S),No306)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); ((No1208S,(No1007S,No0909S)),((((No306,No0913S),(No1114S,No304)),((No0908S,No1206S),((No1202S,No0910S),No0906S))),(No1103S,No0912S)),No305); ((((No306,(No304,No0913S)),(No0908S,((No1007S,(No0909S,No1208S)),(No1206S,((No1202S,No0910S),No0906S))))),(No1103S,No0912S)),No1114S,No305); ((((No1206S,((No1202S,No0910S),No0906S)),((No306,(No304,No0913S)),No0908S)),((No1007S,(No0909S,No1208S)),(No1103S,No0912S))),No1114S,No305); (((No1103S,No0912S),((((No306,(No304,No0913S)),No0908S),(No1206S,((No1202S,No0910S),No0906S))),(No0909S,(No1208S,No1007S)))),No1114S,No305); (((((No306,(No304,No0913S)),No0908S),(No1206S,((No1202S,No0910S),No0906S))),(((No1007S,No1208S),No0909S),(No1103S,No0912S))),No1114S,No305); (((((No306,(No304,No0913S)),No0908S),(No1206S,((No1202S,No0910S),No0906S))),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No1114S,No305); (((((No306,(No304,No0913S)),No0908S),(No1206S,((No1202S,No0910S),No0906S))),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No1114S,No305); (((((No306,(No304,No0913S)),No0908S),(No1206S,((No1202S,No0910S),No0906S))),((No0909S,(No1007S,No1208S)),(No1103S,No0912S))),No1114S,No305); (((((No306,(No304,No0913S)),((No1202S,No0910S),No0906S)),(No0908S,No1206S)),(((No0909S,(No1007S,No1208S)),No0912S),No1103S)),No1114S,No305); (((((No306,(No304,No0913S)),((No1202S,No0910S),No0906S)),(No0908S,No1206S)),(((No0909S,(No1007S,No1208S)),No0912S),No1103S)),No1114S,No305); (((No0908S,((No306,(No0913S,No304)),((No0906S,(No0910S,No1202S)),No1206S))),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No1114S,No305); ((No304,(No306,((((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),(No0908S,No1206S)),((No0906S,(No0910S,No1202S)),No0913S)))),No1114S,No305); (((No1103S,No0912S),(((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No306,No0913S),No304)),(No1208S,(No1007S,No0909S)))),No1114S,No305); (((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No0913S,No304),No306)),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((No1007S,(No0909S,No1208S)),((No0912S,No1103S),(((No0908S,No1206S),(No0906S,(No0910S,No1202S))),((No304,No0913S),No306)))),No1114S,No305); (((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No304,No0913S),No306)),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No1114S,No305); (((No0912S,No1103S),((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No304,No0913S),No306)),(No0909S,(No1208S,No1007S)))),No1114S,No305); (((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),((No304,No0913S),No306)),((No1208S,(No1007S,No0909S)),(No0912S,No1103S))),No1114S,No305); (No1114S,(((((No0906S,(No0910S,No1202S)),(No0908S,((No304,No0913S),No306))),No1206S),(No1007S,(No0909S,No1208S))),(No0912S,No1103S)),No305); (No1114S,((((No304,No0913S),No306),((No0908S,No1206S),(No0906S,(No0910S,No1202S)))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No305); ((((No306,(No0913S,No304)),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No1114S,No305); ((((No306,(No0913S,No304)),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No1114S,No305); ((((No306,(No0913S,No304)),((No0906S,(No0910S,No1202S)),(No0908S,No1206S))),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No1114S,No305); (((No1114S,(No306,(No0913S,No304))),(No0908S,((No0906S,(No0910S,No1202S)),No1206S))),((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),No305); ((((No306,(No0913S,No304)),((No0906S,(No0910S,No1202S)),(No1206S,No0908S))),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No1114S,No305); (((No1103S,No0912S),(((No306,(No0913S,No304)),((No0906S,(No0910S,No1202S)),(No1206S,No0908S))),(No0909S,(No1208S,No1007S)))),No1114S,No305); ((((No306,(No0913S,No304)),((No0906S,(No0910S,No1202S)),(No1206S,No0908S))),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),(((No1206S,No0908S),(No306,(No0913S,(No0906S,(No0910S,No1202S))))),No304)),No1114S,No305); ((((No1103S,((No304,No306),(No0908S,(No1206S,(No0906S,((No0910S,No1202S),No0913S)))))),No0912S),(No1007S,(No0909S,No1208S))),No1114S,No305); ((((((No0913S,No304),(No0906S,(No0910S,No1202S))),(No306,(No0908S,No1206S))),(No1007S,(No0909S,No1208S))),(No0912S,No1103S)),No1114S,No305); (No0912S,(((((No0908S,No1206S),(No0906S,(No0913S,(No0910S,No1202S)))),No304),No306),((No1114S,(No1007S,(No0909S,No1208S))),No1103S)),No305); (No1114S,(((No0908S,((No0906S,No1206S),(No0910S,No1202S))),((No0913S,No304),No306)),((No0912S,No1103S),(No1208S,(No1007S,No0909S)))),No305); (No1114S,((((No0908S,No1206S),((No0913S,No304),No306)),((No0910S,No1202S),No0906S)),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((((No0908S,No1206S),((No0913S,No304),No306)),((No0910S,No1202S),No0906S)),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((((No0908S,No1206S),((No0913S,No304),No306)),((No0910S,No1202S),No0906S)),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),No305); (No1114S,((No0912S,No1103S),((No1208S,(No1007S,No0909S)),((((No0910S,No1202S),No0906S),((No0913S,No304),No306)),(No0908S,No1206S)))),No305); (No0908S,(((No1114S,((No0913S,No304),No306)),(No1103S,((No0909S,(No1208S,No1007S)),No0912S))),(((No0910S,No1202S),No0906S),No1206S)),No305); (No1114S,((((((No0910S,No1202S),No0906S),No1206S),No0908S),((No0913S,No304),No306)),((No1103S,No0912S),(No0909S,(No1208S,No1007S)))),No305); (No1114S,((((No0906S,((No0909S,(No1208S,No1007S)),No0908S)),(No1202S,(No1206S,No0910S))),((No0913S,No304),No306)),(No1103S,No0912S)),No305); (No1114S,(No0912S,(((No0909S,(No1208S,No1007S)),((((No0906S,(No0910S,No1202S)),No1206S),No0908S),((No0913S,No304),No306))),No1103S)),No305); ((((No1007S,(No1208S,No0909S)),No0912S),No1103S),((No306,((No1114S,No304),No0913S)),((No1206S,(No0906S,(No1202S,No0910S))),No0908S)),No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(No0908S,((No1206S,(No306,(No304,No0913S))),(No0906S,(No1202S,No0910S))))),No1114S,No305); (((No0909S,(No1007S,No1208S)),((No0912S,No1103S),(((No0908S,No1206S),(No306,(No304,No0913S))),(No0906S,(No1202S,No0910S))))),No1114S,No305); ((((No306,No0913S),(((No1206S,No0906S),(No1202S,No0910S)),(No0908S,((No0909S,(No1007S,No1208S)),(No0912S,No1103S))))),No304),No1114S,No305); ((((No1103S,No0912S),((No0909S,No1007S),No1208S)),((No306,(No304,No0913S)),(No0908S,(No1206S,(No0906S,(No1202S,No0910S)))))),No1114S,No305); ((((No1103S,No0912S),(No0909S,(No1007S,No1208S))),(((No0910S,(No306,(No304,No0913S))),(No0906S,(No0908S,No1206S))),No1202S)),No1114S,No305); (((No1103S,No0912S),((No0909S,(No1007S,No1208S)),(((((No0910S,No1202S),No0906S),No1206S),No0908S),(No306,(No304,No0913S))))),No1114S,No305); (((No1114S,(No306,(No304,No0913S))),(((No0910S,No1202S),No0906S),(No0908S,No1206S))),((No1103S,No0912S),(No1208S,(No0909S,No1007S))),No305); (((((No0910S,No1202S),No0906S),(No0908S,(No1206S,(No306,(No304,No0913S))))),((No1103S,No0912S),(No0909S,(No1007S,No1208S)))),No1114S,No305); ((((((No1206S,((No0910S,No1202S),No0906S)),No0908S),(No304,No0913S)),No306),((No1103S,No0912S),(No0909S,(No1007S,No1208S)))),No1114S,No305); ((((((No0913S,No304),No306),(No1206S,(No0906S,(No1202S,No0910S)))),No0908S),((No1103S,No0912S),(No1007S,(No0909S,No1208S)))),No1114S,No305); (((((No1206S,No0908S),((No0913S,No304),No306)),((No1202S,No0910S),No0906S)),((No1103S,No0912S),((No1007S,No1208S),No0909S))),No1114S,No305); (((No1007S,No1208S),(No0909S,((No0910S,((No1202S,((No0913S,No304),No306)),(No0906S,(No1206S,No0908S)))),(No1103S,No0912S)))),No1114S,No305); (No1114S,((((No1206S,No0908S),((No0906S,(No0913S,(No0910S,No1202S))),No304)),No306),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No305); (No1114S,(No1103S,(No0912S,(((No0906S,(No0913S,(No0910S,No1202S))),(No304,No306)),((No1206S,No0908S),(No0909S,(No1208S,No1007S)))))),No305); (No1114S,((((No0913S,No304),No306),((No1206S,No0908S),(No0906S,(No0910S,No1202S)))),((No1103S,No0912S),(No1007S,(No0909S,No1208S)))),No305); (((((((No1206S,No0906S),(No0910S,No1202S)),No1103S),(((No0913S,No304),No306),No0908S)),No0912S),(No0909S,(No1208S,No1007S))),No1114S,No305); ((((((No1206S,No0906S),(No0910S,No1202S)),((No0913S,No304),No306)),No0908S),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No1114S,No305); (((((No0908S,No1206S),(No0906S,(No0910S,No1202S))),((No0913S,No304),No306)),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),No1114S,No305); (((((No0908S,No1206S),(No0906S,(No0910S,No1202S))),((No0913S,No304),No306)),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),No1114S,No305); ((((No1103S,No0912S),(No1007S,(No0909S,No1208S))),(((No0908S,No1206S),(No304,No306)),(No0906S,((No1202S,No0910S),No0913S)))),No1114S,No305); ((((No304,No306),((No1103S,No0912S),((No1208S,(No1114S,No1007S)),No0909S))),(No0906S,((No1202S,No0910S),No0913S))),(No0908S,No1206S),No305); ((No304,((No0908S,((((No1208S,No1007S),No0909S),No0912S),No1103S)),((No1206S,(No0906S,(No1202S,No0910S))),(No306,No0913S)))),No1114S,No305); (((No1007S,(No0909S,No1208S)),(No1103S,(((No0908S,((No0906S,(No1202S,No0910S)),No1206S)),(No306,(No304,No0913S))),No0912S))),No1114S,No305); ((((No1206S,No0908S),(No306,(No304,((No0906S,(No1202S,No0910S)),No0913S)))),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),No1114S,No305); ((((((No0906S,(No1202S,No0910S)),No1206S),No0908S),(No306,(No304,No0913S))),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),No1114S,No305); (((No0912S,No1103S),((No1208S,(No1007S,No0909S)),((((No0906S,(No1202S,No0910S)),No1206S),No0908S),(No306,(No304,No0913S))))),No1114S,No305); ((((((No0906S,(No1202S,No0910S)),No1206S),No0908S),((No0913S,No304),No306)),(No0912S,No1103S)),(No1114S,(No1007S,(No0909S,No1208S))),No305); (No1114S,((((No0909S,No1208S),No1007S),(No1103S,No0912S)),((((No0906S,(No1202S,No0910S)),No1206S),No0908S),((No0913S,No304),No306))),No305); (No1114S,((No0908S,((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),(((No0906S,(No1202S,No0910S)),No1206S),((No0913S,No304),No306))),No305); ((No304,(((No0908S,((No0906S,(No1202S,No0910S)),No1206S)),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),(No306,No0913S))),No1114S,No305); (((((No0909S,(No1007S,No1208S)),No0912S),No1103S),((No0908S,(No306,(No304,No0913S))),((No0906S,(No1202S,No0910S)),No1206S))),No1114S,No305); (((((No1007S,(No1208S,No0909S)),No0912S),No1103S),((No0908S,(No0913S,(No306,No304))),((No0906S,(No1202S,No0910S)),No1206S))),No1114S,No305); ((No1103S,(((No304,(No306,(No0908S,((No0906S,(No0913S,(No1202S,No0910S))),No1206S)))),(No1007S,(No1208S,No0909S))),No0912S)),No1114S,No305); ((((No1103S,((No1007S,(No1208S,No0909S)),No0912S)),((No0906S,(No0913S,(No1202S,No0910S))),(No1206S,No0908S))),(No304,No306)),No1114S,No305); (((((((No1007S,No1208S),No0909S),No0912S),No1103S),(((No0908S,((No0910S,(No0913S,No1202S)),No0906S)),No306),No1206S)),No1114S),No304,No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S))))),No305); (No1114S,(((No0913S,No304),No306),((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(No0908S,No1206S)),(No0910S,(No1202S,No0906S)))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),((((No0913S,No304),No306),(No0908S,No1206S)),(No0906S,(No0910S,No1202S)))),No305); (No1114S,((((No0908S,No1206S),(((No0910S,No1202S),No0913S),No0906S)),(((No1103S,No0912S),(No1007S,(No1208S,No0909S))),No306)),No304),No305); (No1114S,((No1103S,No0912S),((No0908S,((No1206S,((No0910S,No1202S),No0906S)),((No304,No0913S),No306))),((No1208S,No0909S),No1007S))),No305); (No1114S,((No1103S,No0912S),(((((No0910S,No1202S),No0906S),(No0908S,No1206S)),((No304,No0913S),No306)),((No1208S,No0909S),No1007S))),No305); (No1114S,(((((No0906S,No1206S),(No1202S,No0910S)),((No304,No0913S),No306)),No0908S),(((No0909S,No1208S),No1007S),(No1103S,No0912S))),No305); (No1114S,((No1103S,No0912S),((((No1206S,((No1202S,No0910S),No0906S)),((No304,No0913S),No306)),No0908S),(No0909S,(No1208S,No1007S)))),No305); (No1114S,((((No304,No0913S),No306),(No0908S,(No1206S,((No1202S,No0910S),No0906S)))),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No305); (No1114S,((((No0908S,No1206S),((((No0913S,No0906S),No0910S),No1202S),No306)),((No0909S,(No1208S,No1007S)),(No1103S,No0912S))),No304),No305); (No1114S,((No1103S,No0912S),((((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S)))),(No0909S,(No1208S,No1007S)))),No305); (No1114S,((((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S)))),((No1208S,(No1007S,No0909S)),(No1103S,No0912S))),No305); (No1114S,((No0909S,(No1208S,No1007S)),((No1103S,No0912S),(((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S)))))),No305); (No1114S,((No1103S,No0912S),(((((No0913S,No304),((No0910S,No1202S),No0906S)),(No0908S,No1206S)),No306),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((No0908S,((No1206S,((No0910S,No1202S),No0906S)),(No306,(No304,No0913S)))),(No1103S,((No0909S,(No1007S,No1208S)),No0912S))),No305); (No1114S,(((((No0910S,No1202S),No0906S),(No0908S,No1206S)),(No306,(No304,No0913S))),(No1103S,((No1007S,(No1208S,No0909S)),No0912S))),No305); (((((No0910S,No1202S),No0906S),(No0908S,No1206S)),(No306,(No1114S,(No304,No0913S)))),(No1103S,((No0909S,(No1007S,No1208S)),No0912S)),No305); (((No1007S,(No1208S,No0909S)),((No0912S,No1103S),(((No306,No304),No0913S),((No1206S,((No0906S,No0910S),No1202S)),No0908S)))),No1114S,No305); (((No1007S,(No1208S,No0909S)),(No0912S,(No1103S,(No0906S,((No1202S,((No306,(No304,No0913S)),No0910S)),(No1206S,No0908S)))))),No1114S,No305); (((((((No1206S,No0908S),No0906S),(No306,(No304,No0913S))),(No0910S,No1202S)),(No1007S,(No1208S,No0909S))),(No1103S,No0912S)),No1114S,No305); (((No1103S,No0912S),(No0909S,(No1007S,No1208S))),((No0906S,(No1206S,(No0910S,No1202S))),(No0908S,(No1114S,(No306,(No304,No0913S))))),No305); ((No0909S,((No1114S,No1007S),No1208S)),(No0912S,(No1103S,(No0908S,((No306,(No304,No0913S)),(No0906S,(No1206S,(No0910S,No1202S))))))),No305); (No1114S,((No0912S,No1103S),((((No306,(No304,No0913S)),(No1206S,((No0910S,No1202S),No0906S))),No0908S),(No0909S,(No1007S,No1208S)))),No305); (No1114S,(((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(((((No1202S,No0906S),(No0910S,No0913S)),(No304,No0908S)),No1206S),No306)),No305); (No1114S,((((No1208S,No1007S),No0909S),(No1103S,No0912S)),((No306,(No304,No0913S)),(((No0910S,No1202S),No0906S),(No0908S,No1206S)))),No305); (No1114S,((((No1208S,No1007S),No0909S),(No1103S,No0912S)),((No306,(No304,No0913S)),(No0908S,(No1206S,((No0910S,No1202S),No0906S))))),No305); (No1114S,((((No1208S,No1007S),No0909S),(No1103S,No0912S)),(((No306,No304),(No0913S,(No0910S,No1202S))),((No0908S,No1206S),No0906S))),No305); (No1114S,(((No306,No304),No0913S),(((No0912S,(No0908S,(No1007S,(No0909S,No1208S)))),(No1206S,(No0906S,(No0910S,No1202S)))),No1103S)),No305); (No1114S,((((No0906S,((No0912S,No1103S),(No1208S,(No1007S,No0909S)))),(No0908S,No1206S)),(No0910S,No1202S)),((No0913S,No304),No306)),No305); (((No0912S,No1103S),((No1208S,No1007S),No0909S)),((((No0913S,No304),No306),No1114S),((No0908S,No1206S),(No0906S,(No0910S,No1202S)))),No305); (No1114S,((((No0906S,((No0913S,No304),(No0910S,No1202S))),(No0908S,No1206S)),((No0912S,No1103S),((No1208S,No1007S),No0909S))),No306),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S))))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S))))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(((No0913S,No304),No306),(No0908S,(No1206S,((No0910S,No1202S),No0906S))))),No305); (No1114S,((No1103S,No0912S),((No0909S,(No1208S,No1007S)),(No0906S,(((No306,(No0913S,No304)),No0908S),((No1202S,No0910S),No1206S))))),No305); (No1114S,((No1007S,(No0909S,No1208S)),((((No306,(No0913S,No304)),No0908S),(No0906S,(No1206S,(No1202S,No0910S)))),(No1103S,No0912S))),No305); (No1114S,((No1007S,(No0909S,No1208S)),((((No304,(No0906S,(No0913S,(No1202S,No0910S)))),(No0908S,No1206S)),No306),(No1103S,No0912S))),No305); (No1114S,((No1208S,(No1007S,No0909S)),((No1103S,No0912S),(No1206S,((No0906S,(No1202S,No0910S)),(((No304,No0913S),No306),No0908S))))),No305); (No1114S,((No1103S,No0912S),((No0909S,(No1208S,No1007S)),(((No304,No0913S),No306),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))))),No305); (No1114S,((No1103S,No0912S),((No0909S,(No1208S,No1007S)),(((No304,No0913S),No306),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))))),No305); (No1114S,((((No304,No0913S),No306),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))),((No1103S,No0912S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((((No304,No0913S),No306),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))),((No1103S,No0912S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((((No304,No0913S),No306),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))),((No1103S,No0912S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,(((No0906S,((No304,No0913S),No306)),((No1202S,No0910S),(No0908S,No1206S))),((No1103S,No0912S),(No1208S,(No1007S,No0909S)))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No0909S,No1208S))),((No1206S,(No0906S,(No1202S,No0910S))),((No306,(No0913S,No304)),No0908S))),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No0909S,No1208S))),((No1206S,(No0906S,(No1202S,No0910S))),((No306,(No0913S,No304)),No0908S))),No305); (No1114S,(((No1103S,No0912S),(No0909S,(No1208S,No1007S))),(No0908S,((No1206S,(No0906S,(No1202S,No0910S))),(No306,(No0913S,No304))))),No305); (No1114S,((No1007S,(No0909S,No1208S)),(((No306,(No0913S,No304)),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))),(No1103S,No0912S))),No305); (No1114S,((((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),(No0913S,No304)),No306),((No1007S,(No0909S,No1208S)),(No1103S,No0912S))),No305); (No1114S,(((((No304,No0913S),No306),(No0908S,No1206S)),(No0906S,(No1202S,No0910S))),((No1007S,(No0909S,No1208S)),(No1103S,No0912S))),No305); (No1114S,(No304,((((No0906S,(No0913S,(No1202S,No0910S))),(No0908S,No1206S)),No306),((No1208S,(No1007S,No0909S)),(No1103S,No0912S)))),No305); (No1114S,(No304,((((No0906S,No1206S),(No0910S,No1202S)),((((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),No0908S),No306)),No0913S)),No305); (No1114S,(((No1103S,No0912S),(No1007S,(No0909S,No1208S))),(No0908S,(((No0913S,No304),No306),(No1206S,((No0910S,No1202S),No0906S))))),No305); (No1114S,(((No1103S,No0912S),(No0909S,(No1208S,No1007S))),((((No0910S,No1202S),No0906S),(No0908S,No1206S)),((No0913S,No304),No306))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No306,(No0913S,No304)),(No0908S,(No1206S,(No0906S,(No0910S,No1202S)))))),No305); (No1114S,((No0912S,No1103S),((No1208S,(No0909S,No1007S)),((No306,(No0913S,No304)),(No0908S,(No1206S,(No0906S,(No0910S,No1202S))))))),No305); (No1114S,((No306,(((No0913S,(No0906S,(No0910S,No1202S))),No304),(No0908S,No1206S))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); ((No0908S,((No306,(No0913S,(No1114S,No304))),(No1206S,((No0910S,No1202S),No0906S)))),((No0912S,No1103S),(No0909S,(No1007S,No1208S))),No305); (((((No306,(No0913S,No304)),(No0908S,((No0910S,No1202S),No0906S))),No1206S),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),No1114S,No305); (((No0908S,((No1206S,((No0910S,No1202S),No0906S)),(No306,(No0913S,No304)))),((No0912S,No1103S),(No1208S,(No0909S,No1007S)))),No1114S,No305); ((((No1202S,(((No0908S,No1206S),No0906S),No0910S)),(No306,(No0913S,No304))),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),No1114S,No305); (((((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No306,(No0913S,No304))),(No0912S,(No1103S,(No0909S,(No1007S,No1208S))))),No1114S,No305); (((((No1206S,(No0906S,(No0913S,(No0910S,No1202S)))),No0908S),(No304,No306)),((No1208S,(No0909S,No1007S)),(No0912S,No1103S))),No1114S,No305); ((((No1103S,(No1007S,(No1208S,No0909S))),No0912S),((((No0910S,No1202S),No0906S),(No0908S,No1206S)),(No0913S,(No304,No306)))),No1114S,No305); ((((No1208S,No0909S),No1007S),(((No306,(No304,No0913S)),((No1206S,(No0906S,(No0910S,No1202S))),No0908S)),(No1103S,No0912S))),No1114S,No305); ((((No306,(No304,No0913S)),((No1206S,(No0906S,(No0910S,No1202S))),No0908S)),((No1103S,No0912S),(No1208S,(No0909S,No1007S)))),No1114S,No305); (((No306,(((No0909S,(No1007S,No1208S)),(No304,No0913S)),((No0906S,(No0910S,No1202S)),(No0908S,No1206S)))),(No1103S,No0912S)),No1114S,No305); (((No306,(No304,No0913S)),(((No0908S,No1206S),(No0906S,(No0910S,No1202S))),((No1208S,(No0909S,No1007S)),(No1103S,No0912S)))),No1114S,No305); (((((No1007S,(No1208S,No0909S)),No0912S),No1103S),((No306,(No304,No0913S)),((No0908S,No1206S),(No0906S,(No0910S,No1202S))))),No1114S,No305); (((No306,((No1114S,No304),No0913S)),(No0908S,(No1206S,(No1202S,(No0906S,No0910S))))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),No305); (((((No306,(No304,No0913S)),No0908S),(No1206S,(No0906S,(No0910S,No1202S)))),((No0912S,No1103S),((No0909S,No1208S),No1007S))),No1114S,No305); ((((No0909S,No1208S),No1007S),(((No0908S,No1206S),(((No0906S,(No0910S,No1202S)),No0913S),(No306,No304))),(No0912S,No1103S))),No1114S,No305); ((((No0909S,No1208S),No1007S),((No0912S,No1103S),(No0908S,((No306,(No304,No0913S)),(No1206S,((No0910S,No1202S),No0906S)))))),No1114S,No305); (((No0912S,No1103S),((No0906S,((((No304,No0913S),No306),(No0908S,No1206S)),(No0910S,No1202S))),((No0909S,No1208S),No1007S))),No1114S,No305); ((((No1208S,No0909S),No1007S),((((No306,(No304,No0913S)),(No0906S,(No0910S,(No1202S,No1206S)))),No0908S),(No1103S,No0912S))),No1114S,No305); (((((No306,(No304,No0913S)),((No0906S,(No0910S,No1202S)),No1206S)),No0908S),((No1208S,(No0909S,No1007S)),(No0912S,No1103S))),No1114S,No305); ((((No306,(No304,No0913S)),((No0906S,(No0908S,No1206S)),((No1007S,(No1208S,No0909S)),(No0910S,No1202S)))),(No0912S,No1103S)),No1114S,No305); (((No1206S,(No0906S,(No0910S,No1202S))),(No0908S,((No0912S,No1103S),(No1007S,(No1208S,No0909S))))),(No306,((No1114S,No304),No0913S)),No305); (((No0912S,No1103S),(No1208S,(No0909S,No1007S))),(((No306,((No1114S,No304),No0913S)),(No1206S,(No0906S,(No0910S,No1202S)))),No0908S),No305); ((No0912S,No1103S),((No0909S,(No1007S,No1208S)),((((No306,(No304,No0913S)),No1114S),(No1202S,(No0906S,(No1206S,No0910S)))),No0908S)),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((((No0910S,No1202S),No0906S),(No306,(No304,No0913S))),(No1206S,No0908S))),No305); (No1114S,(((No0912S,No1103S),(No1208S,(No0909S,No1007S))),((No0908S,((No0910S,No1202S),(No1206S,No0906S))),(No306,(No304,No0913S)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((No0908S,((No1206S,No0906S),(No0910S,No1202S))),(No306,(No304,No0913S)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(((No1206S,(No0906S,(No0910S,No1202S))),No0908S),(No306,(No304,No0913S)))),No305); (No1114S,(((No0912S,No1103S),(No0909S,(No1208S,No1007S))),((No1206S,((No0910S,No1202S),No0906S)),((No0908S,(No304,No0913S)),No306))),No305); ((((No1007S,(No0909S,No1208S)),No0912S),No1103S),(No1114S,((No306,(No0913S,No304)),(No0908S,(No1206S,((No0910S,No1202S),No0906S))))),No305); ((No0912S,No1103S),(No1114S,((No306,(No0913S,No304)),(((No0909S,(No1208S,No1007S)),No0908S),(No1206S,((No0910S,No1202S),No0906S))))),No305); (No1114S,(((No1007S,(No0909S,No1208S)),((No0908S,No1206S),((No306,(No0913S,No304)),((No0910S,No1202S),No0906S)))),(No0912S,No1103S)),No305); (No1114S,((No0906S,((No306,(No0913S,No304)),((No0908S,No1206S),(No0910S,No1202S)))),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No305); (((No0908S,(No0906S,((No0910S,No1202S),No1206S))),(No306,(No0913S,No304))),(((No0912S,No1103S),(No0909S,(No1208S,No1007S))),No1114S),No305); (No1114S,(((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),(No306,(No0913S,No304))),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),No305); (No1114S,((No0913S,((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),(No0908S,(No1206S,(No0906S,(No0910S,No1202S))))),No306)),No304),No305); (((No1007S,(No0909S,No1208S)),(((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),(No306,(No304,No0913S))),(No0912S,No1103S))),No1114S,No305); (((No0912S,No1103S),(((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),(No306,(No304,No0913S))),(No0909S,(No1208S,No1007S)))),No1114S,No305); (No0908S,((No0912S,(No1103S,(No0909S,(No1007S,No1208S)))),((No1114S,(No306,(No304,No0913S))),(No1206S,(No0906S,(No0910S,No1202S))))),No305); (((((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),No0908S),(No1114S,(No306,(No304,No0913S)))),(No1206S,(No0906S,(No0910S,No1202S))),No305); (No1114S,(((No1206S,(No0906S,(No0910S,No1202S))),((No306,(No304,No0913S)),No0908S)),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); (No1114S,(((No1206S,(No0906S,(No0910S,No1202S))),((No306,(No304,No0913S)),No0908S)),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); (No1114S,(((No1206S,(No0906S,(No0910S,No1202S))),((No306,(No304,No0913S)),No0908S)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (No1114S,(((No1206S,(No0906S,(No0910S,No1202S))),((No306,(No304,No0913S)),No0908S)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (No1114S,(((No1206S,(No0906S,(No0910S,No1202S))),((No306,(No304,No0913S)),No0908S)),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); ((((No0908S,No1206S),(No0906S,(No0910S,No1202S))),(No1114S,(No306,(No304,No0913S)))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),No305); (No1114S,((No306,(No304,No0913S)),((No0908S,No1206S),(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(No0906S,(No0910S,No1202S))))),No305); (((No0912S,No1103S),(No0909S,(No1007S,No1208S))),((No0906S,(No0910S,No1202S)),(((No0913S,(No304,No306)),No1114S),(No0908S,No1206S))),No305); (No1114S,(No306,(((No1208S,(No0909S,No1007S)),(No0912S,No1103S)),(((No0906S,(No0910S,No1202S)),(No0908S,No1206S)),(No304,No0913S)))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No306,(No304,No0913S)),(((No0906S,(No0910S,No1202S)),No1206S),No0908S))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No306,(No304,No0913S)),(((No0906S,(No0910S,No1202S)),No1206S),No0908S))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No306,(No304,No0913S)),(((No0906S,(No0910S,No1202S)),No1206S),No0908S))),No305); ((((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),((((No0906S,No0910S),No1206S),No1202S),((No306,No1114S),(No0913S,No304)))),No0908S,No305); (((((No0910S,No1202S),No0906S),No1206S),(No0908S,(No1114S,((No0913S,No304),No306)))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),No305); (No1114S,(((No0913S,No306),((((No0908S,No1206S),No0906S),((No1208S,(No0909S,No1007S)),(No0912S,No1103S))),(No0910S,No1202S))),No304),No305); (No1114S,((No0912S,No1103S),((No0909S,(No1007S,No1208S)),(((No304,No1206S),(No0906S,(No0913S,(No1202S,No0910S)))),(No306,No0908S)))),No305); (No1114S,(((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(((No1206S,(No0906S,(No0910S,No1202S))),No0908S),((No0913S,No304),No306))),No305); (No1114S,((No0912S,No1103S),((No1007S,(No1208S,No0909S)),((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),((No0913S,No304),No306)))),No305); ((((No0912S,(No1103S,(No1007S,(No0909S,No1208S)))),(((No0913S,(No1202S,No0910S)),No0906S),(No0908S,No1206S))),(No304,No306)),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No306,((No304,No0913S),((No1202S,No0910S),No0906S))),(No0908S,No1206S))),No1114S,No305); (((No0912S,No1103S),(((No306,(No304,No0913S)),(No0908S,(((No1202S,No0910S),No0906S),No1206S))),(No1007S,(No0909S,No1208S)))),No1114S,No305); (((((((No1202S,No0910S),No0906S),No1206S),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),(No306,(No304,No0913S))),No0908S),No1114S,No305); ((((No306,(No0908S,No1206S)),(((No0913S,(No1202S,No0910S)),No0906S),No304)),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No1114S,No305); (((((((No1202S,No0910S),No0906S),No1206S),No0908S),(No306,(No304,No0913S))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))),No1114S,No305); (((((No1114S,(No1007S,No1208S)),No0909S),((No0906S,(No1206S,(No1202S,No0910S))),No0908S)),(No1103S,No0912S)),(No306,(No304,No0913S)),No305); ((((No1103S,No0912S),((No1007S,No1208S),No0909S)),((((No306,(No304,No0913S)),No1206S),No0906S),((No1202S,No0910S),No0908S))),No1114S,No305); ((((No306,(No304,No0913S)),No1114S),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))),((No0912S,No1103S),(No1007S,(No1208S,No0909S))),No305); ((((No0913S,(No306,No304)),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))),((No0912S,No1103S),(No1208S,(No0909S,No1007S)))),No1114S,No305); ((((No0912S,(No1208S,(No1007S,No0909S))),No1103S),((((No0913S,No304),No306),(((No0910S,No1202S),No0906S),No1206S)),No0908S)),No1114S,No305); ((((No0912S,(No1208S,(No1007S,No0909S))),No1103S),((((No0913S,No304),No306),(((No0910S,No1202S),No0906S),No1206S)),No0908S)),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),(((No0908S,No304),((((No0913S,No0910S),No1202S),No0906S),No1206S)),No306)),No1114S,No305); ((((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),((No0908S,(((No1202S,No0910S),No0906S),No1206S)),((No0913S,No304),No306))),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),((No0908S,(((No1202S,No0910S),No0906S),No1206S)),((No0913S,No304),No306))),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No1103S,No0912S)),((No0908S,(((No1202S,No0910S),No0906S),No1206S)),((No0913S,No304),No306))),No1114S,No305); ((((No1007S,(No0909S,No1208S)),(No1103S,No0912S)),((No0908S,(((No1202S,No0910S),No0906S),No1206S)),((No0913S,No304),No306))),No1114S,No305); (((No1103S,No0912S),((((No0908S,No1206S),((No0913S,No304),No306)),((No1202S,No0910S),No0906S)),(No1007S,(No0909S,No1208S)))),No1114S,No305); (((No1103S,No0912S),(((No0908S,((No0913S,No304),No306)),((No1202S,No0910S),(No1206S,No0906S))),(No0909S,(No1208S,No1007S)))),No1114S,No305); ((((No0913S,No304),No306),(((No1206S,(No0906S,(No1202S,No0910S))),No0908S),((No0909S,(No1208S,No1007S)),(No1103S,No0912S)))),No1114S,No305); (No1114S,(((No0906S,(No0913S,(No1202S,No0910S))),((No0908S,No1206S),(No304,No306))),(No1103S,((No1007S,(No0909S,No1208S)),No0912S))),No305); (No1114S,(((No1206S,(No0906S,(No0913S,(No1202S,No0910S)))),(No0908S,(No304,No306))),(No1103S,((No0909S,(No1208S,No1007S)),No0912S))),No305); (No1114S,(((No1206S,(No0906S,(No0913S,(No1202S,No0910S)))),(No0908S,(No304,No306))),(No1103S,((No0909S,(No1208S,No1007S)),No0912S))),No305); (No1114S,((((No0908S,No1206S),(No0910S,(No1202S,No0906S))),((No0913S,No304),No306)),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (No1114S,((((No0906S,No0910S),(No1206S,No1202S)),((No0908S,(No0913S,No304)),No306)),((No0909S,(No1208S,No1007S)),(No0912S,No1103S))),No305); (No1114S,(((No1206S,(No0906S,(No0910S,No1202S))),(No0908S,((No0913S,No304),No306))),(((No1007S,(No0909S,No1208S)),No0912S),No1103S)),No305); (No1114S,(((((No1206S,No0908S),((No0906S,(No0910S,No1202S)),No306)),(No1007S,(No0909S,No1208S))),(No0913S,No304)),(No0912S,No1103S)),No305); (No1114S,((No306,(No0913S,No304)),(((((No1007S,(No0909S,No1208S)),No0912S),No1103S),No0908S),((No0906S,(No0910S,No1202S)),No1206S))),No305); ((((No0912S,(No0909S,(No1208S,No1007S))),No1103S),((No0908S,(No306,(No0913S,No304))),((No0906S,(No0910S,No1202S)),No1206S))),No1114S,No305); ((No0912S,No1103S),((No0909S,(No1208S,No1007S)),((No1114S,(No306,(No0913S,No304))),(((No0906S,(No0910S,No1202S)),No1206S),No0908S))),No305); (No1114S,(((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),((No0906S,(No1202S,No0910S)),((No306,(No304,No0913S)),(No1206S,No0908S)))),No305); (No1114S,((No1007S,(No1208S,No0909S)),((No0912S,No1103S),((No306,(No304,No0913S)),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))))),No305); (No1114S,((((No0906S,(No1202S,No0910S)),(No306,(No304,No0913S))),(No0908S,No1206S)),(((No0909S,No1007S),No1208S),(No0912S,No1103S))),No305); (No1114S,((((No0906S,(No1202S,No0910S)),(No306,(No304,No0913S))),(No0908S,No1206S)),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); (No1114S,((((No1206S,(No0906S,(No1202S,No0910S))),(No306,(No304,No0913S))),No0908S),((No0909S,(No1007S,No1208S)),(No0912S,No1103S))),No305); (No1114S,((((No1206S,(No0906S,(No1202S,No0910S))),(No306,(No304,No0913S))),No0908S),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); (No1114S,((No0912S,No1103S),(((No306,(No304,No0913S)),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))),(No1007S,(No1208S,No0909S)))),No305); (No1114S,(((No306,(No304,No0913S)),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))),((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),No305); (No1114S,((No0912S,No1103S),(((No304,No0913S),((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),No306)),(No0909S,(No1007S,No1208S)))),No305); (No1114S,((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(No306,(No0908S,(No1206S,(No0906S,(No0913S,(No1202S,No0910S))))))),No304),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No306,(No304,No0913S)),((No1206S,No0908S),(No0906S,(No1202S,No0910S))))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),((No306,(No304,No0913S)),(No0908S,((No0906S,(No1202S,No0910S)),No1206S)))),No305); (((No0908S,((No1007S,(No1208S,No0909S)),(No0912S,No1103S))),((No1114S,(No306,(No304,No0913S))),(No0906S,(No1202S,No0910S)))),No1206S,No305); (No1114S,((No306,(No304,No0913S)),(No1206S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(No0908S,(No0906S,(No1202S,No0910S)))))),No305); (No1114S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),((No306,(No304,No0913S)),((No0908S,No1206S),(No0906S,(No1202S,No0910S))))),No305); (No1114S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(No306,((No304,No0913S),(No0908S,(No1206S,(No0906S,(No1202S,No0910S))))))),No305); (No1114S,(((No1208S,(No0909S,No1007S)),(No0912S,No1103S)),(((No1206S,No0908S),No0906S),((No1202S,No0910S),(No306,(No0913S,No304))))),No305); (No1114S,(((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(No1206S,((No0906S,(No1202S,No0910S)),(No0908S,(No306,(No0913S,No304)))))),No305); (No1114S,((No0912S,No1103S),((No0908S,No1206S),((No0906S,(No1202S,No0910S)),((No0909S,(No1007S,No1208S)),(No306,(No0913S,No304)))))),No305); (No1114S,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),(((No1206S,(No0906S,(No1202S,No0910S))),No0908S),(No306,(No0913S,No304)))),No305); (((No1103S,No0912S),(No1007S,(No0909S,No1208S))),(((No0908S,No1206S),((No0910S,No1202S),No0906S)),(No1114S,(No306,(No304,No0913S)))),No305); ((((No1103S,No0912S),(No1007S,(No0909S,No1208S))),((No0908S,(No1206S,((No0910S,No1202S),No0906S))),(No306,(No304,No0913S)))),No1114S,No305); (((No0909S,(No1208S,No1007S)),((((No0908S,No1206S),(No0906S,(No306,(No304,No0913S)))),(No0910S,No1202S)),(No0912S,No1103S))),No1114S,No305); ((((No306,(No304,No0913S)),((No1206S,(No0906S,(No0910S,No1202S))),No0908S)),(((No1007S,(No0909S,No1208S)),No0912S),No1103S)),No1114S,No305); (((((No306,(No304,No0913S)),((No1206S,(No0906S,(No0910S,No1202S))),No0908S)),(No0909S,(No1208S,No1007S))),(No0912S,No1103S)),No1114S,No305); ((((No0913S,No306),(((No0906S,((No0910S,No1202S),No1206S)),No0908S),((No1007S,(No0909S,No1208S)),(No0912S,No1103S)))),No304),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No1206S,((No304,No0913S),No306)),(No0906S,((No0910S,No1202S),No0908S)))),No1114S,No305); ((((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),((No0908S,((No304,No0913S),No306)),(((No0910S,No1202S),No0906S),No1206S))),No1114S,No305); ((((((No1007S,(No0909S,No1208S)),No0912S),No1103S),No0906S),((No0908S,No1206S),(No0910S,No1202S))),(No1114S,((No304,No0913S),No306)),No305); (((No0909S,(No1208S,No1007S)),((No0912S,No1103S),(((No0908S,No1206S),(No0906S,(No0910S,No1202S))),((No304,No0913S),No306)))),No1114S,No305); (((No1103S,(No0912S,(No1007S,(No1208S,No0909S)))),((No1206S,(No0906S,(No0910S,No1202S))),(((No304,No0913S),No306),No0908S))),No1114S,No305); (((No1103S,(No0912S,(No1007S,(No1208S,No0909S)))),(No0906S,(((No0908S,No1206S),((No0910S,No1202S),(No304,No0913S))),No306))),No1114S,No305); (((No0909S,(No1007S,No1208S)),(No1103S,No0912S)),((No0908S,(No1206S,(No0906S,(No0910S,No1202S)))),(No1114S,(No306,(No304,No0913S)))),No305); (No1114S,(((No306,(No304,No0913S)),(No0908S,(No1206S,(No0906S,(No0910S,No1202S))))),((No1103S,No0912S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,(((No306,(No304,No0913S)),((No1206S,No0908S),(No0906S,(No1202S,No0910S)))),((No1103S,No0912S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,((No1007S,(No0909S,No1208S)),(((No0906S,((No1202S,No0910S),No1206S)),((No306,(No304,No0913S)),No0908S)),(No1103S,No0912S))),No305); (No1114S,(No304,((No0908S,(No1206S,((No0906S,(No0913S,(No1202S,No0910S))),((No1007S,(No0909S,No1208S)),(No0912S,No1103S))))),No306)),No305); (((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),(No0908S,(((No306,No304),No1114S),(No1206S,(No0906S,(No0913S,(No1202S,No0910S)))))),No305); ((No0908S,(((No1007S,(No0909S,No1208S)),(No0912S,No1103S)),((No0906S,(No1202S,No0910S)),No1206S))),((No306,(No0913S,No304)),No1114S),No305); (((No0912S,No1103S),((((No0906S,(No1202S,No0910S)),No1206S),((No306,(No0913S,No304)),No0908S)),(No1208S,(No1007S,No0909S)))),No1114S,No305); (((No1103S,(No0912S,(No1007S,(No1208S,No0909S)))),(((No0906S,((No1202S,No0913S),No0910S)),((No1206S,No0908S),No306)),No304)),No1114S,No305); (((No1103S,(No0912S,(No1208S,(No0909S,No1007S)))),((No0906S,(No0913S,(No0910S,No1202S))),((No1206S,No0908S),(No304,No306)))),No1114S,No305); (((No1103S,(No0912S,(No1007S,(No1208S,No0909S)))),((((No0906S,(No0913S,(No0910S,No1202S))),No306),(No1206S,No0908S)),No304)),No1114S,No305); (((No1103S,(No0912S,(No1208S,(No0909S,No1007S)))),(((No0913S,((No0910S,No1202S),No0906S)),(No304,No306)),(No1206S,No0908S))),No1114S,No305); (((((No1208S,(No0909S,No1007S)),(No1103S,No0912S)),((No1206S,((No0910S,No1202S),No0906S)),No0908S)),(No306,(No304,No0913S))),No1114S,No305); ((((No0912S,No1103S),(No1007S,(No1208S,No0909S))),((((No306,(No304,No0913S)),No1206S),((No0910S,No1202S),No0906S)),No0908S)),No1114S,No305); (((No0912S,No1103S),((No0909S,(No1007S,No1208S)),((No306,(No304,No0913S)),(No0908S,((No1206S,(No0910S,No1202S)),No0906S))))),No1114S,No305); ((((No0908S,(((No0913S,(No0910S,No1202S)),No0906S),No1206S)),(No306,No304)),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No1114S,No305); (No1114S,(((No0909S,(No1007S,No1208S)),((No0908S,(((No0910S,No1202S),No0906S),No1206S)),(No306,(No0913S,No304)))),(No0912S,No1103S)),No305); (No1114S,((((No1206S,No0908S),No0906S),(No306,(((No0910S,No1202S),No0913S),No304))),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No305); (No1114S,((((No306,(No304,No0913S)),(No1206S,(No0906S,(No0910S,No1202S)))),(No0912S,(No1103S,(No0909S,(No1208S,No1007S))))),No0908S),No305); (No1114S,((((No306,(No304,No0913S)),((No0906S,(No0910S,No1202S)),No1206S)),No0908S),((No0912S,No1103S),(No0909S,(No1208S,No1007S)))),No305); (No1114S,(((No0908S,No1206S),((No306,No304),(No0906S,(No0913S,(No0910S,No1202S))))),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))),No305); (No1114S,(No0906S,((No306,(No0913S,No304)),(((No0908S,No1206S),(No0910S,No1202S)),((No0912S,No1103S),(No1007S,(No0909S,No1208S)))))),No305); (No1114S,(((No0909S,(No1208S,No1007S)),(No0912S,No1103S)),(((No0908S,No1206S),((No0906S,(No0913S,(No0910S,No1202S))),No306)),No304)),No305); (((No0912S,No1103S),(No0909S,(No1208S,No1007S))),(No0908S,((No1206S,(No0906S,(No1202S,No0910S))),(No1114S,((No0913S,No304),No306)))),No305); ((((No0912S,No1103S),(No1007S,(No0909S,No1208S))),((No0908S,(No1206S,(No0906S,(No1202S,No0910S)))),((No0913S,No304),No306))),No1114S,No305); (((No1007S,(No0909S,No1208S)),((((No0913S,No304),No306),No0912S),(No1103S,((No1206S,(No0906S,(No1202S,No0910S))),No0908S)))),No1114S,No305); (No0908S,((((No1007S,(No0909S,No1208S)),No0912S),No1103S),((No1114S,((No304,No0913S),No306)),(((No1202S,No1206S),No0910S),No0906S))),No305); (No1114S,((((No304,No0913S),No306),((((No1202S,No0910S),No0906S),No1206S),No0908S)),(((No0909S,(No1208S,No1007S)),No0912S),No1103S)),No305); (No1114S,(((No0912S,(No1007S,(No1208S,No0909S))),No1103S),(((No1206S,No0908S),((No0913S,(No0906S,(No0910S,No1202S))),No304)),No306)),No305); (No1114S,((No0912S,No1103S),((No1007S,(No1208S,No0909S)),(No304,(No306,((No1206S,No0908S),(No0906S,((No0910S,No1202S),No0913S))))))),No305); (No1114S,(((No1208S,(No0909S,No1007S)),(No0912S,No1103S)),(((No304,No0913S),No306),(No0908S,((No0906S,(No0910S,No1202S)),No1206S)))),No305); (No1114S,(((No1208S,(No0909S,No1007S)),(No0912S,No1103S)),(((No304,No0913S),No306),(No0908S,((No0906S,(No0910S,No1202S)),No1206S)))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(((No304,((No0910S,No1202S),No0913S)),No306),((No1206S,No0908S),No0906S))),No305); (No1114S,(((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),(((No0913S,No304),No306),((No1206S,(No0906S,(No0910S,No1202S))),No0908S))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(((((No0913S,No304),No306),(No1206S,(No0910S,No1202S))),No0906S),No0908S)),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(No0912S,No1103S)),(((No0913S,No304),No306),((No0908S,No1206S),(No0906S,(No0910S,No1202S))))),No305); (No1114S,((No1208S,(No0909S,No1007S)),((No0912S,No1103S),(((No0913S,No304),No306),((No0908S,No1206S),(No0906S,(No0910S,No1202S)))))),No305); (No1114S,((((((No0913S,No304),No306),(No0908S,(No0906S,((No0910S,No1202S),No1206S)))),(No0909S,(No1007S,No1208S))),No0912S),No1103S),No305); (No1114S,((No0912S,(No1103S,(No0909S,(No1007S,No1208S)))),((No304,No306),((No1206S,No0908S),(No0906S,(No0913S,(No0910S,No1202S)))))),No305); (No1114S,((No0909S,(No1007S,No1208S)),((No0912S,(No1103S,(No0908S,((No1206S,(No0906S,(No1202S,No0910S))),(No304,No0913S))))),No306)),No305); (No1114S,((((No0908S,No1206S),(No0906S,(No1202S,No0910S))),(No306,(No304,No0913S))),((No1208S,(No0909S,No1007S)),(No0912S,No1103S))),No305); (No1114S,((No306,((((No1007S,(No1208S,No0909S)),No0912S),No1103S),((No0906S,((No1202S,No0910S),No1206S)),No0908S))),(No0913S,No304)),No305); (No1114S,((((No0909S,(No1007S,No1208S)),No0912S),No1103S),((No1206S,No0908S),(No0906S,(((No1202S,No0910S),No0913S),(No304,No306))))),No305); (No1114S,(No304,((No306,((((No1206S,No0906S),(No1202S,No0910S)),No0908S),(((No0909S,(No1007S,No1208S)),No0912S),No1103S))),No0913S)),No305); (No1114S,((No1007S,(No1208S,No0909S)),((No0912S,No1103S),(No0908S,((No1206S,(No0906S,(No1202S,No0910S))),((No0913S,No304),No306))))),No305); (No1114S,((No0909S,(No1007S,No1208S)),((No0912S,No1103S),(No0908S,((No1206S,(No0906S,(No1202S,No0910S))),((No0913S,No304),No306))))),No305); (No1114S,((No0912S,No1103S),((No0908S,((No0909S,(No1007S,No1208S)),((No0913S,No304),No306))),(No1206S,(No0906S,(No1202S,No0910S))))),No305); (No1114S,((No1007S,(No1208S,No0909S)),((No0912S,No1103S),(((No1206S,(No0906S,(No1202S,No0910S))),((No0913S,No304),No306)),No0908S))),No305); (No1114S,(((No306,(No0913S,No304)),((No1206S,No0908S),(No0906S,(No1202S,No0910S)))),(((No1208S,No0909S),No1007S),(No0912S,No1103S))),No305); (((((No1206S,No0906S),(No1202S,No0910S)),No0908S),(No1114S,(No306,(No0913S,No304)))),((No0909S,(No1007S,No1208S)),(No0912S,No1103S)),No305); (No1114S,(((No306,(No0913S,No304)),((No1206S,(No0906S,(No1202S,No0910S))),No0908S)),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No305); (No1114S,((No0906S,(((No1206S,No0908S),((No0913S,No304),(No1202S,No0910S))),((No0912S,No1103S),(No0909S,(No1007S,No1208S))))),No306),No305); (No1114S,(((No0906S,((No0913S,(No306,No304)),(No1202S,No0910S))),(No1206S,No0908S)),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),No305); (No1114S,(((No306,(No304,No0913S)),((((No1202S,No0910S),No0906S),No1206S),No0908S)),((No0912S,No1103S),(No1007S,(No1208S,No0909S)))),No305); (((No306,((No1114S,No304),No0913S)),((((No1202S,No0910S),No0906S),No1206S),No0908S)),((No0912S,No1103S),(No0909S,(No1007S,No1208S))),No305); (No1114S,(((No1007S,(No1208S,No0909S)),(((((No1202S,No0910S),No0906S),No1206S),No0908S),(No306,(No304,No0913S)))),(No0912S,No1103S)),No305); (((No306,(No304,No0913S)),No1114S),((((No1202S,No0910S),No0906S),(No1206S,No0908S)),(No0912S,(No1103S,(No1007S,(No1208S,No0909S))))),No305); (No1114S,((No0912S,No1103S),((((No1202S,No0910S),(No1007S,(No1208S,No0909S))),(No0906S,No1206S)),((No306,(No304,No0913S)),No0908S))),No305); (No1114S,((((No0908S,((No306,(No304,No0913S)),(No1206S,(No0906S,(No1202S,No0910S))))),(No1007S,(No1208S,No0909S))),No1103S),No0912S),No305); (No1114S,(((No306,(No304,No0913S)),((No1206S,(No0906S,(No1202S,No0910S))),No0908S)),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No305); (No1114S,(((No306,(No304,No0913S)),((No1206S,(No0906S,(No1202S,No0910S))),No0908S)),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No305); (No1114S,((((No1202S,No0910S),(No306,(No304,No0913S))),((No0908S,No1206S),No0906S)),(No0912S,((No1007S,(No1208S,No0909S)),No1103S))),No305); (No1114S,((No0908S,((No306,(No304,No0913S)),(No1206S,(No0906S,(No1202S,No0910S))))),((No1007S,(No1208S,No0909S)),(No1103S,No0912S))),No305); ((((No1202S,No0910S),(No0908S,No1206S)),((No306,((No1114S,No304),No0913S)),No0906S)),((No1208S,(No0909S,No1007S)),(No1103S,No0912S)),No305); (((No0906S,No1206S),(No1202S,No0910S)),(((No306,(No304,No0913S)),No1114S),(No0908S,(((No0909S,(No1007S,No1208S)),No1103S),No0912S))),No305); (((No0912S,No1103S),(No1007S,(No1208S,No0909S))),(((No306,(No304,No0913S)),((No1206S,((No1202S,No0910S),No0906S)),No0908S)),No1114S),No305); (No1114S,(((No306,(No304,No0913S)),((No0912S,No1103S),(No0909S,(No1007S,No1208S)))),((No1206S,((No1202S,No0910S),No0906S)),No0908S)),No305); (((No1103S,No0912S),(No0909S,(No1007S,No1208S))),(((No1206S,No0906S),((No306,((No1114S,No304),No0913S)),No0908S)),(No1202S,No0910S)),No305); phangorn/inst/extdata/trees/woodmouse.mrbayes.nex.run1.t0000644000175100001440000025045012761653173023202 0ustar hornikusers#NEXUS [ID: 2542150086] begin trees; translate 1 No305, 2 No304, 3 No306, 4 No0906S, 5 No0908S, 6 No0909S, 7 No0910S, 8 No0912S, 9 No0913S, 10 No1103S, 11 No1007S, 12 No1114S, 13 No1202S, 14 No1206S, 15 No1208S; tree rep.1 = ((((((3,((((((13,6),15),(5,9)),12),2),11)),7),8),4),10),14,1); tree rep.10000 = (12,((8,(10,(11,(15,6)))),(((14,((7,13),4)),(3,(9,2))),5)),1); tree rep.20000 = (((8,(10,((6,15),11))),(((2,9),((14,5),3)),(4,(7,13)))),12,1); tree rep.30000 = (((4,(((7,(9,13)),(3,2)),(14,5))),((10,8),((15,6),11))),12,1); tree rep.40000 = (((((6,11),15),(10,8)),(((3,(2,9)),(4,(14,(13,7)))),5)),12,1); tree rep.50000 = (((((7,13),4),((11,(6,15)),(10,8))),((3,(2,9)),(14,5))),12,1); tree rep.60000 = (12,(((8,10),((15,6),11)),(((4,(7,13)),(5,(3,(9,2)))),14)),1); tree rep.70000 = (((4,(13,7)),(((((9,2),3),12),5),14)),((8,10),(11,(6,15))),1); tree rep.80000 = (12,(((5,(14,((7,13),4))),(3,(2,9))),((8,((11,6),15)),10)),1); tree rep.90000 = (12,((((2,9),3),(14,(5,(4,(7,13))))),(((11,(15,6)),8),10)),1); tree rep.100000 = (((((9,2),3),(5,((14,(13,7)),4))),(8,(((15,6),11),10))),12,1); tree rep.110000 = (((((6,15),11),(8,10)),(5,(((4,14),(7,13)),((2,9),3)))),12,1); tree rep.120000 = (12,(((5,(14,(3,(9,2)))),((7,4),13)),(10,(((6,11),15),8))),1); tree rep.130000 = (12,(((10,8),(11,(6,15))),((3,(2,9)),(5,((4,(13,7)),14)))),1); tree rep.140000 = (12,(((3,(9,2)),((15,(11,6)),(10,8))),((5,(13,7)),(14,4))),1); tree rep.150000 = (((4,(((9,2),3),(5,(14,(7,13))))),12),(((11,(6,15)),10),8),1); tree rep.160000 = (((5,((14,((3,(9,2)),4)),(7,13))),(((15,11),6),(10,8))),12,1); tree rep.170000 = (12,((((15,6),11),(8,10)),((14,(((13,7),4),5)),((2,9),3))),1); tree rep.180000 = (((((7,13),4),5),((10,(8,(11,(6,15)))),(((9,2),3),14))),12,1); tree rep.190000 = (12,((3,(9,2)),(((8,10),((15,6),11)),(5,(14,((13,7),4))))),1); tree rep.200000 = ((((6,(11,15)),(10,8)),(((4,(13,7)),((2,9),3)),(14,5))),12,1); tree rep.210000 = (((8,(10,(11,(15,6)))),(((13,7),4),(5,((3,(2,9)),14)))),12,1); tree rep.220000 = ((((4,(7,13)),(((3,(9,2)),5),14)),(10,((11,(15,6)),8))),12,1); tree rep.230000 = (12,((14,((((8,(11,(15,6))),10),(((13,7),5),4)),3)),(2,9)),1); tree rep.240000 = ((((14,((2,9),3)),((((6,(15,11)),8),10),5)),(4,(13,7))),12,1); tree rep.250000 = (12,(((6,(11,15)),(10,8)),((3,(9,2)),(5,((14,(13,7)),4)))),1); tree rep.260000 = (((((11,(15,6)),10),8),((((3,(2,9)),14),(4,(13,7))),5)),12,1); tree rep.270000 = (12,((5,14),((((8,10),((6,15),11)),((9,2),(4,(7,13)))),3)),1); tree rep.280000 = ((((((7,13),((2,9),3)),(5,14)),((11,(15,6)),(10,8))),4),12,1); tree rep.290000 = (((8,(((11,6),15),10)),((5,((2,9),3)),((4,14),(7,13)))),12,1); tree rep.300000 = (12,((((3,(5,((7,13),4))),(2,9)),14),(8,(((11,15),6),10))),1); tree rep.310000 = (((11,(6,15)),((5,((14,((13,7),4)),(3,(2,9)))),(10,8))),12,1); tree rep.320000 = (12,((((15,6),11),(10,8)),(((3,(2,9)),(4,(14,(7,13)))),5)),1); tree rep.330000 = ((((((11,(6,15)),(10,8)),(5,((7,13),(4,14)))),3),(2,9)),12,1); tree rep.340000 = ((((10,8),(11,(15,6))),(((((2,9),3),(4,14)),(7,13)),5)),12,1); tree rep.350000 = (12,((5,(((6,15),11),(10,8))),(((2,9),3),(4,(14,(7,13))))),1); tree rep.360000 = (12,(((((6,15),11),8),10),((5,((2,9),3)),((14,(7,13)),4))),1); tree rep.370000 = (12,((4,(14,((7,13),(5,(3,(9,2)))))),((11,(15,6)),(10,8))),1); tree rep.380000 = (((((14,5),(4,(13,7))),((2,9),3)),(8,((6,(15,11)),10))),12,1); tree rep.390000 = ((((((7,13),4),5),((2,9),3)),((((11,(15,6)),10),8),14)),12,1); tree rep.400000 = (12,(((9,2),3),((((6,11),15),(8,10)),((14,5),(4,(7,13))))),1); tree rep.410000 = ((5,((((4,(13,7)),14),((10,8),((11,15),6))),(3,(9,2)))),12,1); tree rep.420000 = ((((8,(11,(15,6))),10),(5,((14,((13,7),4)),(3,(9,2))))),12,1); tree rep.430000 = (12,((((9,2),((7,13),4)),(14,(3,5))),((8,(11,(6,15))),10)),1); tree rep.440000 = (12,((5,((4,((3,(9,2)),(13,7))),14)),((8,((11,15),6)),10)),1); tree rep.450000 = ((((8,10),((6,15),11)),(5,(((2,9),3),(14,((13,7),4))))),12,1); tree rep.460000 = (((14,((5,((2,9),3)),(4,(13,7)))),((8,10),((15,11),6))),12,1); tree rep.470000 = (((4,(13,7)),(14,(5,(3,(2,9))))),((10,((11,(15,6)),8)),12),1); tree rep.480000 = (12,((((15,6),11),(8,10)),((((2,9),3),((4,(13,7)),14)),5)),1); tree rep.490000 = ((((10,8),(11,(6,15))),(((9,2),3),(((4,(13,7)),14),5))),12,1); tree rep.500000 = (12,(((10,(6,(15,11))),8),(((2,9),3),(5,(((7,13),4),14)))),1); tree rep.510000 = (12,((8,(((11,6),15),10)),((4,(((2,9),3),5)),((13,7),14))),1); tree rep.520000 = ((((11,(6,15)),(8,10)),12),((4,(13,7)),((5,14),(3,(9,2)))),1); tree rep.530000 = (12,((((13,7),4),(14,5)),(((9,2),3),((15,(11,6)),(8,10)))),1); tree rep.540000 = (12,((14,((4,((13,7),9)),(2,(3,5)))),((((15,11),6),8),10)),1); tree rep.550000 = ((((3,((9,2),(14,((7,13),4)))),5),12),(10,(((11,15),6),8)),1); tree rep.560000 = (12,((5,(3,((14,(4,(13,7))),(2,9)))),((10,8),((15,6),11))),1); tree rep.570000 = (((((6,(15,11)),10),8),(((3,(2,9)),(((7,13),4),14)),5)),12,1); tree rep.580000 = ((((5,(14,(7,(4,13)))),(3,(9,2))),(8,(10,(6,(11,15))))),12,1); tree rep.590000 = (12,((5,(((4,(7,13)),14),((2,9),3))),(8,(10,(11,(6,15))))),1); tree rep.600000 = ((10,(((((7,13),4),(5,(3,(2,9)))),14),8)),(12,((11,6),15)),1); tree rep.610000 = (((((3,(2,9)),(14,((4,13),7))),5),(10,(8,(6,(15,11))))),12,1); tree rep.620000 = (((3,(9,2)),(((((7,13),4),14),(((15,11),6),(8,10))),5)),12,1); tree rep.630000 = (12,((((15,6),11),(8,10)),((((13,7),(14,4)),((2,9),3)),5)),1); tree rep.640000 = (((((14,(7,13)),4),(3,(9,2))),(5,12)),(8,(((15,6),11),10)),1); tree rep.650000 = (12,(((((11,15),6),10),8),(((5,3),4),(14,((13,7),(9,2))))),1); tree rep.660000 = ((((6,(15,11)),(8,10)),(14,(((9,2),3),(((7,13),4),5)))),12,1); tree rep.670000 = (((((11,(6,15)),8),10),(14,((7,13),(((3,(2,9)),5),4)))),12,1); tree rep.680000 = (12,(((((2,9),3),5),(4,(14,(13,7)))),(8,(10,(11,(6,15))))),1); tree rep.690000 = (((((13,7),((14,5),4)),(3,(9,2))),((10,8),((15,11),6))),12,1); tree rep.700000 = (((5,((4,(3,(2,9))),(7,(13,14)))),(((6,11),15),(10,8))),12,1); tree rep.710000 = (((5,14),((7,13),(4,((2,9),3)))),(12,(8,(((15,6),11),10))),1); tree rep.720000 = (((2,9),3),((7,13),(5,(4,(14,((10,(8,(6,(11,15)))),12))))),1); tree rep.730000 = (((((11,15),6),(10,8)),((4,(14,(7,13))),(5,(3,(2,9))))),12,1); tree rep.740000 = ((12,((11,(15,6)),(10,8))),((14,4),(((7,13),5),((2,9),3))),1); tree rep.750000 = (12,(((((9,2),3),(14,((13,7),4))),5),((10,8),((15,11),6))),1); tree rep.760000 = ((((10,8),((11,6),15)),(5,((4,(14,(13,7))),(3,(2,9))))),12,1); tree rep.770000 = (12,(((3,(2,9)),((10,((6,(11,15)),8)),(5,14))),((13,7),4)),1); tree rep.780000 = (((3,(2,9)),(5,(14,(((8,10),((11,15),6)),12)))),(4,(13,7)),1); tree rep.790000 = (12,(((14,(((3,(2,9)),(7,13)),4)),5),(8,(10,((15,6),11)))),1); tree rep.800000 = (12,(((((9,2),3),5),(14,(4,(7,13)))),((8,((15,6),11)),10)),1); tree rep.810000 = (12,((((15,11),6),(10,8)),(((9,2),3),((14,5),(4,(7,13))))),1); tree rep.820000 = ((((14,(4,((7,13),(3,(9,2))))),5),(10,(8,(11,(15,6))))),12,1); tree rep.830000 = (12,((((3,(2,9)),5),(14,(4,(7,13)))),(8,(10,(6,(15,11))))),1); tree rep.840000 = ((((8,10),(11,(15,6))),(((14,(13,7)),4),(5,((2,9),3)))),12,1); tree rep.850000 = (12,(((10,((15,11),6)),8),(((5,((7,13),4)),14),(3,(9,2)))),1); tree rep.860000 = ((((((3,(9,2)),(14,(13,7))),4),5),((((6,15),11),10),8)),12,1); tree rep.870000 = (12,(((((9,2),3),(5,14)),((8,(11,(15,6))),10)),((4,13),7)),1); tree rep.880000 = ((((14,4),(13,7)),((((2,9),3),(10,((15,(6,11)),8))),5)),12,1); tree rep.890000 = ((((4,(13,7)),(14,(5,(3,(2,9))))),(10,(((15,6),11),8))),12,1); tree rep.900000 = (12,(((8,10),((6,15),11)),((5,14),((3,(2,9)),(4,(7,13))))),1); tree rep.910000 = (((((14,(7,13)),((2,9),3)),(4,5)),(10,(8,(6,(11,15))))),12,1); tree rep.920000 = ((12,(((9,2),3),(5,(((13,7),4),14)))),((8,(11,(6,15))),10),1); tree rep.930000 = (((((3,(9,2)),5),(14,((13,7),4))),((8,((6,15),11)),10)),12,1); tree rep.940000 = (12,(((8,10),(11,(15,6))),((((3,(2,9)),(7,13)),(5,14)),4)),1); tree rep.950000 = (((((6,(15,11)),8),10),(((13,7),(14,4)),(5,(3,(2,9))))),12,1); tree rep.960000 = ((((8,10),((6,15),11)),((((7,13),14),4),(((2,9),3),5))),12,1); tree rep.970000 = ((((((4,14),5),(3,(9,2))),(7,13)),(8,((11,(15,6)),10))),12,1); tree rep.980000 = (12,(((((14,4),(8,(10,(11,(15,6))))),5),(7,13)),((9,2),3)),1); tree rep.990000 = (((5,((3,(9,2)),((7,(14,13)),4))),(10,(((6,11),15),8))),12,1); tree rep.1000000 = (12,(((5,(((13,7),14),4)),((2,9),3)),((11,(15,6)),(10,8))),1); tree rep.1010000 = (12,((((14,5),(((15,6),11),(10,8))),(4,(13,7))),(3,(2,9))),1); tree rep.1020000 = (12,(((6,15),11),((4,(5,(((2,9),3),(14,(13,7))))),(8,10))),1); tree rep.1030000 = (((5,3),((4,(7,13)),((2,9),14))),(((((15,11),6),8),10),12),1); tree rep.1040000 = (12,(3,(((((14,4),((8,10),(6,(11,15)))),5),(13,7)),(2,9))),1); tree rep.1050000 = (12,(((5,((4,(13,7)),14)),(3,(2,9))),(10,(8,(6,(15,11))))),1); tree rep.1060000 = ((((4,((13,7),14)),(5,((8,10),(6,(15,11))))),(3,(2,9))),12,1); tree rep.1070000 = (12,((((8,(10,((15,11),6))),14),((((9,2),3),5),(13,7))),4),1); tree rep.1080000 = ((((10,8),((15,11),6)),12),((5,(14,(3,(9,2)))),(4,(7,13))),1); tree rep.1090000 = (12,((10,8),(((6,11),15),((((7,13),(3,(2,9))),(5,14)),4))),1); tree rep.1100000 = (12,((((13,(7,14)),(5,4)),(3,(2,9))),((8,((15,11),6)),10)),1); tree rep.1110000 = ((((10,8),(6,(15,11))),((4,(((13,7),14),(3,(9,2)))),5)),12,1); tree rep.1120000 = (((10,((6,(11,15)),8)),(((7,13),14),((4,5),((2,9),3)))),12,1); tree rep.1130000 = ((((((9,2),(14,((13,7),4))),5),3),(8,(((15,11),6),10))),12,1); tree rep.1140000 = ((((5,((10,8),((6,15),11))),((9,2),3)),(((13,7),14),4)),12,1); tree rep.1150000 = ((10,8),((12,((5,3),((2,9),((4,(7,13)),14)))),((11,15),6)),1); tree rep.1160000 = (12,(5,(((2,9),3),(((6,(15,11)),(10,8)),((13,7),(14,4))))),1); tree rep.1170000 = (((14,(((7,13),4),(5,((9,2),3)))),(8,((6,(15,11)),10))),12,1); tree rep.1180000 = ((((10,(11,(15,6))),8),(((7,(4,13)),14),(5,((2,9),3)))),12,1); tree rep.1190000 = (12,((5,(((4,(7,13)),14),((9,2),3))),(8,(10,((6,15),11)))),1); tree rep.1200000 = ((((14,(4,(7,13))),5),(3,(9,2))),(((10,8),((15,6),11)),12),1); tree rep.1210000 = (12,(((8,10),((11,15),6)),((((13,7),4),5),(((9,3),2),14))),1); tree rep.1220000 = ((((9,2),3),((14,((6,(15,11)),(8,10))),(((13,7),4),5))),12,1); tree rep.1230000 = (((((9,2),3),((4,14),(13,7))),5),(12,((8,10),(11,(15,6)))),1); tree rep.1240000 = ((((8,10),((15,11),6)),((5,((13,7),14)),((3,(9,2)),4))),12,1); tree rep.1250000 = (12,((10,8),(((15,11),6),(14,((4,(13,7)),(5,((9,2),3)))))),1); tree rep.1260000 = ((((((6,15),11),10),8),((14,((7,13),4)),((3,(9,2)),5))),12,1); tree rep.1270000 = (12,(((7,13),(((2,9),3),((5,14),4))),((((6,11),15),10),8)),1); tree rep.1280000 = ((14,((((10,8),((6,15),11)),5),(((3,(9,2)),(7,13)),4))),12,1); tree rep.1290000 = (12,((8,(10,(11,(15,6)))),(((4,7),13),(14,(5,(3,(9,2)))))),1); tree rep.1300000 = (((((13,7),((14,5),((9,2),3))),4),(10,(((6,15),11),8))),12,1); tree rep.1310000 = ((((8,((6,15),11)),10),((3,(2,9)),((14,5),(4,(13,7))))),12,1); tree rep.1320000 = (12,((((2,9),3),(5,((4,(7,13)),14))),((10,((15,11),6)),8)),1); tree rep.1330000 = (12,(((3,(2,9)),(14,((5,4),(13,7)))),((((15,6),11),8),10)),1); tree rep.1340000 = ((8,((((15,11),6),(5,(((4,14),(13,7)),((2,9),3)))),10)),12,1); tree rep.1350000 = (((((5,14),((4,7),13)),((2,9),3)),(((11,(6,15)),10),8)),12,1); tree rep.1360000 = ((12,((((15,11),6),8),10)),(5,((14,((7,13),4)),((9,2),3))),1); tree rep.1370000 = ((((8,(6,(11,15))),10),(((5,14),((7,13),4)),(3,(9,2)))),12,1); tree rep.1380000 = (((((13,7),14),(5,4)),((12,(2,9)),3)),((8,10),(6,(11,15))),1); tree rep.1390000 = (12,(((4,(7,13)),((5,14),((2,9),3))),(10,(8,((15,11),6)))),1); tree rep.1400000 = ((12,(8,(((15,6),11),10))),(((3,(9,2)),5),(14,((13,7),4))),1); tree rep.1410000 = (((3,((5,(9,2)),12)),((4,(7,13)),14)),((10,((15,6),11)),8),1); tree rep.1420000 = ((((10,(11,(15,6))),8),((14,5),(((9,2),3),(4,(7,13))))),12,1); tree rep.1430000 = (12,(((11,(6,15)),(8,10)),((3,((13,7),4)),(((9,2),5),14))),1); tree rep.1440000 = ((((5,((14,(4,(7,13))),((2,9),3))),(8,10)),(6,(11,15))),12,1); tree rep.1450000 = (((((14,(5,(2,3))),4),((10,8),((11,15),6))),(9,(7,13))),12,1); tree rep.1460000 = ((((((4,(13,7)),((5,14),(3,(9,2)))),10),8),(6,(11,15))),12,1); tree rep.1470000 = (12,((8,(10,(11,(15,6)))),((((4,(7,13)),14),((2,9),3)),5)),1); tree rep.1480000 = (((((7,(4,13)),(5,((9,2),3))),14),(((6,(15,11)),8),10)),12,1); tree rep.1490000 = ((12,((3,(9,2)),((((7,4),13),14),5))),(((6,15),11),(8,10)),1); tree rep.1500000 = ((7,((5,(14,((3,(2,9)),(4,((10,8),((15,6),11)))))),13)),12,1); tree rep.1510000 = (((((6,11),15),(10,8)),((((13,7),14),4),(((2,9),3),5))),12,1); tree rep.1520000 = ((((14,5),(((7,13),4),((2,9),3))),((8,10),((15,11),6))),12,1); tree rep.1530000 = ((5,((4,(7,13)),(14,(3,(2,9))))),(12,(8,(10,(11,(15,6))))),1); tree rep.1540000 = (12,(((3,(2,9)),((4,((14,13),7)),5)),(10,(8,((6,15),11)))),1); tree rep.1550000 = (12,(((11,(15,6)),(10,8)),(5,(((9,2),3),(14,((7,13),4))))),1); tree rep.1560000 = ((((3,(9,2)),((4,(13,7)),(5,14))),((11,(15,6)),(8,10))),12,1); tree rep.1570000 = (((15,11),6),(5,((2,9),(((14,((4,3),12)),(13,7)),(10,8)))),1); tree rep.1580000 = ((((11,(15,6)),(10,8)),(((4,(7,13)),14),(((2,9),3),5))),12,1); tree rep.1590000 = (((((2,9),3),(4,((7,13),(14,5)))),((8,(6,(15,11))),10)),12,1); tree rep.1600000 = ((((11,(15,6)),(10,8)),12),((((14,4),(13,7)),((2,9),3)),5),1); tree rep.1610000 = ((((10,8),((11,15),6)),((((2,9),3),5),(14,(4,(7,13))))),12,1); tree rep.1620000 = (((((2,9),3),((14,5),(4,(7,13)))),(10,(8,((6,15),11)))),12,1); tree rep.1630000 = (((((7,13),((5,4),14)),((9,2),3)),(10,(8,(6,(11,15))))),12,1); tree rep.1640000 = (((((14,5),((2,9),3)),(4,(13,7))),((11,(6,15)),(8,10))),12,1); tree rep.1650000 = (((((3,(9,2)),(((13,7),14),4)),5),(((6,15),11),(8,10))),12,1); tree rep.1660000 = ((((10,((15,11),6)),8),(4,(((14,(7,13)),5),(3,(2,9))))),12,1); tree rep.1670000 = (((14,(((13,7),4),(5,((9,2),3)))),(8,(10,((15,11),6)))),12,1); tree rep.1680000 = (12,(((8,10),(11,(6,15))),(((7,13),(4,14)),(((9,2),3),5))),1); tree rep.1690000 = (((8,((6,(15,11)),10)),(5,((14,((13,4),7)),((9,2),3)))),12,1); tree rep.1700000 = (12,(((4,(5,(13,7))),(((9,2),3),14)),((8,(15,(6,11))),10)),1); tree rep.1710000 = (((((((9,2),3),(4,(7,13))),((8,10),(6,(11,15)))),5),14),12,1); tree rep.1720000 = (((8,((6,(11,15)),10)),(((14,4),(13,7)),((3,(2,9)),5))),12,1); tree rep.1730000 = (((5,((4,14),(13,7))),((2,9),3)),((((11,15),6),(10,8)),12),1); tree rep.1740000 = (12,(((((2,9),3),5),(((13,7),4),14)),((8,((15,6),11)),10)),1); tree rep.1750000 = (12,((((4,(5,14)),(7,13)),((9,2),3)),(10,(8,(11,(15,6))))),1); tree rep.1760000 = ((((5,(14,((7,13),4))),(3,(9,2))),((11,(15,6)),(10,8))),12,1); tree rep.1770000 = ((10,(8,(6,(11,15)))),((((14,4),(13,7)),(5,(3,(2,9)))),12),1); tree rep.1780000 = (12,((((((7,13),4),14),5),((2,9),3)),((10,8),(6,(11,15)))),1); tree rep.1790000 = ((((8,10),(11,(15,6))),((3,(2,9)),(((7,13),(4,14)),5))),12,1); tree rep.1800000 = (((10,8),(11,(6,15))),((((((4,(7,13)),9),3),2),(14,5)),12),1); tree rep.1810000 = (((10,(8,(11,(15,6)))),(((((9,2),3),(7,13)),5),(14,4))),12,1); tree rep.1820000 = (12,((4,((3,(9,2)),(5,((7,13),14)))),((10,8),(11,(6,15)))),1); tree rep.1830000 = (12,((((11,(6,15)),8),10),((14,5),((4,(13,7)),(3,(9,2))))),1); tree rep.1840000 = (12,(((4,(14,((13,7),(3,(9,2))))),5),(((11,(15,6)),10),8)),1); tree rep.1850000 = ((((14,(9,2)),(7,13)),5),(4,(3,(12,((10,8),((15,11),6))))),1); tree rep.1860000 = ((((8,10),(6,(15,11))),((14,5),(((7,13),((9,2),3)),4))),12,1); tree rep.1870000 = (12,(((8,10),(11,(6,15))),(((3,(9,2)),5),((4,(7,13)),14))),1); tree rep.1880000 = ((((((14,((13,7),4)),5),(3,(2,9))),(10,8)),(6,(15,11))),12,1); tree rep.1890000 = ((((10,(11,(6,15))),8),(((7,13),4),(((9,2),3),(14,5)))),12,1); tree rep.1900000 = (12,(((14,5),((3,(2,9)),((13,7),4))),((((6,15),11),8),10)),1); tree rep.1910000 = (((((11,15),6),(10,8)),(5,(14,((7,(13,4)),((2,9),3))))),12,1); tree rep.1920000 = (((((9,2),(5,(7,(4,(14,13))))),3),(10,((6,(15,11)),8))),12,1); tree rep.1930000 = ((((9,2),3),(5,(((13,7),4),14))),(12,(((6,(15,11)),10),8)),1); tree rep.1940000 = (12,((((9,2),3),(5,(((13,7),4),14))),(10,((6,(11,15)),8))),1); tree rep.1950000 = ((((((13,7),(5,4)),((2,9),3)),14),(10,(8,((11,15),6)))),12,1); tree rep.1960000 = (12,(((3,(2,9)),((14,4),((13,7),5))),(((11,(6,15)),10),8)),1); tree rep.1970000 = (12,(((((7,13),(14,5)),4),(3,(2,9))),((10,8),((15,6),11))),1); tree rep.1980000 = ((((6,11),15),(8,10)),(((3,(2,9)),(5,(4,(14,(13,7))))),12),1); tree rep.1990000 = (((8,(10,((15,6),11))),((3,(2,9)),((5,14),((13,7),4)))),12,1); tree rep.2000000 = ((((6,(11,15)),(8,10)),(5,(((((2,9),3),4),14),(7,13)))),12,1); tree rep.2010000 = ((((10,8),(11,(15,6))),((((4,(13,7)),5),14),((2,9),3))),12,1); tree rep.2020000 = (12,(5,(((6,(11,15)),(10,8)),((3,(9,2)),(((13,7),14),4)))),1); tree rep.2030000 = (12,(((((15,11),6),8),10),((((4,(13,7)),14),5),(3,(2,9)))),1); tree rep.2040000 = (8,(((15,11),6),(((((3,(2,9)),((4,14),(13,7))),5),12),10)),1); tree rep.2050000 = (((((15,11),6),(((((7,13),4),14),((2,9),3)),5)),(10,8)),12,1); tree rep.2060000 = ((((8,(11,(15,6))),10),((3,(2,9)),((14,((7,13),4)),5))),12,1); tree rep.2070000 = (12,(((5,((3,(2,9)),14)),(4,(13,7))),((8,((11,15),6)),10)),1); tree rep.2080000 = (12,(((15,6),11),((10,8),((4,(13,7)),((9,2),((14,5),3))))),1); tree rep.2090000 = (12,((((((7,13),4),((9,2),3)),5),14),(((15,11),6),(10,8))),1); tree rep.2100000 = (((9,2),(((((8,10),(6,(15,11))),(((13,7),4),14)),5),3)),12,1); tree rep.2110000 = ((((14,(7,(13,4))),(((2,9),3),5)),((10,8),((11,15),6))),12,1); tree rep.2120000 = (12,((((8,10),(6,(11,15))),(((7,13),4),(5,14))),(3,(9,2))),1); tree rep.2130000 = (12,(((5,(3,(((7,13),4),14))),(9,2)),(((15,(6,11)),8),10)),1); tree rep.2140000 = (12,(((10,8),(6,(11,15))),((7,13),(4,((((2,9),3),5),14)))),1); tree rep.2150000 = (12,((((6,15),11),(10,8)),(((14,(4,(7,13))),(3,(2,9))),5)),1); tree rep.2160000 = ((((((4,14),5),(7,13)),((2,9),3)),((8,10),(11,(15,6)))),12,1); tree rep.2170000 = ((((((13,7),4),14),5),(3,(2,9))),(12,((11,(15,6)),(10,8))),1); tree rep.2180000 = ((12,(((3,(9,2)),((4,(13,7)),14)),5)),((10,(6,(15,11))),8),1); tree rep.2190000 = ((((8,((3,(2,9)),(5,((4,(13,7)),14)))),10),(11,(15,6))),12,1); tree rep.2200000 = (12,(((9,2),3),((((8,10),((15,11),6)),(14,((13,4),7))),5)),1); tree rep.2210000 = ((12,((((15,6),11),8),10)),(((2,9),3),(5,(14,(4,(13,7))))),1); tree rep.2220000 = (((5,(12,((2,9),3))),((4,(13,7)),14)),(((15,11),6),(10,8)),1); tree rep.2230000 = (((5,((4,(14,(13,7))),((9,2),3))),((((15,6),11),8),10)),12,1); tree rep.2240000 = (12,((((11,(6,15)),10),8),((5,(3,(9,2))),(((13,7),4),14))),1); tree rep.2250000 = ((((((3,2),9),(14,((13,7),4))),5),12),((8,10),((6,15),11)),1); tree rep.2260000 = ((((8,10),(11,(15,6))),(((3,(9,2)),4),(5,((14,7),13)))),12,1); tree rep.2270000 = (((8,10),(11,(6,15))),(((((14,4),(13,7)),5),(3,(2,9))),12),1); tree rep.2280000 = (((((6,(11,15)),8),10),(((2,9),((14,5),((13,7),4))),3)),12,1); tree rep.2290000 = (12,(((14,(4,(7,13))),((9,2),3)),(((10,8),(11,(6,15))),5)),1); tree rep.2300000 = (12,((((4,(14,(13,7))),(3,(2,9))),5),((11,(15,6)),(10,8))),1); tree rep.2310000 = (12,(((4,((13,7),(5,14))),((2,9),3)),((6,(11,15)),(10,8))),1); tree rep.2320000 = ((((4,(7,13)),(((3,(9,2)),((10,8),(11,(15,6)))),14)),5),12,1); tree rep.2330000 = (12,((4,(13,7)),(((5,((9,2),3)),14),(((6,(11,15)),8),10))),1); tree rep.2340000 = (12,((((3,(9,2)),5),(14,((7,13),4))),((8,10),(15,(6,11)))),1); tree rep.2350000 = (12,(((4,((13,7),14)),(5,((2,9),3))),((10,8),((11,6),15))),1); tree rep.2360000 = (((((6,15),11),(10,8)),(5,(14,((4,(13,7)),((2,9),3))))),12,1); tree rep.2370000 = (12,(((13,7),(5,((14,4),((10,8),((15,11),6))))),(3,(9,2))),1); tree rep.2380000 = (((6,(15,11)),(8,10)),(12,((5,(14,(7,(4,13)))),(3,(2,9)))),1); tree rep.2390000 = ((((10,8),((6,15),11)),(((14,5),((7,13),4)),(3,(2,9)))),12,1); tree rep.2400000 = ((((10,8),((11,15),6)),((5,14),((4,(7,13)),((9,2),3)))),12,1); tree rep.2410000 = (((8,((11,(15,6)),10)),12),(((5,14),((9,2),3)),((7,13),4)),1); tree rep.2420000 = (12,((((13,7),(4,14)),((((15,6),11),(8,10)),5)),(3,(9,2))),1); tree rep.2430000 = (((((5,14),(4,(13,7))),((9,2),3)),(8,(10,((11,15),6)))),12,1); tree rep.2440000 = ((((13,((14,7),4)),(((9,2),3),5)),12),((10,8),(11,(6,15))),1); tree rep.2450000 = ((((((6,15),11),8),10),((3,(9,2)),(((5,14),4),(7,13)))),12,1); tree rep.2460000 = ((((5,(((7,14),4),13)),((9,2),3)),((10,(15,(11,6))),8)),12,1); tree rep.2470000 = ((((5,14),(7,13)),(((9,2),3),4)),((((6,(15,11)),8),10),12),1); tree rep.2480000 = (((14,((3,(9,2)),(5,(4,(7,13))))),(((15,11),6),(8,10))),12,1); tree rep.2490000 = (12,((((8,((15,(11,6)),10)),(((7,13),4),14)),5),((9,2),3)),1); tree rep.2500000 = (12,(((((9,2),3),5),(14,((13,7),4))),((15,(11,6)),(10,8))),1); tree rep.2510000 = ((6,(15,11)),(8,(10,(12,((5,(14,(4,(7,13)))),(3,(2,9)))))),1); tree rep.2520000 = ((((10,((11,15),6)),8),(((2,9),3),(5,(14,(4,(7,13)))))),12,1); tree rep.2530000 = (14,(((4,(12,(3,(9,2)))),((13,7),5)),((8,10),((11,6),15))),1); tree rep.2540000 = ((((8,(11,(6,15))),10),((4,(13,7)),((3,(2,9)),(14,5)))),12,1); tree rep.2550000 = ((((5,14),(((13,7),4),((9,2),3))),(((15,6),11),(10,8))),12,1); tree rep.2560000 = (12,(((11,15),6),(8,(10,((((7,13),5),(14,4)),((9,2),3))))),1); tree rep.2570000 = (((14,(((13,7),4),5)),((9,2),3)),(12,((11,(6,15)),(10,8))),1); tree rep.2580000 = ((((8,10),((6,15),11)),(3,((9,2),(((14,4),(7,13)),5)))),12,1); tree rep.2590000 = (12,(((10,(11,(15,6))),8),((14,((3,(9,2)),5)),((13,7),4))),1); tree rep.2600000 = ((((10,8),(6,(15,11))),(5,(((13,7),(14,4)),(3,(9,2))))),12,1); tree rep.2610000 = (((8,(((14,(4,(7,13))),(5,(3,(9,2)))),10)),(6,(11,15))),12,1); tree rep.2620000 = (12,(((10,8),(11,(6,15))),((5,(7,13)),(((2,9),3),(4,14)))),1); tree rep.2630000 = ((((2,9),3),((5,14),(4,(13,7)))),((10,(8,((11,15),6))),12),1); tree rep.2640000 = (((10,8),((11,15),6)),(((((7,13),14),4),(((9,2),3),5)),12),1); tree rep.2650000 = (12,(((5,((13,7),4)),(((2,9),3),14)),((8,(11,(6,15))),10)),1); tree rep.2660000 = (12,(((14,((7,13),4)),(5,(3,(2,9)))),(8,((15,(6,11)),10))),1); tree rep.2670000 = ((((8,10),((6,15),11)),(((4,(7,13)),((3,(2,9)),5)),14)),12,1); tree rep.2680000 = (((8,10),((15,6),11)),((((((2,9),3),5),14),(4,(7,13))),12),1); tree rep.2690000 = ((((((14,4),(7,13)),5),(3,(9,2))),(8,((6,(15,11)),10))),12,1); tree rep.2700000 = (((5,(((7,13),4),14)),(3,(9,2))),(12,(((11,(15,6)),10),8)),1); tree rep.2710000 = ((((6,(11,15)),8),10),(((14,5),(((9,2),3),((13,7),4))),12),1); tree rep.2720000 = (((((10,8),(11,(15,6))),((2,9),3)),(4,(5,((7,13),14)))),12,1); tree rep.2730000 = (((10,((15,6),11)),8),(12,(5,((((4,14),7),13),(3,(2,9))))),1); tree rep.2740000 = (12,((((11,15),6),(10,8)),((5,4),((13,7),(14,(3,(2,9)))))),1); tree rep.2750000 = ((((8,10),((15,11),6)),12),(((2,9),3),(((7,13),4),(5,14))),1); tree rep.2760000 = (((((5,14),(3,(2,9))),((13,7),4)),((8,(11,(15,6))),10)),12,1); tree rep.2770000 = (12,((4,((5,(14,((9,2),3))),(7,13))),((8,(11,(6,15))),10)),1); tree rep.2780000 = (12,((3,(9,2)),((7,(4,13)),(((8,((15,6),11)),10),(5,14)))),1); tree rep.2790000 = (12,((10,((11,(15,6)),8)),((4,((14,5),(7,13))),(3,(9,2)))),1); tree rep.2800000 = (((((11,(15,6)),8),10),((4,((14,5),(13,7))),(3,(2,9)))),12,1); tree rep.2810000 = (((12,((9,2),3)),(5,((11,(15,6)),(10,8)))),((4,14),(7,13)),1); tree rep.2820000 = ((((6,(11,15)),(10,8)),((5,((2,9),3)),((13,7),(4,14)))),12,1); tree rep.2830000 = ((((10,8),(11,(15,6))),((4,((13,7),((9,2),3))),(14,5))),12,1); tree rep.2840000 = (((5,((((2,9),3),14),(4,(13,7)))),(10,(8,(6,(15,11))))),12,1); tree rep.2850000 = ((((((13,7),(3,(2,9))),(14,5)),4),(((11,15),6),(10,8))),12,1); tree rep.2860000 = (12,((5,(((14,(13,7)),4),(3,(2,9)))),((6,(15,11)),(10,8))),1); tree rep.2870000 = (((5,((((13,7),4),((2,9),3)),14)),((10,((15,6),11)),8)),12,1); tree rep.2880000 = ((((10,8),((11,15),6)),((14,(((9,2),3),(4,5))),(7,13))),12,1); tree rep.2890000 = (12,((((3,(2,9)),(14,((13,7),4))),5),((11,(6,15)),(8,10))),1); tree rep.2900000 = ((((((7,13),4),(5,14)),(3,(2,9))),((((6,15),11),10),8)),12,1); tree rep.2910000 = (12,(5,(((7,13),4),((14,((9,2),3)),((11,(6,15)),(10,8))))),1); tree rep.2920000 = (12,(((((3,(9,2)),(13,7)),4),(5,14)),((8,(11,(6,15))),10)),1); tree rep.2930000 = ((((10,8),(11,(6,15))),((((4,(13,7)),14),5),((2,9),3))),12,1); tree rep.2940000 = (((((2,3),9),(((5,14),(13,7)),4)),(((6,(11,15)),10),8)),12,1); tree rep.2950000 = ((((5,((7,13),(((2,9),3),4))),14),(((11,(6,15)),10),8)),12,1); tree rep.2960000 = ((((10,((11,15),6)),8),(((9,2),3),(((4,(13,7)),14),5))),12,1); tree rep.2970000 = (12,(((3,(9,2)),((5,14),(4,(13,7)))),(8,(10,((15,11),6)))),1); tree rep.2980000 = (12,((((11,(6,15)),10),8),(5,(14,((3,(2,9)),((7,13),4))))),1); tree rep.2990000 = (((5,14),((((2,9),3),4),(7,13))),(((11,(6,15)),(8,10)),12),1); tree rep.3000000 = ((((14,5),(3,(9,2))),(((10,8),(6,(15,11))),(4,(7,13)))),12,1); tree rep.3010000 = (12,((((3,(2,9)),5),((13,7),(14,4))),((8,10),((15,11),6))),1); tree rep.3020000 = ((((6,11),15),(10,8)),(((3,(2,9)),12),(5,((4,(13,7)),14))),1); tree rep.3030000 = (12,(((8,10),(6,(11,15))),((5,((2,9),3)),(((7,13),4),14))),1); tree rep.3040000 = (((((4,(7,13)),14),(5,(3,(9,2)))),((10,8),(15,(6,11)))),12,1); tree rep.3050000 = ((((13,7),4),(5,((((11,15),6),(8,10)),(((2,9),3),14)))),12,1); tree rep.3060000 = (12,(((10,((11,15),6)),8),(4,(((7,13),14),(5,(3,(2,9)))))),1); tree rep.3070000 = ((((6,(15,11)),(8,10)),(((5,(14,4)),(7,13)),((2,9),3))),12,1); tree rep.3080000 = (12,((14,(((5,7),(13,4)),(3,(9,2)))),((10,(6,(15,11))),8)),1); tree rep.3090000 = ((((4,(7,((14,(3,(9,2))),13))),5),(10,((6,(11,15)),8))),12,1); tree rep.3100000 = ((((((7,13),4),(5,14)),((2,9),3)),((((15,6),11),10),8)),12,1); tree rep.3110000 = (((((7,13),((((15,11),6),(10,8)),4)),14),(((9,2),3),5)),12,1); tree rep.3120000 = (((8,(10,(15,(11,6)))),((((2,9),3),5),(4,(14,(7,13))))),12,1); tree rep.3130000 = (((((((13,7),14),4),(3,(9,2))),5),(((11,(6,15)),10),8)),12,1); tree rep.3140000 = (((((9,2),3),((7,13),4)),((((15,11),6),(8,10)),12)),(5,14),1); tree rep.3150000 = ((((14,(7,13)),4),(3,(9,2))),((8,(10,(11,(15,6)))),(5,12)),1); tree rep.3160000 = ((14,(5,(((3,(9,2)),(7,(4,13))),((10,8),((11,15),6))))),12,1); tree rep.3170000 = (12,(((8,10),(6,(11,15))),((5,(14,((13,7),4))),((9,2),3))),1); tree rep.3180000 = ((((9,2),3),(((4,14),(7,13)),5)),(((11,(6,15)),(10,8)),12),1); tree rep.3190000 = (12,((((9,2),3),((4,14),((13,7),5))),((6,(11,15)),(10,8))),1); tree rep.3200000 = ((((3,(9,2)),(((6,15),11),(8,10))),((4,(7,13)),(14,5))),12,1); tree rep.3210000 = (12,((((3,(9,2)),(((7,13),14),4)),5),((8,10),(11,(6,15)))),1); tree rep.3220000 = (((10,8),((11,15),6)),((((3,(9,2)),((4,(13,7)),14)),5),12),1); tree rep.3230000 = (((((15,6),11),(10,8)),(((2,9),(((13,7),4),14)),(5,3))),12,1); tree rep.3240000 = (((5,(8,(10,((6,15),11)))),((3,(9,2)),(14,((13,7),4)))),12,1); tree rep.3250000 = ((12,(((14,((13,7),4)),5),(3,(9,2)))),((10,8),(11,(15,6))),1); tree rep.3260000 = ((((((((9,2),3),5),(4,(13,7))),14),(11,(15,6))),(8,10)),12,1); tree rep.3270000 = (12,((((4,(13,7)),((9,2),3)),(5,14)),((11,(6,15)),(8,10))),1); tree rep.3280000 = (((8,10),((11,15),6)),(((3,(9,2)),((5,14),((13,7),4))),12),1); tree rep.3290000 = ((((14,((13,7),4)),5),((2,9),3)),(12,((11,(6,15)),(10,8))),1); tree rep.3300000 = (((((9,2),3),(5,((4,(13,7)),14))),((6,(11,15)),(8,10))),12,1); tree rep.3310000 = (12,((10,8),(((((4,5),14),(7,13)),(3,(9,2))),((15,11),6))),1); tree rep.3320000 = (((14,((13,7),4)),((((10,8),(6,(11,15))),(3,(9,2))),5)),12,1); tree rep.3330000 = (12,(((((5,((((13,7),4),14),(9,2))),3),10),8),((15,11),6)),1); tree rep.3340000 = ((((14,5),((7,13),4)),(3,((9,2),12))),(((6,15),11),(10,8)),1); tree rep.3350000 = (12,((((9,2),3),((5,14),((13,7),4))),(((6,(15,11)),10),8)),1); tree rep.3360000 = (12,((((((6,15),11),((7,13),(4,14))),(3,(2,9))),5),(10,8)),1); tree rep.3370000 = (((3,(9,2)),(14,(((13,7),5),(4,(((15,11),6),(8,10)))))),12,1); tree rep.3380000 = ((((14,5),((3,(9,2)),((7,13),4))),(10,(8,((6,15),11)))),12,1); tree rep.3390000 = (12,(((10,((15,11),6)),8),((3,((9,2),5)),(14,(4,(13,7))))),1); tree rep.3400000 = (((((4,(13,7)),5),14),((10,(((11,15),6),8)),((2,9),3))),12,1); tree rep.3410000 = (((5,(((2,9),3),(((13,7),4),14))),((11,(6,15)),(8,10))),12,1); tree rep.3420000 = (12,(((14,(5,((13,7),4))),((9,2),3)),(((11,(15,6)),8),10)),1); tree rep.3430000 = (12,((((6,(15,11)),8),10),(((3,(9,2)),5),((4,14),(13,7)))),1); tree rep.3440000 = ((((10,8),(11,(15,6))),(((7,13),4),(14,((3,(9,2)),5)))),12,1); tree rep.3450000 = (12,(((5,(3,(2,9))),(((4,13),7),14)),((8,10),((15,11),6))),1); tree rep.3460000 = ((((6,(11,15)),(8,10)),(((((13,4),7),14),(3,(2,9))),5)),12,1); tree rep.3470000 = ((((10,(6,(11,15))),8),((((13,7),(14,4)),((2,9),3)),5)),12,1); tree rep.3480000 = (((3,(2,9)),12),(((4,((13,7),14)),5),((10,(6,(11,15))),8)),1); tree rep.3490000 = (12,((((9,2),3),(5,(4,(14,(7,13))))),(10,(8,((11,15),6)))),1); tree rep.3500000 = (12,(((8,10),((6,15),11)),((3,(9,2)),((5,(4,(7,13))),14))),1); tree rep.3510000 = (12,((5,(3,(2,9))),((4,(14,(7,13))),(8,(10,((11,15),6))))),1); tree rep.3520000 = (((5,((3,(9,2)),(14,((7,4),13)))),(10,(((11,15),6),8))),12,1); tree rep.3530000 = ((((8,10),((15,11),6)),12),((5,(4,((7,13),14))),(3,(9,2))),1); tree rep.3540000 = (12,(((((2,9),3),5),(14,((7,13),4))),(10,(8,(11,(15,6))))),1); tree rep.3550000 = (((((5,14),(4,(13,7))),((9,2),3)),((8,((6,15),11)),10)),12,1); tree rep.3560000 = ((((10,(11,(15,6))),8),(4,(((13,7),(3,(2,9))),(14,5)))),12,1); tree rep.3570000 = (((3,(2,9)),(5,(14,(13,(4,7))))),((((15,(6,11)),8),10),12),1); tree rep.3580000 = (((((9,2),3),(((4,(7,13)),((8,((15,11),6)),10)),5)),14),12,1); tree rep.3590000 = (12,(((5,((4,(13,7)),14)),((2,9),3)),(10,(((6,15),11),8))),1); tree rep.3600000 = (12,((((14,5),(4,(13,7))),((2,9),3)),((((6,15),11),8),10)),1); tree rep.3610000 = ((((((11,15),6),10),8),((5,(3,(2,9))),((4,(13,7)),14))),12,1); tree rep.3620000 = (12,(((10,8),(6,(11,15))),(((7,13),14),(((9,2),3),(5,4)))),1); tree rep.3630000 = (12,(((((13,7),(4,14)),5),(8,(10,((6,15),11)))),(3,(2,9))),1); tree rep.3640000 = ((((6,(15,11)),(10,8)),(4,(((13,7),(5,14)),(3,(2,9))))),12,1); tree rep.3650000 = (12,((14,(((10,8),(11,(15,6))),((7,13),5))),((3,(2,9)),4)),1); tree rep.3660000 = ((((((6,15),11),10),8),((5,(((13,7),4),14)),(3,(9,2)))),12,1); tree rep.3670000 = ((((15,6),11),(10,(8,(5,((((7,13),14),4),(3,(9,2))))))),12,1); tree rep.3680000 = ((8,10),((5,((((9,2),3),12),(14,((13,7),4)))),((15,6),11)),1); tree rep.3690000 = (((5,((((13,7),4),(3,(9,2))),14)),((10,8),((6,15),11))),12,1); tree rep.3700000 = (((((3,(9,2)),((5,14),(7,13))),4),((10,8),(6,(15,11)))),12,1); tree rep.3710000 = (12,((((11,(6,15)),8),10),(5,((((7,13),4),14),((9,2),3)))),1); tree rep.3720000 = ((((13,4),((7,((2,9),3)),(14,5))),((10,8),(11,(15,6)))),12,1); tree rep.3730000 = (12,((((15,11),6),(10,8)),((14,(5,(13,(7,4)))),(3,(2,9)))),1); tree rep.3740000 = (((5,((14,((13,7),4)),(3,(9,2)))),((10,8),(11,(6,15)))),12,1); tree rep.3750000 = (12,((((13,7),(5,((9,2),3))),(14,4)),((11,(6,15)),(10,8))),1); tree rep.3760000 = (((((4,(7,13)),14),((3,(9,2)),5)),((8,10),(11,(6,15)))),12,1); tree rep.3770000 = (13,(7,((3,(2,9)),(14,(((12,((10,8),((15,6),11))),4),5)))),1); tree rep.3780000 = (((((2,9),3),(13,(4,7))),(14,5)),(12,(((11,(15,6)),10),8)),1); tree rep.3790000 = (12,(((((4,(13,7)),14),(5,(3,(2,9)))),(11,(15,6))),(10,8)),1); tree rep.3800000 = ((((3,(2,9)),(((4,13),7),(5,14))),12),(10,((6,(11,15)),8)),1); tree rep.3810000 = (12,((8,(10,(6,(11,15)))),(((2,9),3),((4,((13,7),14)),5))),1); tree rep.3820000 = (((5,(((14,(13,7)),4),((9,2),3))),((8,10),(11,(15,6)))),12,1); tree rep.3830000 = ((((10,8),((6,15),11)),((4,(13,7)),(((2,9),3),(14,5)))),12,1); tree rep.3840000 = (12,(((((9,2),(14,5)),(((11,(15,6)),10),8)),(4,(7,13))),3),1); tree rep.3850000 = (12,(((14,5),(3,((9,2),(4,(7,13))))),(((15,11),6),(10,8))),1); tree rep.3860000 = (12,(((10,8),((15,11),6)),(((14,(4,(7,13))),5),((2,9),3))),1); tree rep.3870000 = (12,(((14,((13,7),4)),(5,((2,9),3))),(((11,(15,6)),8),10)),1); tree rep.3880000 = ((((((8,10),(11,(15,6))),5),(14,(4,(13,7)))),((2,9),3)),12,1); tree rep.3890000 = (((((11,(15,6)),(8,10)),((9,2),3)),((14,4),((13,7),5))),12,1); tree rep.3900000 = (((((6,11),15),(10,8)),((5,((4,(13,7)),14)),(3,(9,2)))),12,1); tree rep.3910000 = (((5,(((8,10),(11,(15,6))),(((2,9),3),((7,4),13)))),14),12,1); tree rep.3920000 = (12,(((((15,6),11),10),8),((((7,13),4),(5,14)),((9,2),3))),1); tree rep.3930000 = (12,((9,2),((((10,8),(11,(15,6))),(3,5)),((14,(13,7)),4))),1); tree rep.3940000 = (12,(((3,((2,9),5)),(4,((7,14),13))),(((11,(15,6)),8),10)),1); tree rep.3950000 = (12,(((10,(11,(15,6))),8),(((13,(7,(14,4))),(3,(2,9))),5)),1); tree rep.3960000 = (12,((((((13,7),4),(5,14)),3),(9,2)),((8,10),((11,15),6))),1); tree rep.3970000 = ((((14,(4,(13,7))),((3,(9,2)),5)),((((6,15),11),8),10)),12,1); tree rep.3980000 = (((((14,5),((7,13),4)),(3,(2,9))),((10,8),((11,15),6))),12,1); tree rep.3990000 = (((5,(14,((13,7),4))),(((2,9),3),12)),(10,(8,(11,(6,15)))),1); tree rep.4000000 = (12,((4,(((7,13),((2,9),3)),(14,5))),(((11,(6,15)),10),8)),1); tree rep.4010000 = ((12,(14,(5,((3,(2,9)),((7,13),4))))),(((15,11),6),(8,10)),1); tree rep.4020000 = (12,(((14,((5,(3,(9,2))),(7,13))),4),(((6,(15,11)),8),10)),1); tree rep.4030000 = (12,((((10,8),(6,(15,11))),5),(((9,2),3),(14,(4,(7,13))))),1); tree rep.4040000 = (((((14,5),((9,2),3)),((13,7),4)),12),((10,8),(6,(11,15))),1); tree rep.4050000 = (12,(((14,(((11,15),6),(10,8))),(7,13)),((5,(3,(2,9))),4)),1); tree rep.4060000 = ((((4,(((14,13),7),5)),(3,(9,2))),(((11,(15,6)),8),10)),12,1); tree rep.4070000 = (12,(((5,((7,(4,13)),14)),((9,2),3)),((6,(11,15)),(8,10))),1); tree rep.4080000 = (12,((((((15,11),6),(8,10)),((3,(9,2)),((13,7),4))),5),14),1); tree rep.4090000 = ((((((13,7),5),4),((3,14),(9,2))),(8,(10,(11,(6,15))))),12,1); tree rep.4100000 = (12,(((((4,((3,(9,2)),5)),((7,13),14)),10),8),((11,15),6)),1); tree rep.4110000 = (((((15,11),6),10),8),(((5,(14,(4,(7,13)))),(3,(9,2))),12),1); tree rep.4120000 = (12,((((11,(6,15)),10),8),((((9,2),3),(14,((7,13),4))),5)),1); tree rep.4130000 = (12,((((2,9),3),(8,(((6,15),11),10))),(5,((4,14),(13,7)))),1); tree rep.4140000 = ((((13,7),4),((12,(3,(2,9))),(5,14))),(10,((6,(15,11)),8)),1); tree rep.4150000 = ((((14,((13,7),4)),((9,2),3)),5),(8,((6,(15,11)),(10,12))),1); tree rep.4160000 = (((13,7),4),(((14,5),((2,9),(12,3))),((11,(15,6)),(10,8))),1); tree rep.4170000 = ((5,((3,(2,9)),((14,(13,7)),4))),(12,((6,(15,11)),(10,8))),1); tree rep.4180000 = ((((2,9),3),(((4,(13,7)),14),((10,(8,((15,11),6))),5))),12,1); tree rep.4190000 = ((((14,5),((7,13),4)),(3,(9,2))),((((15,6),11),(8,10)),12),1); tree rep.4200000 = (12,((((8,10),(6,(11,15))),(3,(2,9))),((((13,7),14),4),5)),1); tree rep.4210000 = (12,((((10,8),(6,(15,11))),(14,(4,((7,13),(3,(2,9)))))),5),1); tree rep.4220000 = (((8,(((15,11),6),10)),((5,((7,13),4)),(14,((9,2),3)))),12,1); tree rep.4230000 = (12,(((5,((2,9),3)),(14,((13,7),4))),((8,(11,(15,6))),10)),1); tree rep.4240000 = (((((4,(7,13)),((9,2),3)),(14,5)),((10,8),(11,(6,15)))),12,1); tree rep.4250000 = (12,((((3,(9,2)),5),(14,(4,(7,13)))),(((15,6),11),(8,10))),1); tree rep.4260000 = (((8,(6,(11,15))),10),(12,(4,((7,13),((5,14),(3,(2,9)))))),1); tree rep.4270000 = (12,((((3,(9,2)),5),((13,7),(14,4))),(((15,6),11),(10,8))),1); tree rep.4280000 = ((((((15,6),11),10),8),(((7,13),((2,9),3)),(4,(14,5)))),12,1); tree rep.4290000 = ((((((2,9),3),14),(5,((13,7),4))),(8,((6,(15,11)),10))),12,1); tree rep.4300000 = (12,(((8,10),(11,(6,15))),(((((2,9),(3,(13,7))),5),14),4)),1); tree rep.4310000 = (12,(((5,((4,(7,13)),14)),(3,(9,2))),((10,(11,(15,6))),8)),1); tree rep.4320000 = (((8,(10,(6,(11,15)))),((((9,2),3),(4,(5,(7,13)))),14)),12,1); tree rep.4330000 = ((((8,10),((6,15),11)),(((5,(3,(9,2))),((4,7),13)),14)),12,1); tree rep.4340000 = (12,(((3,(2,9)),(((13,7),5),(4,14))),(8,(10,(11,(15,6))))),1); tree rep.4350000 = ((((5,(3,(2,9))),(14,(4,(13,7)))),12),((8,((6,15),11)),10),1); tree rep.4360000 = ((((10,8),((15,6),11)),((((14,4),(13,7)),5),(3,(2,9)))),12,1); tree rep.4370000 = (12,(((5,(3,(2,9))),(4,((13,7),14))),(((15,6),11),(8,10))),1); tree rep.4380000 = (((((13,(7,4)),14),(((2,9),3),5)),12),((((15,6),11),10),8),1); tree rep.4390000 = (12,((((15,11),6),(10,8)),((((9,2),3),((4,14),(7,13))),5)),1); tree rep.4400000 = (12,(((4,(14,5)),(((9,2),3),(7,13))),(10,(8,(6,(15,11))))),1); tree rep.4410000 = ((((((5,14),(13,7)),((9,2),3)),4),(8,(10,(11,(15,6))))),12,1); tree rep.4420000 = (12,(((8,10),(6,(11,15))),((4,((7,13),14)),(5,(3,(2,9))))),1); tree rep.4430000 = (((((15,6),11),(10,8)),(((5,((9,2),3)),(4,(7,13))),14)),12,1); tree rep.4440000 = (((((2,9),3),(5,(14,(13,(7,4))))),(10,(8,(11,(15,6))))),12,1); tree rep.4450000 = (((((6,(11,15)),10),8),(((3,(2,9)),((5,4),14)),(13,7))),12,1); tree rep.4460000 = ((12,((8,10),((11,6),15))),((5,(14,(4,(7,13)))),((9,2),3)),1); tree rep.4470000 = (12,((((11,15),6),(10,8)),(((3,(2,9)),((4,14),(7,13))),5)),1); tree rep.4480000 = ((((((10,((6,15),11)),8),((13,7),4)),5),((3,(9,2)),14)),12,1); tree rep.4490000 = (((6,(11,15)),(8,10)),(12,((4,(13,7)),((14,5),((9,2),3)))),1); tree rep.4500000 = ((((((15,11),6),10),8),(((4,(7,13)),14),(((2,9),3),5))),12,1); tree rep.4510000 = (((3,(2,9)),((5,14),((7,13),4))),(12,((8,((11,15),6)),10)),1); tree rep.4520000 = (12,((10,((11,(6,15)),8)),(((14,(13,7)),4),((3,(9,2)),5))),1); tree rep.4530000 = (12,(((8,((6,15),11)),10),((4,(5,(14,(3,(2,9))))),(7,13))),1); tree rep.4540000 = ((((8,10),((15,6),11)),((14,((7,13),4)),(5,(3,(2,9))))),12,1); tree rep.4550000 = (12,((((14,(4,(13,7))),(3,(9,2))),5),((((11,6),15),10),8)),1); tree rep.4560000 = (12,((((6,15),11),(10,8)),((((2,9),3),((7,13),4)),(5,14))),1); tree rep.4570000 = (12,((((7,13),((2,9),3)),((4,14),5)),(((15,11),6),(10,8))),1); tree rep.4580000 = ((12,(((4,((9,2),3)),5),(14,(7,13)))),((8,10),((11,15),6)),1); tree rep.4590000 = (((5,((14,(4,(7,13))),(3,(9,2)))),12),((10,8),(11,(6,15))),1); tree rep.4600000 = (12,((8,10),(((14,5),(4,(13,7))),((2,9),((11,(6,15)),3)))),1); tree rep.4610000 = (12,((((4,14),(13,7)),(5,((9,2),3))),((8,10),(6,(11,15)))),1); tree rep.4620000 = (((((4,(7,13)),(14,5)),((9,2),3)),(((11,(15,6)),10),8)),12,1); tree rep.4630000 = (12,((((11,15),6),(10,8)),((((4,(13,7)),14),5),((9,2),3))),1); tree rep.4640000 = (12,((((3,(9,2)),5),((14,4),(7,13))),(((15,11),6),(10,8))),1); tree rep.4650000 = (12,(((2,9),3),((13,7),((14,4),(((10,8),(11,(15,6))),5)))),1); tree rep.4660000 = ((((14,(4,(7,13))),((5,(9,2)),3)),12),((10,8),((15,6),11)),1); tree rep.4670000 = (12,((((3,9),2),(5,(((7,13),4),14))),((10,8),((6,15),11))),1); tree rep.4680000 = ((7,(4,13)),(14,((((10,(11,(6,15))),8),12),(5,((2,9),3)))),1); tree rep.4690000 = (12,(((15,11),6),((((3,(9,2)),(5,((4,(13,7)),14))),10),8)),1); tree rep.4700000 = (12,((10,(((11,15),6),8)),(5,((3,(2,9)),((4,(13,7)),14)))),1); tree rep.4710000 = (12,((4,(7,13)),(((((10,(11,(6,15))),8),14),5),((9,2),3))),1); tree rep.4720000 = (((((3,(2,9)),5),(14,(4,(13,7)))),(8,((15,(11,6)),10))),12,1); tree rep.4730000 = (12,(((((3,(9,2)),(14,(7,13))),5),(10,(8,(6,(15,11))))),4),1); tree rep.4740000 = ((((4,((7,13),14)),5),(((9,2),3),12)),(8,((11,(6,15)),10)),1); tree rep.4750000 = (((((15,11),6),(10,8)),((5,((((9,2),3),14),(13,7))),4)),12,1); tree rep.4760000 = ((((8,(6,(15,11))),10),((14,(5,(3,(9,2)))),(4,(13,7)))),12,1); tree rep.4770000 = (12,((8,((6,(15,11)),10)),((((13,7),14),4),(5,(3,(2,9))))),1); tree rep.4780000 = ((5,(((4,(13,7)),((((15,6),11),(8,10)),12)),14)),(3,(9,2)),1); tree rep.4790000 = (((((4,14),(13,7)),(5,(3,(2,9)))),((8,10),(11,(6,15)))),12,1); tree rep.4800000 = (12,((5,(((((6,(11,15)),10),8),(3,(9,2))),(4,(13,7)))),14),1); tree rep.4810000 = (((10,((6,(11,15)),8)),(((5,4),(((2,9),(7,13)),3)),14)),12,1); tree rep.4820000 = ((((6,11),15),(10,8)),((((4,(14,(13,7))),5),((2,9),3)),12),1); tree rep.4830000 = ((((14,((13,7),((3,(9,2)),5))),4),12),(10,((6,(11,15)),8)),1); tree rep.4840000 = ((((((4,(7,13)),14),(3,(9,2))),5),12),(10,(((6,15),11),8)),1); tree rep.4850000 = (12,(((6,(11,15)),(10,8)),(((2,9),3),(5,((4,(13,7)),14)))),1); tree rep.4860000 = (5,((((9,2),3),(14,(4,(13,7)))),(12,((((6,15),11),10),8))),1); tree rep.4870000 = ((((10,8),(11,(6,15))),(5,(((2,9),3),(4,((7,13),14))))),12,1); tree rep.4880000 = (12,((((3,(2,9)),(((7,13),4),5)),14),((10,(11,(6,15))),8)),1); tree rep.4890000 = (12,(((8,10),((11,15),6)),((4,((13,14),7)),(((2,9),3),5))),1); tree rep.4900000 = ((((5,((4,(13,7)),14)),((9,2),3)),((8,10),((15,11),6))),12,1); tree rep.4910000 = (12,(((2,9),(3,(((4,14),5),(13,7)))),((10,((15,6),11)),8)),1); tree rep.4920000 = (12,(((10,((11,15),6)),8),((((9,2),(13,7)),((5,14),4)),3)),1); tree rep.4930000 = (12,(((((15,11),6),10),8),(((4,((9,2),3)),((7,13),14)),5)),1); tree rep.4940000 = (((((4,5),((9,2),3)),((13,7),14)),((8,10),(6,(15,11)))),12,1); tree rep.4950000 = ((((3,((9,2),(14,5))),(4,(7,13))),(((11,15),6),(10,8))),12,1); tree rep.4960000 = ((8,(10,((6,15),11))),(12,(4,((5,(14,(7,13))),(3,(9,2))))),1); tree rep.4970000 = (12,(5,((((4,(13,7)),14),((9,2),3)),(((15,6),11),(10,8)))),1); tree rep.4980000 = (12,((((6,(15,11)),8),10),((((13,7),(4,14)),5),((9,2),3))),1); tree rep.4990000 = ((((4,((9,2),3)),((7,13),(5,14))),(8,((15,(11,6)),10))),12,1); tree rep.5000000 = (12,(((10,8),((11,6),15)),(((7,13),4),(((9,2),3),(14,5)))),1); tree rep.5010000 = ((((3,(9,2)),(4,((7,13),(5,14)))),((10,8),(11,(15,6)))),12,1); tree rep.5020000 = (12,(((5,((3,(2,9)),((7,13),4))),14),((8,((6,15),11)),10)),1); tree rep.5030000 = (((((4,(7,13)),(5,14)),((2,9),3)),(8,((11,(6,15)),10))),12,1); tree rep.5040000 = (((8,10),(11,(6,15))),(12,((((7,13),(14,4)),(3,(9,2))),5)),1); tree rep.5050000 = (12,(((11,(15,6)),(10,8)),((7,13),(4,((14,5),(3,(9,2)))))),1); tree rep.5060000 = (12,((((5,14),((4,(13,7)),(2,9))),3),(((11,(15,6)),8),10)),1); tree rep.5070000 = (((5,((7,13),((14,4),((9,2),3)))),(10,(8,(6,(15,11))))),12,1); tree rep.5080000 = (12,(((14,(((7,13),5),4)),((2,9),3)),((10,8),((15,11),6))),1); tree rep.5090000 = (12,(((8,(6,(15,11))),10),((3,(2,9)),((13,((7,14),4)),5))),1); tree rep.5100000 = (((8,(((15,11),6),10)),(5,(((2,9),3),(14,(4,(7,13)))))),12,1); tree rep.5110000 = ((((9,3),2),((((7,13),4),14),(((8,10),((6,11),15)),5))),12,1); tree rep.5120000 = (12,(((((8,10),((15,6),11)),((2,9),3)),(4,(7,13))),(14,5)),1); tree rep.5130000 = (12,(((((8,10),((6,15),11)),5),14),(((13,7),4),((2,9),3))),1); tree rep.5140000 = ((9,2),((12,((14,((4,(7,13)),(8,(10,(11,(6,15)))))),5)),3),1); tree rep.5150000 = ((((6,(15,11)),(10,8)),((3,(9,2)),(5,(14,((7,4),13))))),12,1); tree rep.5160000 = ((((5,(3,(2,9))),(((13,7),4),14)),(((11,(15,6)),8),10)),12,1); tree rep.5170000 = (((5,((3,(9,2)),(((15,6),11),(10,8)))),(((7,13),14),4)),12,1); tree rep.5180000 = (5,((((8,((15,6),11)),10),12),((3,(2,9)),(14,((7,13),4)))),1); tree rep.5190000 = ((((11,(6,15)),(8,10)),((3,(9,2)),(5,((4,14),(13,7))))),12,1); tree rep.5200000 = ((((((15,6),11),10),8),((3,((13,(7,4)),(5,14))),(2,9))),12,1); tree rep.5210000 = (((((7,13),((14,5),(3,(9,2)))),4),(8,((11,(15,6)),10))),12,1); tree rep.5220000 = (12,(((((15,6),11),8),10),((((9,2),3),14),(((13,7),4),5))),1); tree rep.5230000 = (12,((((6,(11,15)),8),10),((9,2),((3,5),((7,13),(4,14))))),1); tree rep.5240000 = ((((4,(7,13)),(((9,2),3),5)),14),((6,(11,15)),(12,(10,8))),1); tree rep.5250000 = (12,(((8,10),((6,15),11)),((14,((13,7),(5,4))),(9,(3,2)))),1); tree rep.5260000 = (12,(((((7,13),4),14),((3,(2,9)),5)),((15,(11,6)),(8,10))),1); tree rep.5270000 = ((12,(((14,4),(13,7)),(5,(3,(2,9))))),((10,8),(6,(15,11))),1); tree rep.5280000 = ((((10,((15,6),11)),8),(((5,(14,4)),(13,7)),((9,2),3))),12,1); tree rep.5290000 = ((12,((((2,9),3),5),(14,((13,4),7)))),((10,(11,(6,15))),8),1); tree rep.5300000 = (((((11,(15,6)),8),10),(5,((4,((2,9),3)),((13,7),14)))),12,1); tree rep.5310000 = (((((15,6),11),(8,10)),(5,((((3,(9,2)),14),4),(13,7)))),12,1); tree rep.5320000 = ((((7,13),(14,((5,4),((10,8),((15,11),6))))),(3,(2,9))),12,1); tree rep.5330000 = (((10,(8,(11,(15,6)))),(((7,13),(9,2)),(3,(14,(4,5))))),12,1); tree rep.5340000 = (12,((10,(8,((6,15),11))),(5,((4,(13,7)),(((9,2),3),14)))),1); tree rep.5350000 = (12,((((14,(4,(13,7))),(3,(2,9))),5),(((11,15),6),(10,8))),1); tree rep.5360000 = ((((((((15,11),6),(10,8)),((7,13),4)),(3,(9,2))),14),5),12,1); tree rep.5370000 = (4,(((5,(7,13)),((10,(((11,15),6),8)),(12,(3,(2,9))))),14),1); tree rep.5380000 = (((5,(((9,2),3),(((7,13),4),14))),((6,(15,11)),(10,8))),12,1); tree rep.5390000 = (12,((((4,(7,13)),14),((3,(2,9)),5)),(10,((11,(6,15)),8))),1); tree rep.5400000 = ((((((14,(7,13)),4),(3,(9,2))),5),((10,(6,(11,15))),8)),12,1); tree rep.5410000 = (12,(((10,8),((15,6),11)),(5,(((4,(7,13)),14),((2,9),3)))),1); tree rep.5420000 = ((((3,(2,9)),(14,(4,((7,13),5)))),((8,10),((11,15),6))),12,1); tree rep.5430000 = ((((6,(11,15)),(((((13,7),4),14),5),(3,(9,2)))),(8,10)),12,1); tree rep.5440000 = ((((10,(11,(15,6))),8),((((9,2),(4,(13,7))),3),(14,5))),12,1); tree rep.5450000 = (((5,4),(((13,7),(((10,8),(11,(6,15))),(3,(9,2)))),14)),12,1); tree rep.5460000 = ((8,(((6,15),11),10)),((((14,4),(7,13)),5),(12,(3,(2,9)))),1); tree rep.5470000 = (12,((((15,11),6),(10,8)),((14,(4,(5,(7,13)))),((9,2),3))),1); tree rep.5480000 = (((6,(15,11)),(10,8)),(12,((5,(14,4)),(((2,9),3),(7,13)))),1); tree rep.5490000 = (((10,(8,((11,15),6))),(((4,14),(13,7)),(((2,9),5),3))),12,1); tree rep.5500000 = ((((((7,13),(4,14)),5),((2,9),3)),((8,10),(15,(11,6)))),12,1); tree rep.5510000 = (12,((((((3,(2,9)),14),(7,13)),4),5),((10,8),(6,(11,15)))),1); tree rep.5520000 = (12,((((6,(15,11)),10),8),((14,5),((3,(2,9)),((13,4),7)))),1); tree rep.5530000 = (((((13,7),4),((5,(3,(2,9))),14)),(8,((6,(11,15)),10))),12,1); tree rep.5540000 = (12,((((10,(15,(6,11))),8),(4,(((9,2),3),(14,(7,13))))),5),1); tree rep.5550000 = (((((7,13),4),(((2,9),3),(5,14))),(10,(((15,11),6),8))),12,1); tree rep.5560000 = (12,((10,(8,((15,11),6))),(((3,(2,9)),(5,14)),((13,7),4))),1); tree rep.5570000 = (((((10,8),(6,(15,11))),3),(5,(((7,13),4),((9,2),14)))),12,1); tree rep.5580000 = (((((2,9),3),((((8,((6,15),11)),10),(14,(13,7))),4)),5),12,1); tree rep.5590000 = (12,(((3,(2,9)),(((14,4),(7,13)),5)),(((11,(6,15)),10),8)),1); tree rep.5600000 = (12,((((11,15),6),(10,8)),(5,(((7,13),(14,4)),(3,(2,9))))),1); tree rep.5610000 = ((((14,((13,7),4)),(5,(3,(2,9)))),(10,(8,(6,(11,15))))),12,1); tree rep.5620000 = ((((10,8),((6,15),11)),(((5,(4,(7,13))),14),((2,9),3))),12,1); tree rep.5630000 = (12,((((11,(15,6)),10),8),(((13,7),4),((3,(9,2)),(5,14)))),1); tree rep.5640000 = ((((6,(15,11)),(8,10)),(5,(((13,7),(14,4)),((9,2),3)))),12,1); tree rep.5650000 = (12,(((3,(2,9)),((7,13),((14,4),5))),((10,8),((11,15),6))),1); tree rep.5660000 = (((((4,(7,13)),14),(((2,9),3),5)),(8,(((6,15),11),10))),12,1); tree rep.5670000 = ((12,(5,(((9,2),3),((4,14),(7,13))))),(10,(8,((11,15),6))),1); tree rep.5680000 = ((((6,(11,15)),(10,8)),(5,((((13,7),4),14),(3,(9,2))))),12,1); tree rep.5690000 = (12,(((((7,13),4),14),(((2,9),3),5)),(((6,(15,11)),8),10)),1); tree rep.5700000 = ((14,(((5,((13,7),4)),12),((9,2),3))),(10,(8,(6,(15,11)))),1); tree rep.5710000 = (12,((5,(((7,(14,13)),4),((9,2),3))),((10,8),((15,6),11))),1); tree rep.5720000 = ((((13,7),4),14),(((9,2),3),(((((15,11),6),(8,10)),12),5)),1); tree rep.5730000 = (12,((((5,14),((13,7),4)),(3,(2,9))),(((11,(6,15)),10),8)),1); tree rep.5740000 = (((((7,13),14),((4,(3,(2,9))),5)),((8,10),((15,6),11))),12,1); tree rep.5750000 = (((((2,9),(5,3)),(4,(14,(7,13)))),(10,(((15,11),6),8))),12,1); tree rep.5760000 = ((((8,(11,(15,6))),10),((5,14),((3,(9,2)),(4,(7,13))))),12,1); tree rep.5770000 = (12,((((11,(15,6)),8),10),((((2,9),3),(((4,7),13),14)),5)),1); tree rep.5780000 = (((10,(8,((11,15),6))),((4,((7,13),(14,5))),(3,(9,2)))),12,1); tree rep.5790000 = ((((4,(7,13)),(14,((3,(2,9)),5))),((10,((15,11),6)),8)),12,1); tree rep.5800000 = ((((3,((4,(13,7)),14)),(5,(2,9))),(8,(10,((15,6),11)))),12,1); tree rep.5810000 = (12,(((8,((11,15),6)),10),(((7,13),4),((((2,9),3),14),5))),1); tree rep.5820000 = ((((3,(2,9)),12),((10,8),(6,(15,11)))),(((14,5),4),(7,13)),1); tree rep.5830000 = (((10,8),(11,(6,15))),(12,(((14,5),((4,13),7)),(3,(2,9)))),1); tree rep.5840000 = ((((13,7),(5,(4,(14,(3,(2,9)))))),(10,(((11,15),6),8))),12,1); tree rep.5850000 = (((((3,(9,2)),5),((6,(15,11)),(8,10))),((14,(13,7)),4)),12,1); tree rep.5860000 = (12,((5,(((4,(7,13)),14),((2,9),3))),(10,(((6,11),15),8))),1); tree rep.5870000 = ((5,(((9,2),3),(((7,13),4),14))),(12,((6,(15,11)),(10,8))),1); tree rep.5880000 = (((((2,9),3),(((7,14),(13,4)),5)),(8,(10,(11,(15,6))))),12,1); tree rep.5890000 = (((((6,15),11),10),8),(((14,5),(3,((9,2),12))),((13,7),4)),1); tree rep.5900000 = ((((11,6),15),((5,(((9,2),3),(14,((13,7),4)))),(8,10))),12,1); tree rep.5910000 = ((((8,10),((6,15),11)),(((5,(4,(7,13))),14),((9,2),3))),12,1); tree rep.5920000 = ((((11,(6,15)),(10,8)),((5,((14,(13,7)),4)),(3,(2,9)))),12,1); tree rep.5930000 = ((((4,(3,(2,9))),(((7,13),14),5)),((10,8),((11,15),6))),12,1); tree rep.5940000 = (12,(3,(((5,(9,2)),((13,7),(14,4))),((((15,6),11),10),8))),1); tree rep.5950000 = (((5,14),(((13,7),4),((2,9),3))),(12,((6,(15,11)),(8,10))),1); tree rep.5960000 = (12,(((10,((11,15),6)),8),(((14,5),(4,(13,7))),((9,2),3))),1); tree rep.5970000 = (12,((((9,2),3),((4,(13,7)),(5,14))),((11,(15,6)),(8,10))),1); tree rep.5980000 = (12,(((10,((11,15),6)),8),((7,13),(4,((14,5),(3,(9,2)))))),1); tree rep.5990000 = ((((2,9),3),((5,(14,((10,8),(11,(6,15))))),(4,(13,7)))),12,1); tree rep.6000000 = (12,(((8,10),((15,11),6)),(((2,9),3),((14,((13,7),4)),5))),1); tree rep.6010000 = (12,((10,(8,(15,(6,11)))),((((9,2),3),((13,14),(7,4))),5)),1); tree rep.6020000 = (12,((((5,(2,3)),14),(4,(9,(13,7)))),(8,(((6,15),11),10))),1); tree rep.6030000 = (((((14,(7,13)),4),(5,(3,(2,9)))),((((6,15),11),8),10)),12,1); tree rep.6040000 = ((((4,14),(7,13)),(5,((2,9),3))),((10,((6,(11,15)),8)),12),1); tree rep.6050000 = ((((2,9),(((14,5),(4,(7,13))),3)),(10,((11,(15,6)),8))),12,1); tree rep.6060000 = (12,(((5,((3,(2,9)),(4,(7,13)))),14),((((6,15),11),8),10)),1); tree rep.6070000 = (12,(((3,(9,2)),(((7,13),(4,14)),5)),((15,(11,6)),(10,8))),1); tree rep.6080000 = (((5,(((13,7),(((9,2),3),14)),4)),((8,10),(11,(15,6)))),12,1); tree rep.6090000 = (((8,10),(11,(15,6))),((((9,2),3),(5,((4,(13,7)),14))),12),1); tree rep.6100000 = (12,(((3,(9,2)),((14,5),(4,(7,13)))),(8,(10,(6,(11,15))))),1); tree rep.6110000 = (12,((((9,2),3),(5,(4,((13,7),14)))),((10,8),((11,15),6))),1); tree rep.6120000 = ((((6,(11,15)),10),8),(12,(5,(((2,9),3),(4,(7,(13,14)))))),1); tree rep.6130000 = ((((((13,7),4),((14,5),(3,(2,9)))),8),((6,(15,11)),10)),12,1); tree rep.6140000 = (((8,(((11,15),6),10)),12),((((2,9),3),(14,(4,(13,7)))),5),1); tree rep.6150000 = (12,((((6,15),11),(8,10)),(5,((3,(2,9)),(((13,7),4),14)))),1); tree rep.6160000 = ((10,(((15,11),6),8)),(12,((3,(9,2)),((((13,7),4),5),14))),1); tree rep.6170000 = (((((10,8),(11,(6,15))),(14,((7,13),4))),(((2,9),5),3)),12,1); tree rep.6180000 = (12,((5,(14,(4,(13,7)))),(((6,(15,11)),(10,8)),((9,2),3))),1); tree rep.6190000 = (12,((5,((3,(2,9)),((4,(13,7)),14))),((15,(6,11)),(10,8))),1); tree rep.6200000 = (12,(((8,10),(11,(6,15))),(((3,(9,2)),5),(4,(14,(7,13))))),1); tree rep.6210000 = ((((3,(9,2)),((5,7),((14,4),13))),((8,(11,(15,6))),10)),12,1); tree rep.6220000 = ((((((2,9),3),(14,((13,7),4))),5),((10,8),(11,(6,15)))),12,1); tree rep.6230000 = (((((4,(14,(13,7))),((3,(2,9)),5)),(10,8)),(6,(11,15))),12,1); tree rep.6240000 = ((((11,(6,15)),(8,10)),((((2,9),3),14),(5,(4,(7,13))))),12,1); tree rep.6250000 = ((((6,(15,11)),(10,8)),(14,((((13,7),5),(3,(9,2))),4))),12,1); tree rep.6260000 = (((((15,11),6),(10,8)),((4,((13,7),14)),(5,(3,(9,2))))),12,1); tree rep.6270000 = (12,(((((4,(13,7)),14),5),((9,2),3)),((11,(15,6)),(8,10))),1); tree rep.6280000 = (((10,(((15,11),6),8)),((((2,9),3),((7,13),5)),(14,4))),12,1); tree rep.6290000 = ((((10,8),((11,15),6)),((((13,7),(3,(2,9))),14),(4,5))),12,1); tree rep.6300000 = ((12,((8,10),((6,11),15))),(((3,9),2),(5,(((13,7),4),14))),1); tree rep.6310000 = (((10,(8,((15,6),11))),(4,((((7,13),14),5),(3,(9,2))))),12,1); tree rep.6320000 = (12,(((2,9),3),(5,(4,(((7,13),14),((8,10),((11,6),15)))))),1); tree rep.6330000 = (12,((10,((11,(15,6)),8)),(((7,13),(((9,2),3),(5,14))),4)),1); tree rep.6340000 = (12,(((15,(11,6)),(8,10)),(((14,(13,(4,7))),((2,9),3)),5)),1); tree rep.6350000 = (((((9,2),3),(5,((14,4),(7,13)))),(8,(10,(6,(11,15))))),12,1); tree rep.6360000 = ((((((7,(13,4)),14),((10,8),(6,(15,11)))),5),(3,(9,2))),12,1); tree rep.6370000 = (((10,8),((15,6),11)),(12,(14,((5,4),((3,(9,2)),(7,13))))),1); tree rep.6380000 = ((((((2,9),3),((4,(13,7)),12)),5),14),((8,10),(6,(11,15))),1); tree rep.6390000 = (12,((((11,15),6),(8,10)),((((2,9),3),((4,(7,13)),14)),5)),1); tree rep.6400000 = (12,(((8,(15,(6,11))),10),((((13,7),4),14),(5,(3,(2,9))))),1); tree rep.6410000 = ((((8,10),((6,15),11)),((((9,2),3),((14,5),4)),(7,13))),12,1); tree rep.6420000 = (((10,(8,((11,15),6))),(5,((3,(9,2)),(14,(4,(13,7)))))),12,1); tree rep.6430000 = ((((((4,(7,13)),14),5),(3,(2,9))),((6,(15,11)),(8,10))),12,1); tree rep.6440000 = (12,((((14,((7,13),4)),(3,(2,9))),5),(8,(10,(11,(6,15))))),1); tree rep.6450000 = (12,(((11,(6,15)),(8,10)),(((3,(2,9)),(5,14)),((13,7),4))),1); tree rep.6460000 = (12,((3,(9,2)),(((((6,15),11),8),10),(((4,(13,7)),14),5))),1); tree rep.6470000 = (12,((((((2,9),(4,3)),(7,13)),5),14),(10,(8,((15,11),6)))),1); tree rep.6480000 = ((10,(((6,15),11),8)),(12,(((9,2),3),((14,((7,13),4)),5))),1); tree rep.6490000 = ((((10,8),(6,(11,15))),(((3,(2,9)),5),(14,(4,(13,7))))),12,1); tree rep.6500000 = (12,((10,((11,(15,6)),8)),((14,(4,(13,7))),(5,((2,9),3)))),1); tree rep.6510000 = (12,(((5,(3,(2,9))),((14,(7,13)),4)),((8,(6,(11,15))),10)),1); tree rep.6520000 = (12,((((((9,2),3),14),(4,(7,13))),5),(((6,11),15),(10,8))),1); tree rep.6530000 = ((12,((((2,9),3),((5,14),4)),(13,7))),((6,(11,15)),(10,8)),1); tree rep.6540000 = ((((((4,(13,7)),14),5),((2,9),3)),(8,((6,(11,15)),10))),12,1); tree rep.6550000 = (12,(((2,9),3),(((4,(7,13)),(5,14)),((11,(6,15)),(10,8)))),1); tree rep.6560000 = (12,(((3,(2,9)),(((13,7),4),(5,14))),(8,(10,(6,(15,11))))),1); tree rep.6570000 = (12,(((5,(4,14)),(((11,(15,6)),(8,10)),((2,9),3))),(13,7)),1); tree rep.6580000 = ((((8,((15,6),11)),10),((3,(2,9)),((5,(4,(7,13))),14))),12,1); tree rep.6590000 = (((4,(3,(2,9))),((7,13),((14,(10,(8,((15,6),11)))),5))),12,1); tree rep.6600000 = (12,((((14,(4,(13,7))),5),((2,9),3)),(((6,(15,11)),8),10)),1); tree rep.6610000 = (12,((((7,13),4),(((2,9),3),(14,5))),((8,((11,15),6)),10)),1); tree rep.6620000 = ((((6,15),11),(8,10)),(((3,(9,2)),(5,(((7,13),14),4))),12),1); tree rep.6630000 = (((8,((11,(6,15)),((5,(((13,7),14),4)),(3,(2,9))))),10),12,1); tree rep.6640000 = ((((3,(2,9)),((14,5),4)),(12,(13,7))),((10,8),((6,15),11)),1); tree rep.6650000 = ((5,((((8,(10,((6,11),15))),((2,9),3)),4),(13,(14,7)))),12,1); tree rep.6660000 = ((((10,8),((11,15),6)),(((3,5),((14,(7,13)),4)),(2,9))),12,1); tree rep.6670000 = ((((4,(13,7)),(14,5)),((2,9),3)),((((15,6),11),(8,10)),12),1); tree rep.6680000 = (((((((9,2),3),(((13,7),14),4)),5),(8,10)),(6,(11,15))),12,1); tree rep.6690000 = ((((3,(9,2)),((4,14),(5,(7,13)))),(8,(10,(11,(15,6))))),12,1); tree rep.6700000 = (12,((11,(6,15)),(((3,(((13,7),(14,4)),(9,2))),5),(10,8))),1); tree rep.6710000 = (12,(((8,(6,(15,11))),10),((4,((14,5),(((2,9),3),7))),13)),1); tree rep.6720000 = (12,((((3,(2,9)),((13,7),(14,4))),5),((8,10),((15,6),11))),1); tree rep.6730000 = (12,(((4,(3,(2,9))),((14,5),(7,13))),(10,(((11,15),6),8))),1); tree rep.6740000 = (12,(((8,(6,(11,15))),10),(((4,(13,7)),(((2,9),3),14)),5)),1); tree rep.6750000 = (12,(((((6,15),11),8),10),((14,(5,(4,(7,13)))),((2,9),3))),1); tree rep.6760000 = (12,(((((4,(7,13)),14),5),(3,(2,9))),(10,((11,(15,6)),8))),1); tree rep.6770000 = (((((9,2),3),(((13,7),(5,4)),14)),((8,((15,11),6)),10)),12,1); tree rep.6780000 = (12,(((((13,7),4),(14,5)),(10,(8,(11,(6,15))))),(3,(2,9))),1); tree rep.6790000 = (((((11,(15,6)),10),8),((((14,(13,7)),5),(3,(2,9))),4)),12,1); tree rep.6800000 = (((10,(8,((11,6),15))),((3,(2,9)),(5,(14,(4,(13,7)))))),12,1); tree rep.6810000 = ((((((11,15),6),8),10),(((((13,7),4),14),5),(3,(2,9)))),12,1); tree rep.6820000 = ((((8,(15,(11,6))),10),(((5,((2,9),3)),(4,(7,13))),14)),12,1); tree rep.6830000 = (12,(((6,(11,15)),(8,10)),((((7,13),(4,14)),5),((2,9),3))),1); tree rep.6840000 = (12,(((((6,15),11),8),10),((((7,4),13),(5,((9,2),3))),14)),1); tree rep.6850000 = (((5,(((4,(7,13)),14),(3,(2,9)))),((10,((15,11),6)),8)),12,1); tree rep.6860000 = (12,((((3,(9,2)),(((7,13),14),4)),5),((10,8),((11,15),6))),1); tree rep.6870000 = (((((14,(4,(7,13))),5),((9,2),3)),((((6,15),11),8),10)),12,1); tree rep.6880000 = (12,((((((9,2),3),4),(5,14)),(13,7)),((10,8),((15,11),6))),1); tree rep.6890000 = ((((14,(13,7)),((5,((2,9),3)),4)),((10,8),(15,(11,6)))),12,1); tree rep.6900000 = (12,(((14,(4,(7,13))),((3,(2,9)),5)),(10,(((11,15),6),8))),1); tree rep.6910000 = ((((3,(2,9)),(((4,13),(7,14)),5)),(((11,15),6),(10,8))),12,1); tree rep.6920000 = (((((7,13),4),14),((3,(9,2)),(((((6,11),15),8),10),5))),12,1); tree rep.6930000 = (12,((((2,9),3),(5,(14,((7,13),4)))),((6,(15,11)),(10,8))),1); tree rep.6940000 = (12,((5,(((6,11),15),(10,8))),((3,(9,2)),(14,((13,7),4)))),1); tree rep.6950000 = ((((9,2),3),(((7,13),14),(((8,(((15,11),6),10)),4),5))),12,1); tree rep.6960000 = ((((10,8),((15,6),11)),(5,((((7,13),4),14),((9,2),3)))),12,1); tree rep.6970000 = ((((14,((3,(9,2)),5)),((13,7),4)),(8,(10,(15,(6,11))))),12,1); tree rep.6980000 = ((((5,((9,2),3)),14),(4,(7,13))),(12,(((11,(6,15)),10),8)),1); tree rep.6990000 = ((((8,10),(11,(15,6))),(5,(((2,9),3),(((7,13),4),14)))),12,1); tree rep.7000000 = (12,(((10,8),((11,15),6)),(5,(14,(3,((2,9),((13,7),4)))))),1); tree rep.7010000 = ((4,((5,((6,(15,11)),((8,12),10))),14)),(((9,2),3),(7,13)),1); tree rep.7020000 = (12,((8,(10,(11,(15,6)))),(((4,(13,7)),5),(14,(3,(9,2))))),1); tree rep.7030000 = ((((10,8),((6,15),11)),((4,(7,13)),((14,5),(3,(9,2))))),12,1); tree rep.7040000 = (((10,((6,(11,15)),8)),(((3,(9,2)),(14,(4,(7,13)))),5)),12,1); tree rep.7050000 = (12,((10,(((6,15),11),8)),((5,((9,3),2)),(((4,14),13),7))),1); tree rep.7060000 = (12,(((3,(9,2)),((14,(4,(13,7))),5)),((11,(6,15)),(8,10))),1); tree rep.7070000 = (((((14,4),((7,13),((10,8),((11,15),6)))),5),(3,(9,2))),12,1); tree rep.7080000 = (((5,14),((13,7),((12,((6,(11,15)),(8,10))),(3,(2,9))))),4,1); tree rep.7090000 = (12,((8,(10,(11,(15,6)))),((5,(((13,7),4),14)),(3,(2,9)))),1); tree rep.7100000 = (12,((5,(3,((2,9),((7,13),(14,4))))),((8,10),(11,(6,15)))),1); tree rep.7110000 = ((((10,8),((15,11),6)),((13,7),((5,(3,(9,2))),(4,14)))),12,1); tree rep.7120000 = (((8,(10,((6,15),11))),((14,((13,7),4)),((3,(9,2)),5))),12,1); tree rep.7130000 = (12,(((10,8),(6,(11,15))),((((14,(7,13)),4),5),((9,2),3))),1); tree rep.7140000 = (12,((10,(((11,15),6),8)),(((14,((13,7),4)),5),(3,(2,9)))),1); tree rep.7150000 = (12,(((8,(11,(15,6))),10),(5,(((2,9),3),((4,(13,7)),14)))),1); tree rep.7160000 = (12,(5,(((2,9),3),(((((6,15),11),(10,8)),14),(4,(13,7))))),1); tree rep.7170000 = (((4,(7,13)),((14,5),(3,(2,9)))),((8,10),(((15,11),6),12)),1); tree rep.7180000 = ((5,((((2,9),3),14),((4,(13,7)),(10,(8,((15,6),11)))))),12,1); tree rep.7190000 = (12,((10,(8,((11,15),6))),(((3,(9,2)),5),(14,((7,13),4)))),1); tree rep.7200000 = ((((11,(6,15)),(10,8)),(((3,(2,9)),5),((4,14),(13,7)))),12,1); tree rep.7210000 = (((14,(7,13)),(((10,8),((6,11),15)),(4,((3,(2,9)),5)))),12,1); tree rep.7220000 = (12,(((7,13),4),(((5,14),(8,(((15,6),11),10))),(3,(9,2)))),1); tree rep.7230000 = (12,(((6,(11,15)),(8,10)),((5,14),(((9,2),3),(7,(4,13))))),1); tree rep.7240000 = (12,((4,((5,((7,13),14)),((9,2),3))),((11,(15,6)),(8,10))),1); tree rep.7250000 = (12,((10,(8,(11,(15,6)))),(((2,9),3),((5,14),(4,(13,7))))),1); tree rep.7260000 = (12,((((5,((9,2),3)),(4,(13,7))),14),(10,(8,((11,15),6)))),1); tree rep.7270000 = ((((((((6,15),11),(8,10)),14),(5,4)),(3,(2,9))),(7,13)),12,1); tree rep.7280000 = ((((14,((7,13),(5,4))),((2,9),3)),12),(8,(10,(11,(15,6)))),1); tree rep.7290000 = (12,((((11,(6,15)),8),10),((5,(14,(3,(2,9)))),(4,(7,13)))),1); tree rep.7300000 = (((5,(12,(3,(2,9)))),(14,(4,(13,7)))),(((15,6),11),(10,8)),1); tree rep.7310000 = (((((5,((9,2),3)),((7,13),4)),14),((8,10),(6,(15,11)))),12,1); tree rep.7320000 = (12,((14,(((9,2),3),(5,((4,7),13)))),((8,10),(6,(15,11)))),1); tree rep.7330000 = (((10,((6,(15,11)),8)),((5,(14,((7,13),4))),(3,(9,2)))),12,1); tree rep.7340000 = (12,((((9,2),3),(5,(((13,7),4),14))),((8,((6,11),15)),10)),1); tree rep.7350000 = ((((5,((2,9),3)),((4,(13,7)),14)),((8,10),((15,6),11))),12,1); tree rep.7360000 = (((3,(2,9)),(((6,(11,15)),(8,10)),(((7,13),(4,14)),5))),12,1); tree rep.7370000 = ((((((13,7),(4,14)),(3,(9,2))),5),(((15,11),6),(8,10))),12,1); tree rep.7380000 = (((3,(2,9)),((14,(7,13)),4)),((12,((8,10),(6,(11,15)))),5),1); tree rep.7390000 = (((10,((6,(15,11)),8)),(((13,7),4),((14,5),(3,(2,9))))),12,1); tree rep.7400000 = (12,(((3,((9,2),5)),(4,((7,13),14))),(((6,(11,15)),10),8)),1); tree rep.7410000 = (12,((5,(((4,14),(13,7)),((9,2),3))),((8,((11,15),6)),10)),1); tree rep.7420000 = (((6,(15,11)),(((3,(2,9)),12),(5,(((13,7),14),4)))),(8,10),1); tree rep.7430000 = (12,(((2,9),(8,(10,((11,15),6)))),((14,(4,(13,7))),(5,3))),1); tree rep.7440000 = (12,(((((2,9),3),(14,(4,(13,7)))),5),((10,8),((15,6),11))),1); tree rep.7450000 = (((((11,(6,15)),10),8),((14,((4,((9,2),3)),(13,7))),5)),12,1); tree rep.7460000 = (((((2,9),3),(((14,(13,7)),4),5)),(8,((11,(6,15)),10))),12,1); tree rep.7470000 = (((((13,(4,7)),14),(3,(2,9))),5),((8,(10,(11,(6,15)))),12),1); tree rep.7480000 = ((((10,(6,(11,15))),8),((5,((2,9),3)),((13,7),(14,4)))),12,1); tree rep.7490000 = (12,((((15,11),6),(8,10)),(3,((((7,13),4),14),((9,2),5)))),1); tree rep.7500000 = ((4,(((7,13),(3,(9,2))),((14,5),(10,((6,(15,11)),8))))),12,1); tree rep.7510000 = (((5,(((7,(4,14)),(3,(2,9))),13)),(8,(10,(11,(15,6))))),12,1); tree rep.7520000 = (12,((((13,7),(4,14)),(3,((9,2),5))),(((15,11),6),(10,8))),1); tree rep.7530000 = (((((5,14),((9,2),3)),((13,7),((8,10),(15,(6,11))))),4),12,1); tree rep.7540000 = (12,(((10,((11,15),6)),8),(14,(5,(((7,13),4),(3,(9,2)))))),1); tree rep.7550000 = (((((6,15),11),(8,10)),(5,(3,((2,9),(4,((7,13),14)))))),12,1); tree rep.7560000 = ((((5,((4,(7,13)),14)),(3,(2,9))),((10,8),((15,6),11))),12,1); tree rep.7570000 = (12,((((10,8),(11,(15,6))),(14,(4,(13,7)))),(5,((2,9),3))),1); tree rep.7580000 = (12,(((((5,14),4),(7,13)),((2,9),3)),(10,(8,((15,6),11)))),1); tree rep.7590000 = (12,((((11,(6,15)),10),8),(((((4,(7,13)),14),3),(2,9)),5)),1); tree rep.7600000 = (((10,8),(11,(6,15))),(12,(((13,7),(4,14)),(((2,9),3),5))),1); tree rep.7610000 = (12,(((7,13),(((3,4),(14,5)),(9,2))),(10,(8,((11,15),6)))),1); tree rep.7620000 = (12,((5,14),(((10,(8,((6,11),15))),((2,9),3)),((13,7),4))),1); tree rep.7630000 = (((((6,(15,11)),10),8),((14,5),((3,(2,9)),(4,(7,13))))),12,1); tree rep.7640000 = (((((2,9),3),((13,7),((14,4),5))),((((15,11),6),8),10)),12,1); tree rep.7650000 = (12,((10,(8,((6,11),15))),(5,(((2,9),3),((4,(7,13)),14)))),1); tree rep.7660000 = (12,((((14,(3,(9,2))),5),((4,7),13)),((8,((11,15),6)),10)),1); tree rep.7670000 = (12,(((((13,7),4),(14,5)),((9,2),3)),((8,(11,(15,6))),10)),1); tree rep.7680000 = ((((3,(9,2)),((14,5),(4,(7,13)))),(((6,(15,11)),8),10)),12,1); tree rep.7690000 = ((12,(((4,(7,13)),(14,5)),(3,(2,9)))),(8,(((15,11),6),10)),1); tree rep.7700000 = (((((11,(15,6)),8),10),(((14,5),((2,9),3)),((7,13),4))),12,1); tree rep.7710000 = (((5,(((4,14),(13,7)),((2,9),3))),((8,10),((15,6),11))),12,1); tree rep.7720000 = (12,(((5,((9,2),((13,7),(4,14)))),3),(8,(10,((15,6),11)))),1); tree rep.7730000 = (12,((((3,(9,2)),(14,5)),(4,(7,13))),(((15,6),11),(8,10))),1); tree rep.7740000 = ((((3,(9,2)),(5,(14,((13,7),4)))),(10,((6,(11,15)),8))),12,1); tree rep.7750000 = (((((15,11),6),(8,10)),((((13,7),4),14),(5,((9,2),3)))),12,1); tree rep.7760000 = (((11,(6,15)),(8,10)),(((2,9),3),(12,(5,((13,7),(14,4))))),1); tree rep.7770000 = (12,((((6,(11,15)),8),10),((14,(((9,2),3),(5,4))),(7,13))),1); tree rep.7780000 = (12,((((3,(9,2)),(14,5)),((7,13),4)),(((15,11),6),(10,8))),1); tree rep.7790000 = (((5,(((9,2),3),(14,((13,7),4)))),((8,(11,(15,6))),10)),12,1); tree rep.7800000 = (((((6,(11,15)),8),10),(((14,(7,13)),4),((5,(2,9)),3))),12,1); tree rep.7810000 = ((((8,10),(11,(6,15))),((5,((4,14),(13,7))),((2,9),3))),12,1); tree rep.7820000 = ((5,((4,(13,7)),((3,((12,2),9)),14))),(8,(((6,15),11),10)),1); tree rep.7830000 = (12,(((8,10),(11,(15,6))),((13,7),((14,5),(((9,2),3),4)))),1); tree rep.7840000 = (12,((5,((((2,9),3),14),(4,(13,7)))),(((6,(15,11)),10),8)),1); tree rep.7850000 = (((((3,(2,9)),((4,(13,7)),14)),5),(((15,11),6),(10,8))),12,1); tree rep.7860000 = (12,(((((6,11),15),8),10),((((2,9),3),(((14,13),4),7)),5)),1); tree rep.7870000 = (12,((3,(9,2)),(((((6,15),11),(10,8)),5),(14,((7,13),4)))),1); tree rep.7880000 = (12,(((8,10),(14,(5,(((13,7),4),(3,(9,2)))))),((11,15),6)),1); tree rep.7890000 = (12,(((15,(6,11)),(10,8)),(((9,2),3),(4,(((7,13),14),5)))),1); tree rep.7900000 = (12,((((5,((13,7),(14,4))),(2,9)),3),(((11,15),6),(8,10))),1); tree rep.7910000 = (((((14,4),(2,((7,13),5))),9),3),(((10,((11,15),6)),8),12),1); tree rep.7920000 = (((((3,(2,9)),5),((14,4),(7,13))),(8,((6,(15,11)),10))),12,1); tree rep.7930000 = ((3,(2,((((4,(14,(13,7))),((11,(15,6)),(10,8))),5),9))),12,1); tree rep.7940000 = ((((((13,7),(4,14)),5),(3,(2,9))),(((11,6),15),(8,10))),12,1); tree rep.7950000 = ((((10,8),((15,11),6)),((5,12),(3,(9,2)))),((4,14),(13,7)),1); tree rep.7960000 = (12,((((11,(6,15)),10),8),(5,((13,7),((14,4),(3,(9,2)))))),1); tree rep.7970000 = (((10,(8,(11,(15,6)))),((((2,9),3),((7,13),5)),(14,4))),12,1); tree rep.7980000 = (12,(((3,(2,9)),(((8,10),((15,6),11)),(4,5))),(14,(13,7))),1); tree rep.7990000 = (12,((((((14,(4,(13,7))),5),(3,(2,9))),10),8),(6,(15,11))),1); tree rep.8000000 = (12,(5,(((2,9),3),(14,((13,7),(4,((15,(6,11)),(10,8))))))),1); tree rep.8010000 = (((((2,9),3),(((13,7),(5,14)),4)),((11,(6,15)),(8,10))),12,1); tree rep.8020000 = ((((8,10),(6,(15,11))),(((2,9),3),(((4,13),7),(14,5)))),12,1); tree rep.8030000 = (((3,(9,2)),(14,(((7,13),4),5))),(((10,((6,15),11)),8),12),1); tree rep.8040000 = (((((11,(15,6)),10),8),((((9,2),3),5),(((7,13),14),4))),12,1); tree rep.8050000 = ((((((11,15),6),10),8),(((7,13),(5,(3,(9,2)))),(14,4))),12,1); tree rep.8060000 = (((((15,11),6),(10,8)),((5,(4,(13,7))),((3,(2,9)),14))),12,1); tree rep.8070000 = (((8,10),(5,(((((7,13),4),14),((2,9),3)),((15,11),6)))),12,1); tree rep.8080000 = (12,(5,((((14,4),(7,13)),(3,(9,2))),(((11,15),6),(10,8)))),1); tree rep.8090000 = (12,(((10,((15,6),11)),8),(((7,13),4),((14,5),(3,(9,2))))),1); tree rep.8100000 = ((((15,6),11),(10,8)),(((((9,2),3),5),(((13,7),4),14)),12),1); tree rep.8110000 = (12,((5,((3,(2,9)),(14,(4,(13,7))))),(8,(10,((15,11),6)))),1); tree rep.8120000 = (((5,(4,((3,(9,2)),(14,(7,13))))),((6,(11,15)),(10,8))),12,1); tree rep.8130000 = (12,((14,(5,(((9,2),3),((8,10),(15,(11,6)))))),(7,(4,13))),1); tree rep.8140000 = (((((3,(9,2)),((14,4),(7,13))),5),(8,(10,(11,(6,15))))),12,1); tree rep.8150000 = ((((10,8),((15,6),11)),((4,(13,7)),((14,5),(3,(9,2))))),12,1); tree rep.8160000 = ((((4,(13,7)),(((14,5),(2,9)),3)),((6,(15,11)),(10,8))),12,1); tree rep.8170000 = ((((15,6),11),(((5,((9,2),3)),(14,(4,(13,7)))),(8,10))),12,1); tree rep.8180000 = (12,(((((8,10),(6,(15,11))),5),(14,((7,13),4))),(3,(2,9))),1); tree rep.8190000 = (12,((5,((14,(13,7)),((3,(2,9)),4))),((((11,15),6),10),8)),1); tree rep.8200000 = ((((((6,15),11),10),8),(14,((7,13),4))),(12,(5,((2,9),3))),1); tree rep.8210000 = (12,((5,(((14,(13,7)),4),((9,2),3))),((10,8),((15,11),6))),1); tree rep.8220000 = ((((14,5),(((9,2),12),3)),((7,13),4)),((15,(11,6)),(10,8)),1); tree rep.8230000 = (12,((((15,11),6),(8,10)),((((7,13),(5,14)),4),((2,9),3))),1); tree rep.8240000 = (12,(((3,(2,9)),((4,(7,13)),(5,14))),((8,10),((11,6),15))),1); tree rep.8250000 = (12,((((6,15),11),(8,10)),((14,(4,(13,7))),(((9,2),3),5))),1); tree rep.8260000 = (12,(((3,(2,9)),5),((((7,14),4),13),((8,10),(15,(11,6))))),1); tree rep.8270000 = (12,((((6,15),11),(8,10)),(5,((14,(13,7)),(3,(4,(2,9)))))),1); tree rep.8280000 = (12,(((((7,13),4),((2,9),3)),(5,14)),((8,10),(6,(15,11)))),1); tree rep.8290000 = ((((5,((4,(14,(13,7))),(3,(2,9)))),(8,((6,15),11))),10),12,1); tree rep.8300000 = (12,(((((7,13),4),(5,14)),(3,(9,2))),((6,(15,11)),(10,8))),1); tree rep.8310000 = (12,(((10,8),((((4,(13,7)),((9,2),3)),5),14)),((15,11),6)),1); tree rep.8320000 = ((((5,(((9,2),3),4)),(14,(13,7))),((10,8),((6,15),11))),12,1); tree rep.8330000 = (12,((((11,(6,15)),8),10),((5,((2,9),3)),((4,(7,13)),14))),1); tree rep.8340000 = (12,(((9,2),3),(((4,((8,10),(6,(11,15)))),5),((13,7),14))),1); tree rep.8350000 = (12,(((14,(3,(2,9))),(((7,13),5),4)),((6,(11,15)),(8,10))),1); tree rep.8360000 = (12,(((9,2),(3,((7,13),(4,(5,14))))),(10,(((11,15),6),8))),1); tree rep.8370000 = (((((15,6),11),(10,8)),(((2,9),3),(5,((14,4),(7,13))))),12,1); tree rep.8380000 = (12,(((5,14),((10,((15,11),6)),8)),(((13,7),4),(3,(2,9)))),1); tree rep.8390000 = (12,((((5,14),((9,2),(3,4))),(13,7)),((8,(6,(11,15))),10)),1); tree rep.8400000 = (((((4,14),5),((7,13),((9,2),3))),((10,8),((15,6),11))),12,1); tree rep.8410000 = ((5,(((12,((13,7),4)),14),((9,2),3))),(10,(8,((15,6),11))),1); tree rep.8420000 = (((5,((15,(11,6)),(8,10))),(((13,(7,4)),14),((2,9),3))),12,1); tree rep.8430000 = (12,(((5,((4,(7,13)),14)),((2,9),3)),(8,((11,(15,6)),10))),1); tree rep.8440000 = (12,(((14,5),(4,((10,8),((11,15),6)))),((3,(9,2)),(7,13))),1); tree rep.8450000 = (12,((((13,7),(4,14)),((3,(2,9)),5)),((11,(15,6)),(8,10))),1); tree rep.8460000 = (12,((5,((14,(13,7)),((3,(9,2)),4))),(((15,6),11),(10,8))),1); tree rep.8470000 = (12,((11,(15,6)),(8,(10,(((13,7),4),(5,((3,(9,2)),14)))))),1); tree rep.8480000 = ((((((4,(7,13)),14),(3,(9,2))),5),(8,(((11,15),6),10))),12,1); tree rep.8490000 = (12,(((10,8),((6,15),11)),(((4,(7,13)),14),(5,((9,2),3)))),1); tree rep.8500000 = (((((11,15),6),(10,8)),((5,((14,4),(7,13))),(3,(9,2)))),12,1); tree rep.8510000 = ((((5,4),(3,(9,2))),(7,(14,13))),(12,(((11,(15,6)),8),10)),1); tree rep.8520000 = (((8,10),(6,(15,11))),(12,((((13,7),(4,(14,5))),3),(2,9))),1); tree rep.8530000 = (((((4,(14,(7,13))),5),((2,9),3)),((10,8),(11,(6,15)))),12,1); tree rep.8540000 = (((10,(((15,11),6),8)),(((5,14),((9,2),3)),(4,(7,13)))),12,1); tree rep.8550000 = (12,((10,(((15,11),6),8)),(5,(((2,9),3),((13,7),(4,14))))),1); tree rep.8560000 = ((((3,(9,2)),(4,((14,5),(13,7)))),(((15,6),11),(10,8))),12,1); tree rep.8570000 = ((((3,(9,2)),(((14,(13,7)),4),5)),((10,8),((6,15),11))),12,1); tree rep.8580000 = (((5,14),((3,(2,9)),(((8,10),(11,(15,6))),(4,(7,13))))),12,1); tree rep.8590000 = (12,((((4,(7,13)),(14,5)),(3,(9,2))),(8,(((11,15),6),10))),1); tree rep.8600000 = (12,((14,((((13,7),4),5),((2,9),3))),(((15,(6,11)),8),10)),1); tree rep.8610000 = (12,((5,(((14,4),(13,7)),((2,9),3))),(((15,6),11),(8,10))),1); tree rep.8620000 = (((14,5),(((3,(9,2)),((7,13),4)),((6,(15,11)),(8,10)))),12,1); tree rep.8630000 = (12,(((8,10),(11,(15,6))),((5,((4,(14,13)),7)),((2,9),3))),1); tree rep.8640000 = (12,(((8,((15,11),6)),10),((5,(14,(4,(13,7)))),(3,(9,2)))),1); tree rep.8650000 = (((14,(4,(7,13))),((((8,10),(6,(15,11))),5),(3,(2,9)))),12,1); tree rep.8660000 = (12,(((10,8),(15,(6,11))),(((3,(9,2)),((4,(13,7)),14)),5)),1); tree rep.8670000 = (((((14,5),(4,(13,7))),((9,2),3)),(8,((6,(11,15)),10))),12,1); tree rep.8680000 = ((12,(((11,(6,15)),8),10)),(5,((3,(9,2)),(((7,13),14),4))),1); tree rep.8690000 = (((((((2,9),3),5),(4,14)),(13,7)),((((15,6),11),10),8)),12,1); tree rep.8700000 = (((4,((5,(3,(2,9))),(14,(13,7)))),12),(8,(((11,15),6),10)),1); tree rep.8710000 = ((((12,(2,9)),3),((14,5),((10,(15,(11,6))),8))),((4,13),7),1); tree rep.8720000 = (12,(((4,(7,13)),((14,5),((2,9),3))),(8,((11,(15,6)),10))),1); tree rep.8730000 = (12,((((2,9),3),(((13,7),4),(5,14))),((6,(15,11)),(10,8))),1); tree rep.8740000 = (((10,8),(11,(15,6))),(12,(((4,(13,7)),14),(5,(3,(2,9))))),1); tree rep.8750000 = ((10,((((4,(13,7)),(14,5)),(((2,9),3),12)),((6,15),11))),8,1); tree rep.8760000 = (12,(((3,(9,2)),(((4,(13,7)),14),(10,(((15,6),11),8)))),5),1); tree rep.8770000 = (((4,(13,7)),((((((11,15),6),10),8),((9,3),2)),(5,14))),12,1); tree rep.8780000 = (12,((14,((5,(3,(9,2))),(4,(13,7)))),(10,((6,(15,11)),8))),1); tree rep.8790000 = (((4,(13,7)),((((2,9),12),3),14)),(((10,8),(6,(11,15))),5),1); tree rep.8800000 = ((5,((3,(9,2)),((4,14),(7,13)))),((8,(10,(6,(11,15)))),12),1); tree rep.8810000 = ((10,(8,(11,(6,15)))),((5,(4,(14,(7,13)))),(12,((2,9),3))),1); tree rep.8820000 = (12,(((8,10),(6,(11,15))),(((((4,5),14),(7,13)),3),(9,2))),1); tree rep.8830000 = ((((((3,(2,9)),5),4),14),(13,7)),(12,((8,10),(11,(6,15)))),1); tree rep.8840000 = (12,((10,(8,(6,(15,11)))),(5,((4,(13,7)),(14,((9,2),3))))),1); tree rep.8850000 = ((((10,((15,11),6)),8),((3,(9,2)),(4,((14,5),(7,13))))),12,1); tree rep.8860000 = (((((4,14),(13,7)),5),(((8,10),((6,15),11)),((2,9),3))),12,1); tree rep.8870000 = (12,(((8,10),((((4,(7,13)),14),5),((2,9),3))),(11,(15,6))),1); tree rep.8880000 = ((((15,11),6),(10,8)),(12,((5,((4,(7,13)),14)),((2,9),3))),1); tree rep.8890000 = (((((13,7),(14,4)),(((11,(15,6)),(10,8)),((2,9),3))),5),12,1); tree rep.8900000 = ((((((15,6),11),8),10),(((((7,13),4),14),((9,2),3)),5)),12,1); tree rep.8910000 = (((((4,13),(14,7)),(((9,2),3),5)),((10,(11,(15,6))),8)),12,1); tree rep.8920000 = ((((8,10),(11,(6,15))),((((7,(13,9)),14),4),(5,(2,3)))),12,1); tree rep.8930000 = (12,((((3,(9,2)),5),((13,7),(14,4))),(10,(8,((6,15),11)))),1); tree rep.8940000 = (12,(((5,((4,(7,13)),14)),((2,9),3)),((10,8),((15,6),11))),1); tree rep.8950000 = (12,((8,(((15,6),11),10)),(((3,(2,9)),5),(((13,7),14),4))),1); tree rep.8960000 = (12,((((3,(9,2)),5),((14,(7,13)),4)),((((15,6),11),8),10)),1); tree rep.8970000 = (12,(((6,(11,15)),(10,8)),(((3,(9,2)),((4,(13,7)),14)),5)),1); tree rep.8980000 = ((((5,((14,4),(7,13))),((9,2),3)),(((15,6),11),(10,8))),12,1); tree rep.8990000 = ((((3,(9,2)),(((4,(7,13)),5),14)),((((15,11),6),10),8)),12,1); tree rep.9000000 = (12,(((3,(2,9)),((4,(14,(7,13))),5)),(((6,15),11),(8,10))),1); tree rep.9010000 = (12,((((2,9),3),((4,(5,(13,7))),14)),(((6,15),11),(10,8))),1); tree rep.9020000 = ((((6,(15,11)),8),10),((((13,7),4),(12,(((2,9),3),14))),5),1); tree rep.9030000 = (((8,((11,(15,6)),10)),((14,((2,9),3)),(5,((13,7),4)))),12,1); tree rep.9040000 = (12,((10,(8,(11,(6,15)))),(((14,5),((13,7),4)),(3,(2,9)))),1); tree rep.9050000 = (((((14,(13,4)),7),((3,(2,9)),5)),(((6,15),11),(8,10))),12,1); tree rep.9060000 = ((((5,(7,13)),(12,((9,2),3))),(4,14)),(((11,15),6),(10,8)),1); tree rep.9070000 = (((((7,13),((3,(2,9)),(14,5))),4),((10,8),(6,(11,15)))),12,1); tree rep.9080000 = ((((10,((15,11),6)),8),(((((13,7),4),14),((9,2),3)),5)),12,1); tree rep.9090000 = ((((10,(11,(6,15))),8),(((3,(9,2)),5),(14,(7,(4,13))))),12,1); tree rep.9100000 = (12,(((((((13,7),(5,(3,(2,9)))),4),14),(11,(6,15))),8),10),1); tree rep.9110000 = (((((6,15),11),(8,10)),((5,((2,9),3)),(14,(4,(13,7))))),12,1); tree rep.9120000 = ((((8,(11,(6,15))),10),(((9,2),3),(5,((14,(7,13)),4)))),12,1); tree rep.9130000 = (((((14,5),(4,(7,13))),(3,(9,2))),(8,(((15,11),6),10))),12,1); tree rep.9140000 = (12,((((14,5),((9,2),3)),(13,(7,4))),((8,10),(6,(15,11)))),1); tree rep.9150000 = ((5,((((4,(7,13)),14),((9,2),3)),((6,(15,11)),(10,8)))),12,1); tree rep.9160000 = (12,(((((2,9),3),(5,14)),(4,(7,13))),((8,(6,(11,15))),10)),1); tree rep.9170000 = ((((8,10),((15,6),11)),((((9,2),3),5),(((7,13),14),4))),12,1); tree rep.9180000 = ((((15,(11,6)),10),8),(12,(((9,2),3),(((7,13),(14,4)),5))),1); tree rep.9190000 = ((((8,10),(11,(15,6))),(((3,(9,2)),4),((14,5),(13,7)))),12,1); tree rep.9200000 = (12,((((3,(9,2)),(4,(7,13))),(5,14)),((8,10),(11,(6,15)))),1); tree rep.9210000 = (12,((((6,15),11),(8,10)),((((14,(7,13)),4),((9,2),3)),5)),1); tree rep.9220000 = (12,(((4,(7,(14,13))),(((10,8),((15,11),6)),(9,2))),(5,3)),1); tree rep.9230000 = (((((15,11),6),(8,10)),(((4,14),(7,13)),(5,((2,9),3)))),12,1); tree rep.9240000 = (((((2,9),3),5),(14,(4,(13,7)))),(12,((10,8),((11,15),6))),1); tree rep.9250000 = (12,(((8,(6,(15,11))),10),(((3,(9,2)),4),(5,(14,(7,13))))),1); tree rep.9260000 = ((((8,10),(5,((14,(4,(13,7))),(3,(2,9))))),((15,11),6)),12,1); tree rep.9270000 = (((((2,9),3),(((7,13),(4,14)),5)),(10,(8,((11,15),6)))),12,1); tree rep.9280000 = ((((5,(4,((13,7),14))),((9,2),3)),((11,(15,6)),(8,10))),12,1); tree rep.9290000 = (12,((((6,(11,15)),(8,10)),(14,((13,7),4))),(((9,2),3),5)),1); tree rep.9300000 = (12,((((3,(9,2)),((4,(7,13)),14)),5),(10,((11,(6,15)),8))),1); tree rep.9310000 = (12,(((((9,2),3),(4,(7,13))),(5,14)),(((11,15),6),(8,10))),1); tree rep.9320000 = (12,(((6,(11,15)),(10,8)),(((3,(2,9)),((13,(14,7)),4)),5)),1); tree rep.9330000 = (((((15,6),11),(10,8)),(((13,(4,7)),14),((3,(2,9)),5))),12,1); tree rep.9340000 = ((((9,2),3),(((4,(((8,10),(11,(15,6))),14)),5),(7,13))),12,1); tree rep.9350000 = (12,(((((15,6),11),8),10),((14,5),((4,(13,7)),(3,(9,2))))),1); tree rep.9360000 = (12,((((3,(9,2)),(((7,13),4),(5,14))),(15,(6,11))),(8,10)),1); tree rep.9370000 = ((((((9,2),5),3),(((7,13),4),14)),(10,(((6,15),11),8))),12,1); tree rep.9380000 = ((((3,(9,2)),((4,(7,13)),(5,14))),12),((((15,11),6),10),8),1); tree rep.9390000 = ((((9,2),3),((((13,7),4),14),((8,((11,(6,15)),10)),5))),12,1); tree rep.9400000 = (((13,7),(((14,(4,5)),((9,2),3)),12)),((8,10),(15,(6,11))),1); tree rep.9410000 = ((((8,10),(6,(11,15))),((((9,2),3),(4,(7,13))),(5,14))),12,1); tree rep.9420000 = (((8,10),((11,6),15)),(((3,12),(9,2)),(((7,13),4),(5,14))),1); tree rep.9430000 = ((((5,((7,(4,13)),14)),(3,(2,9))),((8,10),(11,(15,6)))),12,1); tree rep.9440000 = (12,((9,(3,2)),((((4,(13,7)),14),5),(10,(8,((15,6),11))))),1); tree rep.9450000 = ((14,((((11,(6,15)),(8,10)),(5,4)),(((2,9),3),(7,13)))),12,1); tree rep.9460000 = (((((3,(9,2)),5),((14,(7,13)),4)),(10,(8,(11,(6,15))))),12,1); tree rep.9470000 = (((5,((2,9),((((13,7),14),4),3))),((11,(15,6)),(10,8))),12,1); tree rep.9480000 = ((((14,(7,13)),(4,((3,(2,9)),5))),12),((6,(15,11)),(10,8)),1); tree rep.9490000 = ((((10,(11,(15,6))),8),((((13,7),4),(14,5)),(3,(2,9)))),12,1); tree rep.9500000 = (12,(((((11,6),15),10),8),((5,((7,(13,4)),14)),((9,2),3))),1); tree rep.9510000 = (12,((((11,(6,15)),8),10),(((2,9),3),((((7,13),4),5),14))),1); tree rep.9520000 = ((((6,11),15),(8,10)),(((3,(2,9)),(4,(7,13))),(12,(5,14))),1); tree rep.9530000 = (((((5,((7,13),14)),4),(3,(2,9))),(((11,(15,6)),10),8)),12,1); tree rep.9540000 = (12,((10,(8,((6,15),11))),((14,5),(((13,7),4),(3,(2,9))))),1); tree rep.9550000 = (12,(((8,10),((6,15),11)),(((9,2),3),(5,(((14,4),7),13)))),1); tree rep.9560000 = (((10,(8,((11,15),6))),((14,((9,2),3)),(((7,13),4),5))),12,1); tree rep.9570000 = (((14,(3,(2,9))),(5,((13,7),4))),(12,(8,(((15,6),11),10))),1); tree rep.9580000 = (((((9,2),(((7,13),14),4)),(3,5)),(((11,15),6),(8,10))),12,1); tree rep.9590000 = (12,(((9,2),3),(5,((((6,(11,15)),(10,8)),((13,7),4)),14))),1); tree rep.9600000 = (12,((5,((((10,8),(11,(6,15))),((4,7),13)),(3,(9,2)))),14),1); tree rep.9610000 = (12,(((10,((15,6),11)),8),((13,7),(((14,5),(3,(9,2))),4))),1); tree rep.9620000 = (((11,(15,6)),(10,8)),((3,((2,9),(14,((4,(13,7)),5)))),12),1); tree rep.9630000 = (((((11,(15,6)),8),10),((((9,2),3),((14,4),(13,7))),5)),12,1); tree rep.9640000 = (12,(((5,((9,2),3)),(4,(14,(7,13)))),((10,8),(6,(11,15)))),1); tree rep.9650000 = (((5,((14,(4,(13,7))),((9,2),3))),((8,((15,11),6)),10)),12,1); tree rep.9660000 = ((8,(10,((6,15),11))),(((3,(2,9)),((14,(5,(13,7))),4)),12),1); tree rep.9670000 = ((((10,8),(6,(11,15))),((3,(2,9)),((((7,13),5),4),14))),12,1); tree rep.9680000 = (((11,(15,6)),(10,8)),(12,(((2,9),3),(((14,(13,7)),4),5))),1); tree rep.9690000 = ((((6,(11,15)),(8,10)),((((5,14),(3,(9,2))),4),(13,7))),12,1); tree rep.9700000 = (((((((3,(9,2)),5),(14,((7,13),4))),8),10),(11,(15,6))),12,1); tree rep.9710000 = (((11,(6,15)),(((((9,2),3),5),(((13,7),4),14)),(8,10))),12,1); tree rep.9720000 = (12,((10,(8,((15,6),11))),((((4,(7,13)),14),5),(3,(9,2)))),1); tree rep.9730000 = (12,((((7,(14,5)),(3,(9,2))),(13,4)),((8,10),((15,11),6))),1); tree rep.9740000 = (12,(((10,8),((15,11),6)),((5,((9,2),3)),(4,((7,13),14)))),1); tree rep.9750000 = (12,((((11,(15,6)),(8,10)),(5,(14,((7,13),4)))),(3,(9,2))),1); tree rep.9760000 = (((8,10),(((11,15),6),((((9,2),3),5),(((13,7),4),14)))),12,1); tree rep.9770000 = (((((14,5),(3,(9,2))),((7,13),4)),(8,(10,(6,(11,15))))),12,1); tree rep.9780000 = ((((((((6,15),11),8),10),((13,7),4)),((3,(2,9)),14)),12),5,1); tree rep.9790000 = (12,(((4,(13,7)),(14,(((9,2),3),(((11,15),6),(8,10))))),5),1); tree rep.9800000 = ((((3,2),9),((((6,(11,15)),8),10),(14,((4,(13,7)),5)))),12,1); tree rep.9810000 = ((((10,(11,(6,15))),8),((14,(((9,2),3),5)),(4,(7,13)))),12,1); tree rep.9820000 = ((10,(((11,15),6),8)),((((((7,13),14),5),4),(3,(9,2))),12),1); tree rep.9830000 = (12,(((14,((4,(13,7)),((9,2),3))),5),((10,8),(11,(15,6)))),1); tree rep.9840000 = (12,(((6,(15,11)),(10,8)),(((9,2),3),(14,(5,(4,(7,13)))))),1); tree rep.9850000 = (((((6,15),11),(10,8)),((((2,9),3),(((7,13),4),14)),5)),12,1); tree rep.9860000 = ((((3,(2,9)),(5,((7,13),(4,14)))),(8,(((11,15),6),10))),12,1); tree rep.9870000 = ((8,((((5,((14,7),(13,4))),((2,9),3)),((6,15),11)),10)),12,1); tree rep.9880000 = (((4,((14,5),(((11,15),6),(8,10)))),((3,(2,9)),(7,13))),12,1); tree rep.9890000 = ((((5,(((13,7),(2,9)),4)),(14,3)),((10,((15,6),11)),8)),12,1); tree rep.9900000 = (12,(((6,(11,15)),(10,8)),(14,((((7,13),4),5),((9,2),3)))),1); tree rep.9910000 = (12,((5,(((9,2),3),((4,(7,13)),14))),((10,8),((15,11),6))),1); tree rep.9920000 = ((((((2,9),3),(4,(13,7))),(5,14)),(8,(((15,6),11),10))),12,1); tree rep.9930000 = (12,(((10,8),((11,15),6)),((4,(((14,13),7),(3,(2,9)))),5)),1); tree rep.9940000 = (12,((5,(14,(((9,2),3),((7,13),4)))),(8,(((15,11),6),10))),1); tree rep.9950000 = ((((((9,2),3),5),((14,(7,13)),4)),12),(((6,(15,11)),8),10),1); tree rep.9960000 = (12,(((2,9),3),(14,(13,(5,(4,((8,(((15,11),6),10)),7)))))),1); tree rep.9970000 = ((12,(10,(8,((15,6),11)))),((14,((7,13),4)),((2,9),(5,3))),1); tree rep.9980000 = (12,(((14,((((6,15),11),(8,10)),((2,9),3))),5),(4,(13,7))),1); tree rep.9990000 = (((10,(8,((11,15),6))),((13,7),(14,((4,(3,(9,2))),5)))),12,1); tree rep.10000000 = (12,((((5,14),4),((13,7),(8,(10,((15,11),6))))),((3,2),9)),1); end; phangorn/inst/extdata/trees/woodmouse.fasta0000644000175100001440000003447112761653173020722 0ustar hornikusers>No305 nttcgaaaaacacacccactactaaaanttatcagtcactccttcatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggagtctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatacatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacattttcatagaaacatgaaacatcggtgtggtccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaanttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagccacactgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgtccacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatatttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcagaccctata >No304 attcgaaaaacacacccactactaaaaattatcaaccactccttcatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacatttttatagaaacatgaaacatcggtgtagtccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattncaaatttactntcagcaattccatacatcggaactaccctagtagaatgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactctagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacggtccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaaccctgtn >No306 attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagaatgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacnncatattaaaccagaatgatacttcctatttgcctatgcaatcctacggtccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaaccctata >No0906S attcgaaaaacacacccactactaaaaatcatcaatcactccttcatcgatttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacggatcatacatttttatagaaacatgaaacatcggtatgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataataatttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaatacnnnn >No0908S attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagccccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttctccatgtaggacgaggaatgtattacgggtcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggggctacagtaattacaaatttactatcagcaattccatatatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatcctcccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No0909S attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaactatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacactttcatagaaacatgaaacatcggtgtgatccttctattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcgggactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaatcctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No0910S attcgaaaaacacacccactactaaaaattatcaatcactctttcatcgatttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccagatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacggatcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggcgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttgcaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No0912S attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacggggaatgtattacgggtcatacattttcatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgctttccacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagacattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatatttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No0913S attcgaaaaacacacccactactaaaaattatcaaccactccttcatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatgcatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacggatcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagaatgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggcgtactaataatagtttccttcctaataactctagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacggtccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No1103S attcgaaaaacacacccactactaaaaattattaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacattttcatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatatttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No1007S attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgccgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacactttcatagaaacatgaaacatcggtgtgatccttctattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcgggactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaatcctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No1114S nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnatcgacttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggagtctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagnaacccatatttgccgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatatattacggctcatacattctcatagaaacatgaaacatcggtgtggtccttttattcgcagtaatagtcacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttcctatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaggatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatatttcctatttgcctatgcaatcctacggtccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccattccttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No1202S attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgatttaccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccagatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgccgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacggatcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggcgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No1206S attcgaaaaacacacccactactaaaaattatcaatcactccttcatcgatttgccagctccatctaacatttcatcatgatgaaacttcggctcattactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgtcgagacgtaaattatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttctccatgtaggacgaggaatgtattacgggtcatacatttttatagaaacatgaaacatcggtgtgatccttttattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccttctgaggagctacagtaattacaaatttactatcagcaattccatacatcggaactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcacttcatccttccatttatcattgccgccctagtaattgtgcacctcttgtttcttcatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagacctcttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaattctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn >No1208S nnncgaaaaacacacccactactaaaaattatcaatcactccttcatcgacttaccagctccatctaacatctcatcatgatgaaacttcggctccttactaggaatctgcctaataatccaaatcctcacaggcttattcctagcaatacactacacatcagacacaataacagcattctcttcagtaacccatatttgccgagacgtaaactatggctgactaattcgatatatacatgcaaacggagcctcaatattttttatttgcctatttcttcatgtaggacgaggaatgtattacgggtcatacactttcatagaaacatgaaacatcggtgtgatccttctattcgcagtaatagccacagcattcataggatatgttcttccatgaggacaaatatccctctgaggagctacagtaattacaaatttactatcagcaattccatacatcgggactaccctagtagagtgaatctgaggaggattctcagtagacaaagctacattgacacgttttttcgcttttcactttatccttccatttatcattgccgccctagtaattgttcacctcttgtttctccatgaaactggatctaataacccaacaggccttaactcagacgccgataaaatcccatttcacccttactatacaattaaagatattctaggtgtactaataatagtttccttcctaataactttagtccttttctttccagaccttttaggtgacccggacaactacatgcctgctaacccacttaacaccccacctcatattaaaccagaatgatacttcctatttgcctatgcaatcctacgatccatccccaataaactaggcggagtcctagccctaatcctatcaatcttaattttagccttattaccatttcttcacacttccaaacaacgcagcctaatattccgcccaattactcaaacccnnnn phangorn/inst/extdata/FLU.dat0000644000175100001440000000721112761653173015647 0ustar hornikusers 0.138658764751059 0.0533665787145181 0.161000889039552 0.584852305649886 0.00677184253227681 7.73739287051356 0.0264470951166826 0.16720700818221 1.30249856764315e-005 0.014132062548787 0.353753981649393 3.29271694159791 0.530642655337477 0.145469388422239 0.00254733397966779 1.4842345032161 0.124897616909194 0.0616521921873234 5.37051127867923 3.91106992668137e-011 1.19562912226203 1.13231312248046 1.19062446519178 0.322524647863997 1.93483278448943 0.116941459124876 0.108051341246072 1.59309882471598 0.214757862168721 1.87956993845887 1.38709603234116 0.887570549414031 0.0218446166959521 5.33031341222104 0.256491863423002 0.0587745274250666 0.149926734229061 0.246117171830255 0.21857197541607 0.0140859174993809 0.00111215807314139 0.0288399502994541 0.0142107118685268 1.62662283098296e-005 0.243190142026506 0.0231169515264061 0.296045557460629 0.000835873174542931 0.00573068208525287 0.00561362724916376 1.02036695531654 0.016499535540562 0.00651622937676521 0.321611693603646 3.51207228207807 0.474333610192982 15.3000966197798 2.6468479652886 0.290042980143818 3.83228119049152e-006 2.559587177122 3.88148880863814 0.264148929349066 0.347302791211758 0.227707997165566 0.129223639195248 0.0587454231508643 0.890162345593224 0.00525168778853117 0.0417629637305017 0.111457310321926 0.190259181297527 0.313974351356074 0.00150046692269255 0.00127350890508147 9.01795420287895 6.74693648486614 1.33129161941264 0.0804909094320368 0.0160550314767596 0.000836445615590923 1.0600102849456e-006 0.10405366623526 0.0326806570137471 0.00100350082518749 0.00123664495412902 0.119028506158521 1.46335727834648 2.98680003596399 0.319895904499071 0.279910508981581 0.659311477863896 0.154027179890711 0.0364417719063219 0.188539456415654 1.59312060172652e-013 0.712769599068934 0.319558828428154 0.0386317614553493 0.924466914225534 0.0805433268150369 0.634308520867322 0.195750631825315 0.0568693216513547 0.0071324304661639 3.01134451903854 0.950138410087378 3.88131053061457 0.338372183381345 0.336263344504404 0.487822498528951 0.307140298031341 1.58564657669139 0.580704249811294 0.290381075260226 0.570766693213698 0.283807671568883 0.00702658828739369 0.996685669575839 2.08738534433198 5.4182981753166 0.183076905018197 2.14033231636063 0.135481232622983 0.011975265782196 0.60234096342392 0.2801248951174 0.0188080299490973 0.368713573381758 2.90405228596936 0.0449263566753846 1.52696419998775 2.03151132062208 0.000134906239484254 0.54225109402693 2.2068599339404 0.195966354027106 1.36942940801512 0.000536284040016542 1.4893873721753e-005 0.0941066800969967 0.0440205200833047 0.155245492137294 0.196486447133033 0.0223729191088972 0.0321321499585514 0.431277662888057 4.97641445484395e-005 0.0704600385245663 0.814753093809928 0.000431020702277328 0.0998357527014247 0.207066205546908 0.0182892882245349 0.0998554972524385 0.373101926513925 0.525398542949365 0.601692431136271 0.0722059354079545 0.104092870343653 0.0748149970972622 6.44895444648517 0.273934263183281 0.340058468374384 0.0124162215506117 0.874272174533394 5.39392424532822 0.000182294881489116 0.392552239890831 0.124898020409882 0.42775543040588 3.53200526987468 0.103964386383736 0.0102575172450253 0.297123975243582 0.0549045639492389 0.406697814049488 0.285047948309311 0.337229618868315 0.0986313546653266 14.3940521944257 0.890598579382591 0.0731279296372675 4.90484223478739 0.592587985458668 0.0589719751511691 0.0882564232979724 0.654109108255219 0.256900461407996 0.167581646770807 0.0470718 0.0509102 0.0742143 0.0478596 0.0250216 0.0333036 0.0545874 0.0763734 0.0199642 0.0671336 0.0714981 0.0567845 0.0181507 0.0304961 0.0506561 0.0884091 0.0743386 0.0185237 0.0314741 0.0632292 phangorn/inst/extdata/Dayhoff.dat0000644000175100001440000000250612761653173016603 0ustar hornikusers 27.00 98.00 32.00 120.00 0.00 905.00 36.00 23.00 0.00 0.00 89.00 246.00 103.00 134.00 0.00 198.00 1.00 148.00 1153.00 0.00 716.00 240.00 9.00 139.00 125.00 11.00 28.00 81.00 23.00 240.00 535.00 86.00 28.00 606.00 43.00 10.00 65.00 64.00 77.00 24.00 44.00 18.00 61.00 0.00 7.00 41.00 15.00 34.00 0.00 0.00 73.00 11.00 7.00 44.00 257.00 26.00 464.00 318.00 71.00 0.00 153.00 83.00 27.00 26.00 46.00 18.00 72.00 90.00 1.00 0.00 0.00 114.00 30.00 17.00 0.00 336.00 527.00 243.00 18.00 14.00 14.00 0.00 0.00 0.00 0.00 15.00 48.00 196.00 157.00 0.00 92.00 250.00 103.00 42.00 13.00 19.00 153.00 51.00 34.00 94.00 12.00 32.00 33.00 17.00 11.00 409.00 154.00 495.00 95.00 161.00 56.00 79.00 234.00 35.00 24.00 17.00 96.00 62.00 46.00 245.00 371.00 26.00 229.00 66.00 16.00 53.00 34.00 30.00 22.00 192.00 33.00 136.00 104.00 13.00 78.00 550.00 0.00 201.00 23.00 0.00 0.00 0.00 0.00 0.00 27.00 0.00 46.00 0.00 0.00 76.00 0.00 75.00 0.00 24.00 8.00 95.00 0.00 96.00 0.00 22.00 0.00 127.00 37.00 28.00 13.00 0.00 698.00 0.00 34.00 42.00 61.00 208.00 24.00 15.00 18.00 49.00 35.00 37.00 54.00 44.00 889.00 175.00 10.00 258.00 12.00 48.00 30.00 157.00 0.00 28.00 0.087127 0.040904 0.040432 0.046872 0.033474 0.038255 0.049530 0.088612 0.033618 0.036886 0.085357 0.080482 0.014753 0.039772 0.050680 0.069577 0.058542 0.010494 0.029916 0.064718 phangorn/inst/extdata/cpREV.dat0000644000175100001440000000352412761653173016203 0ustar hornikusers 105 227 357 175 43 4435 669 823 538 10 157 1745 768 400 10 499 152 1055 3691 10 3122 665 243 653 431 303 133 379 66 715 1405 331 441 1269 162 19 145 136 168 10 280 92 148 40 29 197 203 113 10 396 286 82 20 66 1745 236 4482 2430 412 48 3313 2629 263 305 345 218 185 125 61 47 159 202 113 21 10 1772 1351 193 68 53 97 22 726 10 145 25 127 454 1268 72 327 490 87 173 170 285 323 185 28 152 117 219 302 100 43 2440 385 2085 590 2331 396 568 691 303 216 516 868 93 487 1202 1340 314 1393 266 576 241 369 92 32 1040 156 918 645 148 260 2151 14 230 40 18 435 53 63 82 69 42 159 10 86 468 49 73 29 56 323 754 281 1466 391 142 10 1971 89 189 247 215 2370 97 522 71 346 968 92 83 75 592 54 200 91 25 4797 865 249 475 317 122 167 760 10 119 0.0755 0.0621 0.0410 0.0371 0.0091 0.0382 0.0495 0.0838 0.0246 0.0806 0.1011 0.0504 0.0220 0.0506 0.0431 0.0622 0.0543 0.0181 0.0307 0.0660 A R N D C Q E G H I L K M F P S T W Y V Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val Symmetrical part of the rate matrix and aa frequencies, estimated for plant chloroplast proteins, under the REVaa model. The first part is S_ij = S_ji, and the second part has the amino acid frequencies (\pi_i). The substitution rate from amino acid i to j is Q_ij = S_ij*PI_j. This is the cpREV model used in protml 2.3b6 (12/10/98), described by Adachi, J., P. J. Waddell, W. Martin, and M. Hasegawa. 2000. Plastid genome phylogeny and a model of amino acid substitution for proteins encoded by chloroplast DNA. Journal of Molecular Evolution 50:348-358. phangorn/inst/extdata/mtmam.dat0000644000175100001440000000726612761653173016346 0ustar hornikusers 32 2 4 11 0 864 0 186 0 0 0 246 8 49 0 0 0 0 569 0 274 78 18 47 79 0 0 22 8 232 458 11 305 550 22 0 75 0 19 0 41 0 0 0 0 21 6 0 0 27 20 0 0 26 232 0 50 408 0 0 242 215 0 0 6 4 76 0 21 0 0 22 0 0 0 378 609 59 0 0 6 5 7 0 0 0 0 57 246 0 11 53 9 33 2 0 51 0 0 53 5 43 18 0 17 342 3 446 16 347 30 21 112 20 0 74 65 47 90 202 681 0 110 0 114 0 4 0 1 360 34 50 691 8 78 614 5 16 6 0 65 0 0 0 0 0 12 0 13 0 7 17 0 0 0 156 0 530 54 0 1 1525 16 25 67 0 682 8 107 0 14 398 0 0 10 0 33 20 5 0 2220 100 0 832 6 0 0 237 0 0 0.0692 0.0184 0.0400 0.0186 0.0065 0.0238 0.0236 0.0557 0.0277 0.0905 0.1675 0.0221 0.0561 0.0611 0.0536 0.0725 0.0870 0.0293 0.0340 0.0428 A R N D C Q E G H I L K M F P S T W Y V Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val //End of File Symmetrical part of the rate matrix and aa frequencies, estimated from the 12 mt proteins (atp6 atp8 cox1 cox2 cox3 cytb nd1 nd2 nd3 nd4 nd4l nd5) on the same strand of the mitochondrial DNA (3331 sites). The data are from 20 species of mammals and three close outgroups (wallaroo, opossum, and platypus). The model used is REVaa+dGamma(K=8) with the estimated gamma parameter to be 0.37. The first part is S_ij = S_ji, and the second part has the amino acid frequencies (PI_i). The substitution rate from amino acid i to j is Q_ij=S_ij*PI_j. The data are from Cao, Y. et al. 1998 Conflict amongst individual mitochondrial proteins in resolving the phylogeny of eutherian orders. Journal of Molecular Evolution 15:1600-1611. The species are listed below 1 SB17F Homo sapiens (African) # D38112 2 CHIMP Pan troglodytes (chimpanzee) # D38113 3 PyGC Pan paniscus (bonobo) # D38116 4 GORIL Gorilla gorilla (gorilla) # D38114 5 ORANG Pongo pygmaeus (orangutan) # D38115 6 Ponpy Pongo pygmaeus abelii (Sumatran orangutan) # X97707 7 Hylla Hylobates lar (common gibbon) # X99256 (lar gibbon) 8 Phovi Phoca vitulina (harbor seal) # X63726 9 Halgr Halichoerus grypus (grey seal) # X72004 10 Felca Felis catus (cat) # U20753 11 Equca Equus caballus (horse) # X79547 12 Rhiun Rhinoceros unicornis (Indian rhinoceros) # X97336 13 Bosta Bos taurus (cow) # J01394 14 Balph Balaenoptera physalus (fin whale) # X61145 15 Balmu Balaenoptera musculus (blue whale) # X72204 16 Ratno Rattus norvegicus (rat) # X14848 17 Musmu Mus musculus (mouse) # J01420 18 Macro Macropus robustus (wallaroo) # Y10524 19 Didvi Didelphis virginiana (opossum) # Z29573 20 Ornan Ornithorhynchus anatinus (platypus) # X83427 The results and details of the model are published in Yang, Z., R. Nielsen, and M. Hasegawa. 1998. Models of amino acid substitution and applications to Mitochondrial protein evolution, Molecular Biology and Evolution 15:1600-1611. Prepared by Z. Yang, April 1998. phangorn/inst/extdata/lg.dat0000644000175100001440000000361512761653173015627 0ustar hornikusers 0.425093 0.276818 0.751878 0.395144 0.123954 5.076149 2.489084 0.534551 0.528768 0.062556 0.969894 2.807908 1.695752 0.523386 0.084808 1.038545 0.363970 0.541712 5.243870 0.003499 4.128591 2.066040 0.390192 1.437645 0.844926 0.569265 0.267959 0.348847 0.358858 2.426601 4.509238 0.927114 0.640543 4.813505 0.423881 0.311484 0.149830 0.126991 0.191503 0.010690 0.320627 0.072854 0.044265 0.008705 0.108882 0.395337 0.301848 0.068427 0.015076 0.594007 0.582457 0.069673 0.044261 0.366317 4.145067 0.536518 6.326067 2.145078 0.282959 0.013266 3.234294 1.807177 0.296636 0.697264 0.159069 0.137500 1.124035 0.484133 0.371004 0.025548 0.893680 1.672569 0.173735 0.139538 0.442472 4.273607 6.312358 0.656604 0.253701 0.052722 0.089525 0.017416 1.105251 0.035855 0.018811 0.089586 0.682139 1.112727 2.592692 0.023918 1.798853 1.177651 0.332533 0.161787 0.394456 0.075382 0.624294 0.419409 0.196961 0.508851 0.078281 0.249060 0.390322 0.099849 0.094464 4.727182 0.858151 4.008358 1.240275 2.784478 1.223828 0.611973 1.739990 0.990012 0.064105 0.182287 0.748683 0.346960 0.361819 1.338132 2.139501 0.578987 2.000679 0.425860 1.143480 1.080136 0.604545 0.129836 0.584262 1.033739 0.302936 1.136863 2.020366 0.165001 0.571468 6.472279 0.180717 0.593607 0.045376 0.029890 0.670128 0.236199 0.077852 0.268491 0.597054 0.111660 0.619632 0.049906 0.696175 2.457121 0.095131 0.248862 0.140825 0.218959 0.314440 0.612025 0.135107 1.165532 0.257336 0.120037 0.054679 5.306834 0.232523 0.299648 0.131932 0.481306 7.803902 0.089613 0.400547 0.245841 3.151815 2.547870 0.170887 0.083688 0.037967 1.959291 0.210332 0.245034 0.076701 0.119013 10.649107 1.702745 0.185202 1.898718 0.654683 0.296501 0.098369 2.188158 0.189510 0.249313 0.079066 0.055941 0.041977 0.053052 0.012937 0.040767 0.071586 0.057337 0.022355 0.062157 0.099081 0.064600 0.022951 0.042302 0.044040 0.061197 0.053287 0.012066 0.034155 0.069147 phangorn/inst/extdata/JTT.dat0000644000175100001440000000252712761653173015667 0ustar hornikusers 58.00 54.00 45.00 81.00 16.00 528.00 56.00 113.00 34.00 10.00 57.00 310.00 86.00 49.00 9.00 105.00 29.00 58.00 767.00 5.00 323.00 179.00 137.00 81.00 130.00 59.00 26.00 119.00 27.00 328.00 391.00 112.00 69.00 597.00 26.00 23.00 36.00 22.00 47.00 11.00 17.00 9.00 12.00 6.00 16.00 30.00 38.00 12.00 7.00 23.00 72.00 9.00 6.00 56.00 229.00 35.00 646.00 263.00 26.00 7.00 292.00 181.00 27.00 45.00 21.00 14.00 54.00 44.00 30.00 15.00 31.00 43.00 18.00 14.00 33.00 479.00 388.00 65.00 15.00 5.00 10.00 4.00 78.00 4.00 5.00 5.00 40.00 89.00 248.00 4.00 43.00 194.00 74.00 15.00 15.00 14.00 164.00 18.00 24.00 115.00 10.00 102.00 21.00 16.00 17.00 378.00 101.00 503.00 59.00 223.00 53.00 30.00 201.00 73.00 40.00 59.00 47.00 29.00 92.00 285.00 475.00 64.00 232.00 38.00 42.00 51.00 32.00 33.00 46.00 245.00 25.00 103.00 226.00 12.00 118.00 477.00 9.00 126.00 8.00 4.00 115.00 18.00 10.00 55.00 8.00 9.00 52.00 10.00 24.00 53.00 6.00 35.00 12.00 11.00 20.00 70.00 46.00 209.00 24.00 7.00 8.00 573.00 32.00 24.00 8.00 18.00 536.00 10.00 63.00 21.00 71.00 298.00 17.00 16.00 31.00 62.00 20.00 45.00 47.00 11.00 961.00 180.00 14.00 323.00 62.00 23.00 38.00 112.00 25.00 16.00 0.076748 0.051691 0.042645 0.051544 0.019803 0.040752 0.061830 0.073152 0.022944 0.053761 0.091904 0.058676 0.023826 0.040126 0.050901 0.068765 0.058565 0.014261 0.032102 0.066005 phangorn/inst/extdata/dayhoff-dcmut.dat0000644000175100001440000000565712761653173017767 0ustar hornikusers0.267828 0.984474 0.327059 1.199805 0.000000 8.931515 0.360016 0.232374 0.000000 0.000000 0.887753 2.439939 1.028509 1.348551 0.000000 1.961167 0.000000 1.493409 11.388659 0.000000 7.086022 2.386111 0.087791 1.385352 1.240981 0.107278 0.281581 0.811907 0.228116 2.383148 5.290024 0.868241 0.282729 6.011613 0.439469 0.106802 0.653416 0.632629 0.768024 0.239248 0.438074 0.180393 0.609526 0.000000 0.076981 0.406431 0.154924 0.341113 0.000000 0.000000 0.730772 0.112880 0.071514 0.443504 2.556685 0.258635 4.610124 3.148371 0.716913 0.000000 1.519078 0.830078 0.267683 0.270475 0.460857 0.180629 0.717840 0.896321 0.000000 0.000000 0.000000 1.127499 0.304803 0.170372 0.000000 3.332732 5.230115 2.411739 0.183641 0.136906 0.138503 0.000000 0.000000 0.000000 0.000000 0.153478 0.475927 1.951951 1.565160 0.000000 0.921860 2.485920 1.028313 0.419244 0.133940 0.187550 1.526188 0.507003 0.347153 0.933709 0.119152 0.316258 0.335419 0.170205 0.110506 4.051870 1.531590 4.885892 0.956097 1.598356 0.561828 0.793999 2.322243 0.353643 0.247955 0.171432 0.954557 0.619951 0.459901 2.427202 3.680365 0.265745 2.271697 0.660930 0.162366 0.525651 0.340156 0.306662 0.226333 1.900739 0.331090 1.350599 1.031534 0.136655 0.782857 5.436674 0.000000 2.001375 0.224968 0.000000 0.000000 0.000000 0.000000 0.000000 0.270564 0.000000 0.461776 0.000000 0.000000 0.762354 0.000000 0.740819 0.000000 0.244139 0.078012 0.946940 0.000000 0.953164 0.000000 0.214717 0.000000 1.265400 0.374834 0.286572 0.132142 0.000000 6.952629 0.000000 0.336289 0.417839 0.608070 2.059564 0.240368 0.158067 0.178316 0.484678 0.346983 0.367250 0.538165 0.438715 8.810038 1.745156 0.103850 2.565955 0.123606 0.485026 0.303836 1.561997 0.000000 0.279379 0.087127 0.040904 0.040432 0.046872 0.033474 0.038255 0.049530 0.088612 0.033619 0.036886 0.085357 0.080481 0.014753 0.039772 0.050680 0.069577 0.058542 0.010494 0.029916 0.064718 A R N D C Q E G H I L K M F P S T W Y V Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val # #Dayhoff rate matrix prepared using the DCMut method* #---------------------------------------------------- # #The first part above indicates the symmetric 'exchangeability' parameters s_ij, #where s_ij = s_ji. #The second part gives the amino acid equilibrium frequencies pi_i. #The net replacement rate from i to j is q_ij = pi_j*s_ij. # #This model is usually scaled so that the mean rate of change at #equilibrium, Sum_i Sum_j!=i pi_i*q_ij, equals 1. You should check this #scaling before using the matrix above. The PAML package will perform #this scaling. # # # #*Prepared by Carolin Kosiol and Nick Goldman, December 2003. # #See the following paper for more details: #Kosiol, C., and Goldman, N. 2005. 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---- eval=TRUE---------------------------------------------------------- set.seed(1) bs <- bootstrap.phyDat(yeast, FUN = function(x)nj(dist.hamming(x)), bs=100) tree <- nj(dist.hamming(yeast)) par("mar" = rep(1, 4)) tree <- plotBS(tree, bs, "phylogram") cnet <- consensusNet(bs, .3) plot(cnet, "2D", show.edge.label=TRUE) ## ---- eval=FALSE--------------------------------------------------------- # plot(cnet) # # rotate 3d plot # play3d(spin3d(axis=c(0,1,0), rpm=6), duration=10) # # create animated gif file # movie3d(spin3d(axis=c(0,1,0), rpm=6), duration=10) ## ---- eval=TRUE---------------------------------------------------------- dm <- dist.hamming(yeast) nnet <- neighborNet(dm) par("mar" = rep(1, 4)) plot(nnet, "2D") ## ---- eval=TRUE---------------------------------------------------------- nnet <- addConfidences(nnet, tree) par("mar" = rep(1, 4)) plot(nnet, "2D", show.edge.label=TRUE) ## ---- eval=TRUE---------------------------------------------------------- tree2 <- rNNI(tree, 2) tree2 <- addConfidences(tree2, tree) # several support values are missing par("mar" = rep(1, 4)) plot(tree2, show.node.label=TRUE) ## ---- eval=TRUE---------------------------------------------------------- cnet <- nnls.networx(cnet, dm) par("mar" = rep(1, 4)) plot(cnet, "2D", show.edge.label=TRUE) phangorn/inst/doc/Trees.Rnw0000644000175100001440000003253613020643153015406 0ustar hornikusers%\VignetteIndexEntry{Constructing phylogenetic trees} %\VignetteKeywords{Documentation} %\VignettePackage{phangorn} %\VignetteEngine{Sweave} \documentclass[12pt]{article} \usepackage{times} \usepackage{hyperref} \begin{document} \SweaveOpts{concordance=TRUE} \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rmethod}[1]{{\texttt{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \textwidth=6.2in \textheight=8.5in %\parskip=.3cm \oddsidemargin=.1in \evensidemargin=.1in \headheight=-.3in \newcommand{\R}{\textsf{R}} \newcommand{\pml}{\Robject{pml}} \newcommand{\phangorn}{\Rpackage{phangorn}} \newcommand{\ape}{\Rpackage{ape}} \newcommand{\multicore}{\Rpackage{multicore}} \newcommand{\term}[1]{\emph{#1}} \newcommand{\mref}[2]{\htmladdnormallinkfoot{#2}{#1}} % leave comments in the text \SweaveOpts{keep.source=TRUE} % Ross Ihakas extenstion for nicer representation \DefineVerbatimEnvironment{Sinput}{Verbatim} {xleftmargin=2em} \DefineVerbatimEnvironment{Soutput}{Verbatim}{xleftmargin=2em} \DefineVerbatimEnvironment{Scode}{Verbatim}{xleftmargin=2em} \fvset{listparameters={\setlength{\topsep}{0pt}}} \renewenvironment{Schunk}{\vspace{\topsep}}{\vspace{\topsep}} <>= options(width=70) foo <- packageDescription("phangorn") options("show.signif.stars" = FALSE) @ \title{Estimating phylogenetic trees with phangorn} %$ (Version \Sexpr{foo$Version})} \author{\mref{mailto:klaus.schliep@gmail.com}{Klaus P. Schliep}} \maketitle \nocite{Paradis2012} \section{Introduction} These notes should enable the user to estimate phylogenetic trees from alignment data with different methods using the \phangorn{} package \cite{Schliep2011}. Several functions of \phangorn{} are also described in more detail in \cite{Paradis2012}. For more theoretical background on all the methods see e.g. \cite{Felsenstein2004, Yang2006}. This document illustrates some of the \phangorn{} features to estimate phylogenetic trees using different reconstruction methods. Small adaptations to the scripts in section \ref{sec:Appendix1} should enable the user to perform phylogenetic analyses. \section{Getting started} The first thing we have to do is to read in an alignment. Unfortunately there exists many different file formats that alignments can be stored in. The function \Rfunction{read.phyDat} is used to read in an alignment. There are several functions to read in alignments depending on the format of the data set (nexus, phylip, fasta) and the kind of data (amino acid or nucleotides) in the \ape{} package \cite{Paradis2004} and \phangorn{}. The function \Rfunction{read.phyDat} calls these other functions. For the specific parameter settings available look in the help files of the function \Rfunction{read.dna} (for phylip, fasta, clustal format), \Rfunction{read.nexus.data} for nexus files. For amino acid data additional \Rfunction{read.aa} is called. %When using the \Rfunction{read.dna} from \ape{} the parameter the we have to use as.character=TRUE. We start our analysis loading the \phangorn{} package and then reading in an alignment. <>= library(phangorn) primates <- read.phyDat("primates.dna", format="phylip", type="DNA") @ \section{Distance based methods} After reading in the alignment we can build a first tree with distance based methods. The function dist.dna from the ape package computes distances for many DNA substitution models. To use the function dist.dna we have to transform the data to class DNAbin. For amino acids the function \Rfunction{dist.ml} offers common substitution models (for example "WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa" or "mtREV24"). After constructing a distance matrix we reconstruct a rooted tree with UPGMA and alternatively an unrooted tree using Neighbor Joining \cite{Saitou1987,Studier1988}. More distance methods like fastme are available in the \Rpackage{ape} package. <>= dm <- dist.ml(primates) treeUPGMA <- upgma(dm) treeNJ <- NJ(dm) @ We can plot the trees treeUPGMA and treeNJ (figure \ref{fig:NJ}) with the commands: <>= layout(matrix(c(1,2), 2, 1), height=c(1,2)) par(mar = c(0,0,2,0)+ 0.1) plot(treeUPGMA, main="UPGMA") plot(treeNJ, "unrooted", main="NJ") @ \begin{figure} \begin{center} <>= <> @ \end{center} \caption{Rooted UPGMA tree and unrooted NJ tree} \label{fig:NJ} \end{figure} Distance based methods are very fast and we will use the UPGMA and NJ tree as starting trees for the maximum parsimony and maximum likelihood analyses. \section{Parsimony} The function parsimony returns the parsimony score, that is the number of changes which are at least necessary to describe the data for a given tree. We can compare the parsimony score or the two trees we computed so far: <>= parsimony(treeUPGMA, primates) parsimony(treeNJ, primates) @ The function optim.parsimony performs tree rearrangements to find trees with a lower parsimony score. The tree rearrangement implemented are nearest-neighbor interchanges (NNI) and subtree pruning and regrafting (SPR). The later one only works so far with the fitch algorithm. However is also a version of the parsimony ratchet \cite{Nixon1999} implemented, which is likely to find better trees than just doing NNI / SPR rearrangements. <>= treePars <- optim.parsimony(treeUPGMA, primates) treeRatchet <- pratchet(primates, trace = 0) parsimony(c(treePars, treeRatchet), primates) @ For small data sets it is also possible to find all most parsimonious trees using a branch and bound algorithm \cite{Hendy1982}. For data sets with more than 10 taxa this can take a long time and depends strongly on how tree like the data are. <>= (trees <- bab(subset(primates,1:10))) @ \section{Maximum likelihood} The last method we will describe in this vignette is Maximum Likelihood (ML) as introduced by Felsenstein \cite{Felsenstein1981}. We can easily compute the likelihood for a tree given the data <>= fit = pml(treeNJ, data=primates) fit @ The function \Rfunction{pml} returns an object of class \Robject{pml}. This object contains the data, the tree and many different parameters of the model like the likelihood. There are many generic functions for the class Robject{pml} available, which allow the handling of these objects. <>= methods(class="pml") @ The object fit just estimated the likelihood for the tree it got supplied, but the branch length are not optimized for the Jukes-Cantor model yet, which can be done with the function \Rfunction{optim.pml}. <>= fitJC <- optim.pml(fit, TRUE) logLik(fitJC) @ With the default values \Rfunction{pml} will estimate a Jukes-Cantor model. The function \Rfunction{update.pml} allows to change parameters. We will change the model to the GTR + $\Gamma(4)$ + I model and then optimize all the parameters. <>= fitGTR <- update(fit, k=4, inv=0.2) fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE, rearrangement = "NNI", control = pml.control(trace = 0)) fitGTR @ With the control parameters the threshols for the fitting process can be changed. Here we want just to supress output during the fitting process. For larger trees the NNI rearrangements often get stuck in local maxima. We added two stochatic algorithm to improve topology search. The first (set rearrangement="stochastic") performs stochastic rearrangements similar as in \cite{Nguyen2015}, which makes random NNI permuation to the tree, which than gets optimised to escape local optima. The second option (rearrangement="ratchet") perform the likelihood ratchet \cite{Vos2003}. While these algorithms may find better trees they will also take more time. <>= fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE, rearrangement = "stochastic", control = pml.control(trace = 0)) fitGTR @ \subsection{Model selection} We can compare nested models for the JC and GTR + $\Gamma(4)$ + I model using likelihood ratio statistic <>= anova(fitJC, fitGTR) @ with the Shimodaira-Hasegawa \cite{Shimodaira1999} test <>= SH.test(fitGTR, fitJC) @ or with the AIC <>= AIC(fitJC) AIC(fitGTR) AICc(fitGTR) BIC(fitGTR) @ An alternative is to use the function \Rfunction{modelTest} to compare different nucleotide or protein models the AIC, AICc or BIC, similar to popular programs ModelTest and ProtTest \cite{Posada1998, Posada2008, Abascal2005}. <>= load("Trees.RData") @ <>= mt = modelTest(primates) @ The results of \Rfunction{modelTest} is illustrated in table \ref{tab:modelTest} \begin{center} <>= library(xtable) print(xtable(mt, caption="Summary table of modelTest", label="tab:modelTest"), include.rownames=FALSE) @ \end{center} The thresholds for the optimization in \Rfunction{modelTest} are not as strict as for \Rfunction{optim.pml} and no tree rearrangements are performed. As \Rfunction{modelTest} computes and optimizes a lot of models it would be a waste of computer time not to save these results. The results are saved as call together with the optimized trees in an environment and this call can be evaluated to get a "pml" object back to use for further optimization or analysis. <>= env <- attr(mt, "env") ls(envir=env) (fit <- eval(get("HKY+G+I", env), env)) @ At last we may want to apply bootstrap to test how well the edges of the tree are supported: %, results=hide <>= bs = bootstrap.pml(fitJC, bs=100, optNni=TRUE, control = pml.control(trace = 0)) @ %$ Now we can plot the tree with the bootstrap support values on the edges and also look at consensusNet to identify potential conflict. <>= par(mfrow=c(2,1)) par(mar=c(1,1,3,1)) plotBS(midpoint(fitJC$tree), bs, p = 50, type="p") title("a)") cnet <- consensusNet(bs, p=0.2) plot(cnet, "2D", show.edge.label=TRUE) title("b)") @ %$ \begin{figure} \begin{center} <>= <> @ \end{center} \caption{a) Unrooted tree (midpoint rooted) with bootstrap support values. b) ConsensusNet from the bootstrap sample} \label{fig:BS} \end{figure} Several analyses, e.g. \Rfunction{bootstrap} and \Rfunction{modelTest}, can be computationally demanding, but as nowadays most computers have several cores one can distribute the computations using the \multicore{} package. However it is only possible to use this approach if R is running from command line ("X11"), but not using a GUI (for example "Aqua" on Macs) and unfortunately the \multicore{} package does not work at all under Windows. \newpage \section{Appendix: Standard scripts for nucleotide analysis}\label{sec:Appendix1} Here we provide two standard scripts which can be adapted for the most common tasks. Most likely the arguments for \Rfunction{read.phyDat} have to be adapted to accommodate your file format. Both scripts assume that the \multicore{} package works on your platform, see comments above. <>= options(prompt=" ") options(continue=" ") @ <>= library(phangorn) file="myfile" dat = read.phyDat(file) dm = dist.ml(dat, "F81") tree = NJ(dm) # as alternative for a starting tree: tree <- pratchet(dat) # parsimony tree tree <- nnls.phylo(tree, dm) # need edge weights # 1. alternative: quick and dirty: GTR + G fitStart = pml(tree, dat, k=4) fit = optim.pml(fitStart, model="GTR", optGamma=TRUE, rearrangement="stochastic") # 2. alternative: preper with modelTest mt <- modelTest(dat, tree=tree, multicore=TRUE) mt[order(mt$AICc),] # choose best model from the table according to AICc bestmodel <- mt$Model[which.min(mt$AICc)] env = attr(mt, "env") fitStart = eval(get("GTR+G+I", env), env) # or let R search the table fitStart = eval(get(bestmodel, env), env) # equivalent to: fitStart = eval(get("GTR+G+I", env), env) fit = optim.pml(fitStart, rearrangement = "stochastic", optGamma=TRUE, optInv=TRUE, model="GTR") bs = bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE) @ \newpage \section{Appendix 2: Standard scripts for amino acid analysis}%\label{sec:Appendix2} You can specify different several models build in which you can specify, e.g. "WAG", "JTT", "Dayhoff", "LG". Optimizing the rate matrix for amino acids is possible, but would take a long, a very long time and you will need to have a large alignement to estimate all the parameters. So make sure to set optBf=FALSE and optQ=FALSE in the function \Rfunction{optim.pml}, which is also the default. <>= library(phangorn) file="myfile" dat = read.phyDat(file, type = "AA") dm = dist.ml(dat, model="JTT") tree = NJ(dm) # parallel will only work safely from command line # and not at all windows (mt <- modelTest(dat, model=c("JTT", "LG", "WAG"), multicore=TRUE)) # run all available amino acid models (mt <- modelTest(dat, model="all", multicore=TRUE)) fitStart = eval(get(mt$Model[which.min(mt$BIC)], env), env) fitNJ = pml(tree, dat, model="JTT", k=4, inv=.2) fit = optim.pml(fitNJ, rearrangement = "stochastic", optInv=TRUE, optGamma=TRUE) fit bs = bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE) @ \newpage % \cite{Sugiura1978} \bibliographystyle{plain} \bibliography{phangorn} \section{Session Information} The version number of \R{} and packages loaded for generating the vignette were: <>= toLatex(sessionInfo()) @ \end{document} phangorn/inst/doc/phangorn-specials.R0000644000175100001440000000702013020643153017362 0ustar hornikusers### R code from vignette source 'phangorn-specials.Rnw' ################################################### ### code chunk number 1: phangorn-specials.Rnw:48-51 ################################################### options(width=70) options("show.signif.stars" = FALSE) foo <- packageDescription("phangorn") ################################################### ### code chunk number 2: phangorn-specials.Rnw:73-79 ################################################### library(phangorn) data = matrix(c("r","a","y","g","g","a","c","-","c","t","c","g", "a","a","t","g","g","a","t","-","c","t","c","a", "a","a","t","-","g","a","c","c","c","t","?","g"), dimnames = list(c("t1", "t2", "t3"),NULL), nrow=3, byrow=TRUE) data ################################################### ### code chunk number 3: phangorn-specials.Rnw:82-84 ################################################### gapsdata1 = phyDat(data) gapsdata1 ################################################### ### code chunk number 4: phangorn-specials.Rnw:87-90 ################################################### gapsdata2 = phyDat(data, type="USER", levels=c("a","c","g","t","-"), ambiguity = c("?", "n")) gapsdata2 ################################################### ### code chunk number 5: phangorn-specials.Rnw:94-109 ################################################### contrast = matrix(data = c(1,0,0,0,0, 0,1,0,0,0, 0,0,1,0,0, 0,0,0,1,0, 1,0,1,0,0, 0,1,0,1,0, 0,0,0,0,1, 1,1,1,1,0, 1,1,1,1,1), ncol = 5, byrow = TRUE) dimnames(contrast) = list(c("a","c","g","t","r","y","-","n","?"), c("a", "c", "g", "t", "-")) contrast gapsdata3 = phyDat(data, type="USER", contrast=contrast) gapsdata3 ################################################### ### code chunk number 6: phangorn-specials.Rnw:165-170 ################################################### tree = unroot(rtree(3)) fit = pml(tree, gapsdata3) fit = optim.pml(fit, optQ=TRUE, subs=c(1,0,1,2,1,0,2,1,2,2), control=pml.control(trace=0)) fit ################################################### ### code chunk number 7: phangorn-specials.Rnw:233-243 ################################################### library(phangorn) primates = read.phyDat("primates.dna", format="phylip", type="DNA") tree <- NJ(dist.ml(primates)) dat <- phyDat(as.character(primates), "CODON") fit <- pml(tree, dat) fit0 <- optim.pml(fit, control = pml.control(trace = 0)) fit1 <- optim.pml(fit, model="codon1", control=pml.control(trace=0)) fit2 <- optim.pml(fit, model="codon2", control=pml.control(trace=0)) fit3 <- optim.pml(fit, model="codon3", control=pml.control(trace=0)) anova(fit0, fit2, fit3, fit1) ################################################### ### code chunk number 8: plotAll ################################################### trees = allTrees(5) par(mfrow=c(3,5), mar=rep(0,4)) for(i in 1:15)plot(trees[[i]], cex=1, type="u") ################################################### ### code chunk number 9: figAll ################################################### getOption("SweaveHooks")[["fig"]]() trees = allTrees(5) par(mfrow=c(3,5), mar=rep(0,4)) for(i in 1:15)plot(trees[[i]], cex=1, type="u") ################################################### ### code chunk number 10: phangorn-specials.Rnw:271-272 ################################################### trees = nni(trees[[1]]) ################################################### ### code chunk number 11: phangorn-specials.Rnw:283-284 ################################################### toLatex(sessionInfo()) phangorn/inst/doc/Networx.html0000644000175100001440000114645013020643153016172 0ustar hornikusers Splits and Networx

Splits and Networx

Klaus Schliep

December 03, 2016

This tutorial gives a basic introduction for constructing phylogenetic networks and adding parameters to trees or networx objects using phangorn (Schliep 2011) in R. Splits graphs or phylogenetic networks are a useful way to display conflicting data or to summarize different trees. Here, we present two popular networks, consensus networks (Holland et al. 2004) and Neighbor-Net (D. Bryant and Moulton 2004).
Trees or networks are often missing either edge weights or edge support values. We show how to improve a tree/networx object by adding support values or estimating edge weights using non-negative Least-Squares (nnls).

We first load the phangorn package and a few data sets we use in this vignette.

library(phangorn)
data(Laurasiatherian)
data(yeast)

consensusNet

A consensusNet (Holland et al. 2004) is a generalization of a consensus tree. Instead of only representing splits (taxon bipartitions) occurring in at least 50% of the trees in a bootstrap or MCMC sample one can use a lower threshold and explore competing splits. Note that, in its basic implementation used here, the consensusNet edge lengths are proportional to the frequence of the corresponding splits in the provided list of trees.

The input for consensusNet is a list of trees i.e. an object of class multiPhylo.

set.seed(1)
bs <- bootstrap.phyDat(yeast, FUN = function(x)nj(dist.hamming(x)), 
    bs=100)
tree <- nj(dist.hamming(yeast))
par("mar" = rep(1, 4))
tree <- plotBS(tree, bs, "phylogram")

cnet <- consensusNet(bs, .3)
plot(cnet, "2D", show.edge.label=TRUE)

In many cases, consensusNet will return more than two incompatible (competing) splits. This cannot be plotted as a planar (2-dimensional) graph. Such as situation requires a n-dimensional graph, where the maximum number of dimensions equals the maximum number of incompatible splits. For example, if we have three alternative incompatible splits: (A,B)|(C,D) vs. (A,C)|(B,D) vs. (A,D)|(B,C), we need a 3-dimensional graph to show all three alternatives. A nice way to get still a good impression of the network is to plot it in 3D.

plot(cnet)
# rotate 3d plot
play3d(spin3d(axis=c(0,1,0), rpm=6), duration=10)
# create animated gif file 
movie3d(spin3d(axis=c(0,1,0), rpm=6), duration=10)

which will result in a spinning graph similar to this

rotatingNetworx

rotatingNetworx

neighborNet

The function neighborNet implements the popular method of D. Bryant and Moulton (2004). The Neighbor-Net algorithm is essentially a 2D-version of the Neighbor joining algorithm. The Neighbour-net is computed in two steps: the first computes a circular ordering of the taxa in the data set; the second step involves the estimation of edge weights using non-negative Least-Squares (nnls).

dm <- dist.hamming(yeast)
nnet <- neighborNet(dm)
par("mar" = rep(1, 4))
plot(nnet, "2D")

The advantage of Neighbor-Net is that it returns a circular split system which can be always displayed in a planar (2D) graph. The plots displayed in phangorn may (however) not be planar, but re-plotting can give you a planar graph. This unwanted behavior will be improved in future version. The rendering of the networx is done using the the fantastic igraph package (Csardi and Nepusz 2006).

Adding support values

We can use the generic function addConfidences to add (branch) support values from a tree, i.e. an object of class phylo to a networx, splits or phylo object. The Neighbor-Net object we computed above provides no support values. We can add the support values from the tree we computed to the splits (edges) shared by both objects.

nnet <- addConfidences(nnet, tree)
par("mar" = rep(1, 4))
plot(nnet, "2D", show.edge.label=TRUE)

Analogously, we can also add support values to a tree:

tree2 <- rNNI(tree, 2)
tree2 <- addConfidences(tree2, tree)
# several support values are missing
par("mar" = rep(1, 4))
plot(tree2, show.node.label=TRUE)

Estimating edge weights (nnls)

Consensus networks, on the other hand, provide primarily information about support values corresponding to a split, but no information about the actual difference between the taxon bipartitions defined by that split. For example, one may be interested how the alternative support values correspond with the actual genetic distance between the involved taxa. Given a distance matrix, we can estimate edge weights using non-negative Least-Squares, and plot them onto the consensusNet splits graph.

cnet <- nnls.networx(cnet, dm)
par("mar" = rep(1, 4))
plot(cnet, "2D", show.edge.label=TRUE)

Import and export networks, advanced functions for networx objects

The functions read.nexus.networx and write.nexus.networx can read and write nexus files for or from SplitsTree (Huson and Bryant 2006). Check-out the new vignette IntertwiningTreesAndNetworks for additional functions, examples, and advanced applications.

References

Bryant, David, and Vincent Moulton. 2004. “Neighbor-Net: An Agglomerative Method for the Construction of Phylogenetic Networks.†Molecular Biology and Evolution 21 (2): 255–65. doi:10.1093/molbev/msh018.

Csardi, Gabor, and Tamas Nepusz. 2006. “The Igraph Software Package for Complex Network Research.†InterJournal Complex Systems: 1695. http://igraph.org.

Holland, Barbara R., Katharina T. Huber, Vincent Moulton, and Peter J. Lockhart. 2004. “Using Consensus Networks to Visualize Contradictory Evidence for Species Phylogeny.†Molecular Biology and Evolution 21 (7): 1459–61. doi:10.1093/molbev/msh145.

Huson, D.H., and D. Bryant. 2006. “Application of Phylogenetic Networks in Evolutionary Studies.†Molecular Biology and Evolution 23 (2): 254–67.

Schliep, Klaus Peter. 2011. “Phangorn: Phylogenetic Analysis in R.†Bioinformatics 27 (4): 592–93. doi:10.1093/bioinformatics/btq706.

phangorn/inst/doc/IntertwiningTreesAndNetworks.html0000644000175100001440000122666313020643153022375 0ustar hornikusers Intertwining phylogenetic trees and networks: R Example Script

Intertwining phylogenetic trees and networks: R Example Script

Klaus Schliep, Alastair Potts, David Morrison and Guido Grimm

December 03, 2016

Description: This script provides examples of the new functions available in the phangorn library to ‘intertwine’ trees and networks, i.e. compare trees and networks and data transferrance. It also provides a step-by-step guide for users new to R.

Methodological advancement: The major advancement in this phangorn update is the introduction of a generic network object with a wide range of related transfer and analysis functions. These new functions provide the first means to directly transfer information amongst a wide range of phylogenetic trees and networks, as well as means to visualise and further analyse this information. This should provide a platform for individuals to easily conduct tree-network comparisons and stimulate further function development by the community.

What next?: By implementing full network handling compatibility in R, and providing exemplar scripts (such as this) and support, the scientific community now has an easy means to analyse and compare the results of phylogenetic trees vs. network approaches. We hope this will open a largely unexplored world to the general phylogenetic audience.

Installing R

  1. Download R
    Select the nearest mirror to your location at https://cran.r-project.org/mirrors.html
  2. Select your operating system and download the relevant installation file.
  3. Install R following the instructions.

Installing the phangorn library

Open R and run the two lines of code below in the command line.
(You will need to select a region from which to download the library)

install.packages("phangorn", dependencies=TRUE)
# install latest development version needs devtools
install.packages("devtools", dependencies=TRUE)
library(devtools)
install_github("KlausVigo/phangorn")

Getting started

library(phangorn)    # load the phangorn library

Set the working directory

This is often a major stumbling block for new R users. You need to specify where in your folder structure you wish to work. i.e, where are the files stored that you wish to input?

This is done using the setwd() function, e.g. setwd(“C:/TreesNetworks/Example Filesâ€).

We now set it to the folder of the phangorn package, which contains the files we want to load for this example.

Read in the example file datasets:

This example files are based on the woodmouse dataset available in the ape library. Ultimately, this dataset is from this study: Michaux, J. R., Magnanou, E., Paradis, E., Nieberding, C. and Libois, R. (2003) Mitochondrial phylogeography of the Woodmouse (Apodemus sylvaticus) in the Western Palearctic region. Molecular Ecology, 12, 685-697.)

## automatically set the correct working directory for the examples below
# setwd(system.file("extdata/trees", package = "phangorn"))
# for this vignette we create a path to the files we want to load 
fdir <- system.file("extdata/trees", package = "phangorn")
## in your case it may look something like this...
# setwd("C:/TreesNetworks/Example Files")

##DNA Matrix, maybe not needed 
woodmouse <- read.phyDat(file.path(fdir, "woodmouse.fasta"),format="fasta") 
## RAxML best-known tree with bipartition support (from previous analysis)
raxml.tree <- read.tree(file.path(fdir,"RAxML_bipartitions.woodmouse"))
## RAxML bootstrap trees (from previous analysis)
raxml.bootstrap <- read.tree(file.path(fdir,"RAxML_bootstrap.woodmouse"))
## MrBayes consensus tree (50% majority rule) (from previous analysis)
mrbayes.tree <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.con"))
## MrBayes sample runs 1 and 2 (from previous analysis)
run1 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run1.t"))
run2 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run2.t"))
## How many trees are in the MrBayes tree sample?
run1
## 1001 phylogenetic trees
run2
## 1001 phylogenetic trees
## Combining the two runs and removing 25% burn-in
mrbayes.trees <- c(run1[251:1001],run2[251:1001])
## NeigbourNet Nexus file generated by SplitsTree (from previous analysis)
Nnet <- read.nexus.networx(file.path(fdir,"woodmouse.nxs"))

All example files read into R.

Viewing the data

par(mfrow=c(1,2), mar=c(2,2,2,2)) # Setting plot parameters
### Plotting trees with support values:
##  RAxML
plot(raxml.tree)
nodelabels(raxml.tree$node.label, adj = c(1, 0), frame = "none")
##  MrBayes
plot(mrbayes.tree)
nodelabels(mrbayes.tree$node.label, adj = c(1, 0), frame = "none")

par(mfrow=c(1,1)) # Setting plot parameters
# NeighbourNet
plot.networx(Nnet,"2D")

## alternatively,
# plot(Nnet,"2D")

Interwining trees and network functions

1A:

Identification of edge bundles (in black) in a neighbour-net (NN) network that correspond to branches (labelled 1-12) in a tree (a maximum likelihood tree, in this case).

# create a vector of labels for the network corresponding to edges in the tree
edge.lab <- createLabel(Nnet, raxml.tree, raxml.tree$edge[,2], "edge")
# could be also 1:27 instead of raxml.tree$edge[,2]

# Show the correspondingly labelled tree and network in R
par(mfrow=c(1,2), mar=c(1,1,1,1))
#plotBS(raxml.tree, rotate.tree = 180) 
plot(raxml.tree, "u", rotate.tree = 180) 
edgelabels(raxml.tree$edge[,2],col="blue", frame="none")

# find edges that are in the network but not in the tree
edge.col <- rep("black", nrow(Nnet$edge))
edge.col[ is.na(edge.lab) ] <- "red"
# or a simpler alternative...
edge.col <- createLabel(Nnet, raxml.tree, "black", nomatch="red")

x <- plot.networx(Nnet, edge.label = edge.lab, show.edge.label = T, "2D", edge.color = edge.col,
                  col.edge.label = "blue")

# the above plot function returns an invisible networx object and this object also  
# contains the colors for the edges.

1B:

Bootstrap support for all branches (branch labels) in the ML tree mapped on the corresponding edge bundles of the NN network. The edges in the network which are not found as ML tree branches are highlighted in red.

x <- addConfidences(Nnet,raxml.tree)
# find splits that are in the network but not in the tree
split.col <- rep("black", length(x$splits))
split.col[ !matchSplits(as.splits(x), as.splits(raxml.tree)) ] <- "red"

# simpler alternative...
split.col2 <- createLabel(x, raxml.tree, label="black", "split", nomatch="red")

# Plotting in R
par(mfrow=c(1,1))
out.x <- plot.networx(x,"2D",show.edge.label=TRUE, split.color=split.col, col.edge.label = "blue")

Again we can write to SplitsTree for viewing…

# write.nexus.networx(out.x,"woodmouse.tree.support.nxs")
## or we can also export the splits alone (for usage in software other than SplitsTree)
# write.nexus.splits(as.splits(out.x),"woodmouse.splits.support.nxs")

1C:

Frequencies of bipartitions found in the bootstrap pseudoreplicates mapped on the corresponding edge bundles of the NN network using a threshold of 10% (i.e. any edge is labelled that occurs in at least 100 of the 1000 ML-BS pseudoreplicates). Edge bundles not found in the ML tree are labelled using grey edges.

y <- addConfidences(Nnet, as.splits(raxml.bootstrap))
edge.col <- createLabel(y, raxml.tree, label="black", "edge", nomatch="grey")

y <- plot(y,"2D",show.edge.label=TRUE, edge.color=edge.col)

## Write to SplitsTree for viewing
# write.nexus.networx(y,"NN.with.bs.support.nxs")

Extras…

We can also compare the neighborNet with a consensusNet (Holland BR, Huber KT, Moulton V, Lockhart PJ,2004, Using consensus networks to visualize contradictory evidence for species phylogeny. Molecular Biology and Evolution, 21, 1459-1461). Furthermore, we can extract the support values from the consensus network, and place these on to the NeighbourNet (this is similar to the process explained in 1C above).

cnet <- consensusNet(raxml.bootstrap,prob=0.10)
edge.col <- createLabel(cnet, Nnet, label="black", "edge", nomatch="grey")
cnet <- plot.networx(cnet, "2D", show.edge.label = TRUE, edge.color=edge.col)

edge.col <- createLabel(Nnet, cnet, label="black", "edge", nomatch="grey")
z <- plot.networx(Nnet, "2D", show.edge.label = TRUE, edge.color=edge.col)

obj <- addConfidences(Nnet,cnet)
plot.networx(obj,"2D",show.edge.label=T, edge.color=edge.col, col.edge.label = "blue")

## Write to SplitsTree for viewing
# write.nexus.networx(obj,"Nnet.with.ML.Cnet.Bootstrap.nxs")
phangorn/inst/doc/IntertwiningTreesAndNetworks.Rmd0000644000175100001440000002171213020643153022136 0ustar hornikusers--- title: 'Intertwining phylogenetic trees and networks: R Example Script' author: "Klaus Schliep, Alastair Potts, David Morrison and Guido Grimm" date: "`r format(Sys.time(), '%B %d, %Y')`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{IntertwiningTreesAndNetworks} %\VignetteEngine{knitr::rmarkdown} %\usepackage[utf8]{inputenc} --- ```{r setup, echo=FALSE} # set global chunk options: images will be bigger knitr::opts_chunk$set(fig.width=7, fig.height=7) options(digits = 4) ``` *Description:* This script provides examples of the new functions available in the phangorn library to 'intertwine' trees and networks, i.e. compare trees and networks and data transferrance. It also provides a step-by-step guide for users new to R. *Methodological advancement:* The major advancement in this phangorn update is the introduction of a generic network object with a wide range of related transfer and analysis functions. These new functions provide the first means to directly transfer information amongst a wide range of phylogenetic trees and networks, as well as means to visualise and further analyse this information. This should provide a platform for individuals to easily conduct tree-network comparisons and stimulate further function development by the community. *What next?:* By implementing full network handling compatibility in R, and providing exemplar scripts (such as this) and [support](https://github.com/KlausVigo/phangorn), the scientific community now has an easy means to analyse and compare the results of phylogenetic trees vs. network approaches. We hope this will open a largely unexplored world to the general phylogenetic audience. ### Installing R 1. Download R Select the nearest mirror to your location at https://cran.r-project.org/mirrors.html 2. Select your operating system and download the relevant installation file. 3. Install R following the instructions. ### Installing the phangorn library Open R and run the two lines of code below in the command line. (You will need to select a region from which to download the library) ```{r, eval=FALSE} install.packages("phangorn", dependencies=TRUE) # install latest development version needs devtools install.packages("devtools", dependencies=TRUE) library(devtools) install_github("KlausVigo/phangorn") ``` ### Getting started ```{r} library(phangorn) # load the phangorn library ``` ### Set the working directory This is often a major stumbling block for new R users. You need to specify where in your folder structure you wish to work. i.e, where are the files stored that you wish to input? This is done using the setwd() function, e.g. setwd("C:/TreesNetworks/Example Files"). We now set it to the folder of the phangorn package, which contains the files we want to load for this example. ### Read in the example file datasets: This example files are based on the woodmouse dataset available in the ape library. Ultimately, this dataset is from this study: Michaux, J. R., Magnanou, E., Paradis, E., Nieberding, C. and Libois, R. (2003) Mitochondrial phylogeography of the Woodmouse (*Apodemus sylvaticus*) in the Western Palearctic region. Molecular Ecology, 12, 685-697.) ```{r} ## automatically set the correct working directory for the examples below # setwd(system.file("extdata/trees", package = "phangorn")) # for this vignette we create a path to the files we want to load fdir <- system.file("extdata/trees", package = "phangorn") ## in your case it may look something like this... # setwd("C:/TreesNetworks/Example Files") ##DNA Matrix, maybe not needed woodmouse <- read.phyDat(file.path(fdir, "woodmouse.fasta"),format="fasta") ## RAxML best-known tree with bipartition support (from previous analysis) raxml.tree <- read.tree(file.path(fdir,"RAxML_bipartitions.woodmouse")) ## RAxML bootstrap trees (from previous analysis) raxml.bootstrap <- read.tree(file.path(fdir,"RAxML_bootstrap.woodmouse")) ## MrBayes consensus tree (50% majority rule) (from previous analysis) mrbayes.tree <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.con")) ## MrBayes sample runs 1 and 2 (from previous analysis) run1 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run1.t")) run2 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run2.t")) ## How many trees are in the MrBayes tree sample? run1 run2 ## Combining the two runs and removing 25% burn-in mrbayes.trees <- c(run1[251:1001],run2[251:1001]) ## NeigbourNet Nexus file generated by SplitsTree (from previous analysis) Nnet <- read.nexus.networx(file.path(fdir,"woodmouse.nxs")) ``` All example files read into R. ### Viewing the data ```{r} par(mfrow=c(1,2), mar=c(2,2,2,2)) # Setting plot parameters ### Plotting trees with support values: ## RAxML plot(raxml.tree) nodelabels(raxml.tree$node.label, adj = c(1, 0), frame = "none") ## MrBayes plot(mrbayes.tree) nodelabels(mrbayes.tree$node.label, adj = c(1, 0), frame = "none") par(mfrow=c(1,1)) # Setting plot parameters # NeighbourNet plot.networx(Nnet,"2D") ## alternatively, # plot(Nnet,"2D") ``` ### Interwining trees and network functions ### 1A: Identification of edge bundles (in black) in a neighbour-net (NN) network that correspond to branches (labelled 1-12) in a tree (a maximum likelihood tree, in this case). ```{r} # create a vector of labels for the network corresponding to edges in the tree edge.lab <- createLabel(Nnet, raxml.tree, raxml.tree$edge[,2], "edge") # could be also 1:27 instead of raxml.tree$edge[,2] # Show the correspondingly labelled tree and network in R par(mfrow=c(1,2), mar=c(1,1,1,1)) #plotBS(raxml.tree, rotate.tree = 180) plot(raxml.tree, "u", rotate.tree = 180) edgelabels(raxml.tree$edge[,2],col="blue", frame="none") # find edges that are in the network but not in the tree edge.col <- rep("black", nrow(Nnet$edge)) edge.col[ is.na(edge.lab) ] <- "red" # or a simpler alternative... edge.col <- createLabel(Nnet, raxml.tree, "black", nomatch="red") x <- plot.networx(Nnet, edge.label = edge.lab, show.edge.label = T, "2D", edge.color = edge.col, col.edge.label = "blue") # the above plot function returns an invisible networx object and this object also # contains the colors for the edges. ``` ### 1B: Bootstrap support for all branches (branch labels) in the ML tree mapped on the corresponding edge bundles of the NN network. The edges in the network which are not found as ML tree branches are highlighted in red. ```{r} x <- addConfidences(Nnet,raxml.tree) # find splits that are in the network but not in the tree split.col <- rep("black", length(x$splits)) split.col[ !matchSplits(as.splits(x), as.splits(raxml.tree)) ] <- "red" # simpler alternative... split.col2 <- createLabel(x, raxml.tree, label="black", "split", nomatch="red") # Plotting in R par(mfrow=c(1,1)) out.x <- plot.networx(x,"2D",show.edge.label=TRUE, split.color=split.col, col.edge.label = "blue") ``` Again we can write to SplitsTree for viewing... ```{r} # write.nexus.networx(out.x,"woodmouse.tree.support.nxs") ## or we can also export the splits alone (for usage in software other than SplitsTree) # write.nexus.splits(as.splits(out.x),"woodmouse.splits.support.nxs") ``` ### 1C: Frequencies of bipartitions found in the bootstrap pseudoreplicates mapped on the corresponding edge bundles of the NN network using a threshold of 10% (i.e. any edge is labelled that occurs in at least 100 of the 1000 ML-BS pseudoreplicates). Edge bundles not found in the ML tree are labelled using grey edges. ```{r} y <- addConfidences(Nnet, as.splits(raxml.bootstrap)) edge.col <- createLabel(y, raxml.tree, label="black", "edge", nomatch="grey") y <- plot(y,"2D",show.edge.label=TRUE, edge.color=edge.col) ## Write to SplitsTree for viewing # write.nexus.networx(y,"NN.with.bs.support.nxs") ``` ### Extras... We can also compare the neighborNet with a consensusNet (Holland BR, Huber KT, Moulton V, Lockhart PJ,2004, Using consensus networks to visualize contradictory evidence for species phylogeny. Molecular Biology and Evolution, 21, 1459-1461). Furthermore, we can extract the support values from the consensus network, and place these on to the NeighbourNet (this is similar to the process explained in 1C above). ```{r} cnet <- consensusNet(raxml.bootstrap,prob=0.10) edge.col <- createLabel(cnet, Nnet, label="black", "edge", nomatch="grey") cnet <- plot.networx(cnet, "2D", show.edge.label = TRUE, edge.color=edge.col) edge.col <- createLabel(Nnet, cnet, label="black", "edge", nomatch="grey") z <- plot.networx(Nnet, "2D", show.edge.label = TRUE, edge.color=edge.col) obj <- addConfidences(Nnet,cnet) plot.networx(obj,"2D",show.edge.label=T, edge.color=edge.col, col.edge.label = "blue") ## Write to SplitsTree for viewing # write.nexus.networx(obj,"Nnet.with.ML.Cnet.Bootstrap.nxs") ``` phangorn/inst/doc/Ancestral.pdf0000644000175100001440000073735513020643153016256 0ustar hornikusers%PDF-1.5 %ÐÔÅØ 25 0 obj << /Length 1821 /Filter /FlateDecode >> stream xÚXÝsÓFÏ_¡É“ÝÁÇÝét’˜¡SÊZ:iÉôò 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Schliep}} \maketitle \nocite{Paradis2006} \section{Introduction} These notes describe the ancestral sequence reconstruction using the \phangorn{} package \cite{Schliep2011}. \phangorn{} provides several methods to estimate ancestral character states with either Maximum Parsimony (MP) or Maximum Likelihood (ML). %For more background on all the methods see e.g. \cite{Felsenstein2004, Yang2006}. \section{Parsimony reconstructions} To reconstruct ancestral sequences we first load some data and reconstruct a tree: <>= library(phangorn) primates = read.phyDat("primates.dna", format = "phylip", type = "DNA") tree = pratchet(primates, trace=0) tree = acctran(tree, primates) parsimony(tree, primates) @ For parsimony analysis of the edge length represent the observed number of changes. Reconstructing ancestral states therefore defines also the edge lengths of a tree. However there can exist several equally parsimonious reconstructions or states can be ambiguous and therefore edge length can differ. %\phangorn{} brakes them equally down. "MPR" reconstructs the ancestral states for each (internal) node as if the tree would be rooted in that node. However the nodes are not independent of each other. If one chooses one state for a specific node, this can restrict the choice of neighbouring nodes (figure \ref{fig:Pars}). The function acctran (accelerated transformation) assigns edge length and internal nodes to the tree \cite{Swofford1987}. <>= anc.acctran = ancestral.pars(tree, primates, "ACCTRAN") anc.mpr = ancestral.pars(tree, primates, "MPR") @ All the ancestral reconstructions for parsimony are based on the fitch algorithm and so far only bifurcating trees are allowed. However trees can get pruned afterwards using the function \Rfunction{multi2di} from \ape{}. <>= tmp <- require(seqLogo) if(tmp) seqLogo( t(subset(anc.mpr, getRoot(tree), 1:20)[[1]]), ic.scale=FALSE) @ \begin{figure} \begin{center} <>= <> @ \caption{Representation for the reconstruction of the first 20 characters for the root node.} \end{center} \end{figure} <>= options(SweaveHooks=list(fig=function() par(mar=c(2.1, 4.1, 2.1, 2.1)))) @ <>= par(mfrow=c(2,1)) plotAnc(tree, anc.mpr, 17) title("MPR") plotAnc(tree, anc.acctran, 17) title("ACCTRAN") @ \begin{figure} \begin{center} <>= <> @ \caption{Ancestral reconstruction for one character for the "MPR" and "ACCTRAN" reconstruction. When nodes contain several colours reconstruction is not unique!}\label{fig:Pars} \end{center} \end{figure} \section{Likelihood reconstructions} \phangorn{} also offers the possibility to estimate ancestral states using a ML. The advantages of ML over parsimony is that the reconstruction accounts for different edge lengths. So far only a marginal construction is implemented (see \cite{Yang2006}). <>= fit = pml(tree, primates) fit = optim.pml(fit, model="F81", control = pml.control(trace=0)) @ We can assign the ancestral states according to the highest likelihood ("ml"): \[ P(x_r = A) = \frac{L(x_r=A)}{\sum_{k \in \{A,C,G,T\}}L(x_r=k)} \] and the highest posterior probability ("bayes") criterion: \[ P(x_r=A) = \frac{\pi_A L(x_r=A)}{\sum_{k \in \{A,C,G,T\}}\pi_k L(x_r=k)}, \] where $L(x_r)$ is the joint probability of states at the tips and the state at the root $x_r$ and $\pi_i$ are the estimated base frequencies of state $i$. Both methods agree if all states (base frequencies) have equal probabilities. <>= anc.ml = ancestral.pml(fit, "ml") anc.bayes = ancestral.pml(fit, "bayes") @ The differences of the two approaches for a specific site (17) are represented in figure\ref{fig:MLB}. <>= par(mfrow=c(2,1)) plotAnc(tree, anc.ml, 17) title("ML") plotAnc(tree, anc.bayes, 17) title("Bayes") @ \begin{figure} \begin{center} <>= <> @ \caption{Ancestral reconstruction for fig.\ref{fig:Pars} the using the maximum likelihood and (empirical) Bayesian reconstruction.}\label{fig:MLB} \end{center} \end{figure} \bibliographystyle{plain} \bibliography{phangorn} \section{Session Information} The version number of \R{} and packages loaded for generating the vignette were: <>= toLatex(sessionInfo()) @ \end{document} phangorn/inst/doc/phangorn-specials.Rnw0000644000175100001440000003515313020643153017737 0ustar hornikusers%\VignetteIndexEntry{Advanced features} %\VignetteKeywords{Documentation} %\VignettePackage{phangorn} %\VignetteEngine{Sweave} \documentclass[12pt]{article} % setwd("/home/kschliep/Desktop/phangorn/vignettes") % Sweave("phangorn-specials.Rnw") % tools::texi2dvi("phangorn-specials.tex", pdf=TRUE) \usepackage{times} \usepackage{hyperref} \usepackage{amsmath} \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rmethod}[1]{{\texttt{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \textwidth=6.2in \textheight=8.5in %\parskip=.3cm \oddsidemargin=.1in \evensidemargin=.1in \headheight=-.3in \newcommand{\R}{\textsf{R}} \newcommand{\pml}{\Robject{pml}} \newcommand{\phangorn}{\Rpackage{phangorn}} \newcommand{\ape}{\Rpackage{ape}} \newcommand{\multicore}{\Rpackage{multicore}} \newcommand{\term}[1]{\emph{#1}} \newcommand{\mref}[2]{\htmladdnormallinkfoot{#2}{#1}} \begin{document} \SweaveOpts{concordance=TRUE} % Ross Ihakas extenstion for nicer representation \DefineVerbatimEnvironment{Sinput}{Verbatim} {xleftmargin=2em} \DefineVerbatimEnvironment{Soutput}{Verbatim}{xleftmargin=2em} \DefineVerbatimEnvironment{Scode}{Verbatim}{xleftmargin=2em} \fvset{listparameters={\setlength{\topsep}{0pt}}} \renewenvironment{Schunk}{\vspace{\topsep}}{\vspace{\topsep}} <>= options(width=70) options("show.signif.stars" = FALSE) foo <- packageDescription("phangorn") @ \title{Special features of phangorn (Version \Sexpr{foo$Version})} %$ \author{\mref{mailto:klaus.schliep@gmail.com}{Klaus P. Schliep}} \maketitle \nocite{Paradis2012} \section*{Introduction} This document illustrates some of the \phangorn{} \cite{Schliep2011} specialised features which are useful but maybe not as well-known or just not (yet) described elsewhere. This is mainly interesting for someone who wants to explore different models or set up some simulation studies. We show how to construct data objects for different character states other than nucleotides or amino acids or how to set up different models to estimate transition rate. The vignette \emph{Trees} describes in detail how to estimate phylogenies from nucleotide or amino acids. \section{User defined data formats}\label{sec:USER} To better understand how to define our own data type it is useful to know a bit more about the internal representation of \Robject{phyDat} objects. The internal representation of \Robject{phyDat} object is very similar to \Robject{factor} objects. As an example we will show here several possibilities to define nucleotide data with gaps defined as a fifth state. Ignoring gaps or coding them as ambiguous sites - as it is done in most programs, also in phangorn as default - may be misleading (see Warnow(2012)\cite{Warnow2012}). When the number of gaps is low and the gaps are missing at random coding gaps as separate state may be not important. Let assume we have given a matrix where each row contains a character vector of a taxonomical unit: <>= library(phangorn) data = matrix(c("r","a","y","g","g","a","c","-","c","t","c","g", "a","a","t","g","g","a","t","-","c","t","c","a", "a","a","t","-","g","a","c","c","c","t","?","g"), dimnames = list(c("t1", "t2", "t3"),NULL), nrow=3, byrow=TRUE) data @ Normally we would transform this matrix into an phyDat object and gaps are handled as ambiguous character like "?". <<>>= gapsdata1 = phyDat(data) gapsdata1 @ Now we will define a "USER" defined object and have to supply a vector levels of the character states for the new data, in our case the for nucleotide states and the gap. Additional we can define ambiguous states which can be any of the states. <>= gapsdata2 = phyDat(data, type="USER", levels=c("a","c","g","t","-"), ambiguity = c("?", "n")) gapsdata2 @ This is not yet what we wanted as two sites of our alignment, which contain the ambiguous characters "r" and "y", got deleted. To define ambiguous characters like "r" and "y" explicitly we have to supply a contrast matrix similar to contrasts for factors. <>= contrast = matrix(data = c(1,0,0,0,0, 0,1,0,0,0, 0,0,1,0,0, 0,0,0,1,0, 1,0,1,0,0, 0,1,0,1,0, 0,0,0,0,1, 1,1,1,1,0, 1,1,1,1,1), ncol = 5, byrow = TRUE) dimnames(contrast) = list(c("a","c","g","t","r","y","-","n","?"), c("a", "c", "g", "t", "-")) contrast gapsdata3 = phyDat(data, type="USER", contrast=contrast) gapsdata3 @ Here we defined "n" as a state which can be any nucleotide but not a gap "-" and "?" can be any state including a gap. These data can be used in all functions available in \phangorn{} to compute distance matrices or perform parsimony and maximum likelihood analysis. \section{Markov models of character evolution} To model nucleotide substitutions across the edges of a tree T we can assign a transition matrix. In the case of nucleotides, with four character states, each 4 $\times$ 4 transition matrix has, at most, 12 free parameters. Time-reversible Markov models are used to describe how characters change over time, and use fewer parameters. Time-reversible means that these models need not be directed in time, and the Markov property states that these models depend only on the current state. These models are used in analysis of phylogenies using maximum likelihood and MCMC, computing pairwise distances, as well in simulating sequence evolution. We will now describe the General Time-Reversible (GTR) model \cite{Tavare1986}. The parameters of the GTR model are the equilibrium frequencies $\pi = (\pi_A ,\pi_C ,\pi_G ,\pi_T)$ and a rate matrix $Q$ which has the form \begin{equation}\label{eq:GTR} Q = \begin{pmatrix} \ast & \alpha\pi_C & \beta\pi_G & \gamma\pi_T \\ \alpha\pi_A & \ast & \delta\pi_G & \epsilon\pi_T \\ \beta\pi_A & \delta\pi_C & \ast & \eta\pi_T \\ \gamma\pi_A & \epsilon\pi_C & \eta\pi_G & \ast \\ \end{pmatrix} \end{equation} where we need to assign 6 paramters $\alpha, \dots, \eta$. The elements on the diagonal are chosen so that the rows sum to zero. The Jukes-Cantor (JC) \cite{Jukes1969} model can be derived as special case from the GTR model, for equal equilibrium frequencies $\pi_A = \pi_C = \pi_G = \pi_T = 0.25$ and equal rates set to $\alpha = \beta = \gamma = \delta = \eta$. Table \ref{models} lists all built-in nucleotide models in \phangorn. The transition probabilities which describe the probabilities of change from character $i$ to $j$ in time $t$, are given by the corresponding entries of the matrix exponential \[ P(t) = (p_{ij}(t)) = e^{Qt}, \qquad \sum_j p_{ij}=1 \] where $P(t)$ is the transition matrix spanning a period of time $t$. \section{Estimation of non-standard transition rate matrices} In the last section \ref{sec:USER} we described how to set up user defined data formats. Now we describe how to estimate transition matrices with pml. Again for nucleotide data the most common models can be called directly in the \Rfunction{optim.pml} function (e.g. "JC69", "HKY", "GTR" to name a few). Table \ref{models} lists all the available nucleotide models, which can estimated directly in \Rfunction{optim.pml}. For amino acids several transition matrices are available ("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU", "Blossum62", "Dayhoff\_DCMut" and "JTT-DCMut") or can be estimated with \Rfunction{optim.pml}. For example Mathews et al. (2010) \cite{Mathews2010} used this function to estimate a phytochrome amino acid transition matrix. We will now show how to estimate a rate matrix with different transition ($\alpha$) and transversion ratio ($\beta$) and a fixed rate to the gap state ($\gamma$) - a kind of Kimura two-parameter model (K81) for nucleotide data with gaps as fifth state (see table \ref{gaps}). \begin{table}[htbp] \centering \begin{tabular}{l|lllll} & a & c & g & t & - \\ \hline a & & & & & \\ c & $\beta$ & & & & \\ g & $\alpha$ & $\beta$ & & & \\ t & $\beta$ & $\alpha$ & $\beta$ & & \\ - & $\gamma$ & $\gamma$ & $\gamma$ & $\gamma$ & \\ \end{tabular} \caption{Rate matrix K to optimise. }\label{gaps} \end{table} If we want to fit a non-standard transition rate matrices, we have to tell \Rfunction{optim.pml}, which transitions in Q get the same rate. The parameter vector subs accepts a vector of consecutive integers and at least one element has to be zero (these gets the reference rate of 1). Negative values indicate that there is no direct transition is possible and the rate is set to zero. <<>>= tree = unroot(rtree(3)) fit = pml(tree, gapsdata3) fit = optim.pml(fit, optQ=TRUE, subs=c(1,0,1,2,1,0,2,1,2,2), control=pml.control(trace=0)) fit @ Here are some conventions how the models are estimated: \\ If a model is supplied the base frequencies bf and rate matrix Q are optimised according to the model (nucleotides) or the adequate rate matrix and frequencies are chosen (for amino acids). If optQ=TRUE and neither a model or subs are supplied than a symmetric (optBf=FALSE) or reversible model (optBf=TRUE, i.e. the GTR for nucleotides) is estimated. This can be slow if the there are many character states, e.g. for amino acids. \begin{table}[htbp] \centering \begin{tabular}{|llllr|} \hline model & optQ & optBf & subs & df \\ \hline JC & FALSE & FALSE & $c(0, 0, 0, 0, 0, 0)$ & 0 \\ F81 & FALSE & TRUE & $c(0, 0, 0, 0, 0, 0)$ & 3 \\ K80 & TRUE & FALSE & $c(0, 1, 0, 0, 1, 0)$ & 1 \\ HKY & TRUE & TRUE & $c(0, 1, 0, 0, 1, 0)$ & 4 \\ TrNe & TRUE & FALSE & $c(0, 1, 0, 0, 2, 0)$ & 2 \\ TrN & TRUE & TRUE & $c(0, 1, 0, 0, 2, 0)$ & 5 \\ TPM1 & TRUE & FALSE & $c(0, 1, 2, 2, 1, 0)$ & 2 \\ K81 & TRUE & FALSE & $c(0, 1, 2, 2, 1, 0)$ & 2 \\ TPM1u & TRUE & TRUE & $c(0, 1, 2, 2, 1, 0)$ & 5 \\ TPM2 & TRUE & FALSE & $c(1, 2, 1, 0, 2, 0)$ & 2 \\ TPM2u & TRUE & TRUE & $c(1, 2, 1, 0, 2, 0)$ & 5 \\ TPM3 & TRUE & FALSE & $c(1, 2, 0, 1, 2, 0)$ & 2 \\ TPM3u & TRUE & TRUE & $c(1, 2, 0, 1, 2, 0)$ & 5 \\ TIM1e & TRUE & FALSE & $c(0, 1, 2, 2, 3, 0)$ & 3 \\ TIM1 & TRUE & TRUE & $c(0, 1, 2, 2, 3, 0)$ & 6 \\ TIM2e & TRUE & FALSE & $c(1, 2, 1, 0, 3, 0)$ & 3 \\ TIM2 & TRUE & TRUE & $c(1, 2, 1, 0, 3, 0)$ & 6 \\ TIM3e & TRUE & FALSE & $c(1, 2, 0, 1, 3, 0)$ & 3 \\ TIM3 & TRUE & TRUE & $c(1, 2, 0, 1, 3, 0)$ & 6 \\ TVMe & TRUE & FALSE & $c(1, 2, 3, 4, 2, 0)$ & 4 \\ TVM & TRUE & TRUE & $c(1, 2, 3, 4, 2, 0)$ & 7 \\ SYM & TRUE & FALSE & $c(1, 2, 3, 4, 5, 0)$ & 5 \\ GTR & TRUE & TRUE & $c(1, 2, 3, 4, 5, 0)$ & 8 \\ \hline \end{tabular} \caption{DNA models available in phangorn, how they are defined and number of parameters to estimate. The elements of the vector subs correspond to $\alpha, \dots, \eta$ in equation (\ref{eq:GTR})}\label{models} \end{table} \section{Codon substitution models} A special case of the transition rates are codon models. \phangorn{} now offers the possibility to estimate the $d_N/d_S$ ratio (sometimes called ka/ks), for an overview see \cite{Yang2014}. These functions extend the option to estimates the $d_N/d_S$ ratio for pairwise sequence comparison as it is available through the function \Rfunction{kaks} in \Rpackage{seqinr}. The transition rate between between codon $i$ and $j$ is defined as follows: \begin{eqnarray} q_{ij}=\left\{ \begin{array}{l@{\quad}l} 0 & \textrm{if i and j differ in more than one position} \\ \pi_j & \textrm{for synonymous transversion} \\ \pi_j\kappa & \textrm{for synonymous transition} \\ \pi_j\omega & \textrm{for non-synonymous transversion} \\ \pi_j\omega\kappa & \textrm{for non synonymous transition} \end{array} \right. \nonumber \end{eqnarray} where $\omega$ is the $d_N/d_S$ ratio, $\kappa$ the transition transversion ratio and $\pi_j$ is the the equilibrium frequencies of codon $j$. For $\omega\sim1$ the an amino acid change is neutral, for $\omega < 1$ purifying selection and $\omega > 1$ positive selection. There are four models available: "codon0", where both parameter $\kappa$ and $\omega$ are fixed to 1, "codon1" where both parameters are estimated and "codon2" or "codon3" where $\kappa$ or $\omega$ is fixed to 1. We compute the $d_N/d_S$ for some sequences given a tree using the ML functions \Rfunction{pml} and \Rfunction{optim.pml}. First we have to transform the the nucleotide sequences into codons (so far the algorithms always takes triplets). <>= library(phangorn) primates = read.phyDat("primates.dna", format="phylip", type="DNA") tree <- NJ(dist.ml(primates)) dat <- phyDat(as.character(primates), "CODON") fit <- pml(tree, dat) fit0 <- optim.pml(fit, control = pml.control(trace = 0)) fit1 <- optim.pml(fit, model="codon1", control=pml.control(trace=0)) fit2 <- optim.pml(fit, model="codon2", control=pml.control(trace=0)) fit3 <- optim.pml(fit, model="codon3", control=pml.control(trace=0)) anova(fit0, fit2, fit3, fit1) @ The models described here all assume equal frequencies for each codon (=1/61). One can optimise the codon frequencies setting the option to optBf=TRUE. As the convergence of the 61 parameters the convergence is likely slow set the maximal iterations to a higher value than the default (e.g. control = pml.control(maxit=50)). The "YN98" model \cite{Yang98} is similar to the "codon1", but addional optimises the codon frequencies. \section{Generating trees} \phangorn{} has several functions to generate tree topologies, which may are interesting for simulation studies. \Rfunction{allTrees} computes all possible bifurcating tree topologies either rooted or unrooted for up to 10 taxa. One has to keep in mind that the number of trees is growing exponentially, use \Rfunction(howmanytrees) from \ape{} as a reminder. %<>= %trees = allTrees(5) %@ <>= trees = allTrees(5) par(mfrow=c(3,5), mar=rep(0,4)) for(i in 1:15)plot(trees[[i]], cex=1, type="u") @ \begin{figure} \begin{center} <>= <> @ \end{center} \caption{all (15) unrooted trees with 5 taxa} \label{fig:NJ} \end{figure} \Rfunction{nni} returns a list of all trees which are one nearest neighbor interchange away. <>= trees = nni(trees[[1]]) @ \Rfunction{rNNI} and \Rfunction{rSPR} generate trees which are a defined number of NNI (nearest neighbor interchange) or SPR (subtree pruning and regrafting) away. \bibliographystyle{plain} \bibliography{phangorn} \section{Session Information} The version number of \R{} and packages loaded for generating the vignette were: <>= toLatex(sessionInfo()) @ \end{document} phangorn/inst/doc/Networx.Rmd0000644000175100001440000001237213020643153015742 0ustar hornikusers--- title: "Splits and Networx" author: "Klaus Schliep" date: "`r format(Sys.time(), '%B %d, %Y')`" output: rmarkdown::html_vignette bibliography: phangorn.bib vignette: > %\VignetteIndexEntry{Splits and Networx} %\VignetteEngine{knitr::rmarkdown} %\usepackage[utf8]{inputenc} --- This tutorial gives a basic introduction for constructing phylogenetic networks and adding parameters to trees or networx objects using [phangorn](https://cran.r-project.org/package=phangorn) [@Schliep2011] in R. Splits graphs or phylogenetic networks are a useful way to display conflicting data or to summarize different trees. Here, we present two popular networks, consensus networks [@Holland2004] and Neighbor-Net [@Bryant2004]. Trees or networks are often missing either edge weights or edge support values. We show how to improve a tree/networx object by adding support values or estimating edge weights using non-negative Least-Squares (nnls). We first load the phangorn package and a few data sets we use in this vignette. ```{r, eval=TRUE} library(phangorn) data(Laurasiatherian) data(yeast) ``` ## consensusNet A consensusNet [@Holland2004] is a generalization of a consensus tree. Instead of only representing splits (taxon bipartitions) occurring in at least 50% of the trees in a bootstrap or MCMC sample one can use a lower threshold and explore competing splits. Note that, in its basic implementation used here, the consensusNet edge lengths are proportional to the frequence of the corresponding splits in the provided list of trees. The input for `consensusNet` is a list of trees i.e. an object of class `multiPhylo`. ```{r, eval=TRUE} set.seed(1) bs <- bootstrap.phyDat(yeast, FUN = function(x)nj(dist.hamming(x)), bs=100) tree <- nj(dist.hamming(yeast)) par("mar" = rep(1, 4)) tree <- plotBS(tree, bs, "phylogram") cnet <- consensusNet(bs, .3) plot(cnet, "2D", show.edge.label=TRUE) ``` In many cases, `consensusNet` will return more than two incompatible (competing) splits. This cannot be plotted as a planar (2-dimensional) graph. Such as situation requires a n-dimensional graph, where the maximum number of dimensions equals the maximum number of incompatible splits. For example, if we have three alternative incompatible splits: (A,B)|(C,D) vs. (A,C)|(B,D) vs. (A,D)|(B,C), we need a 3-dimensional graph to show all three alternatives. A nice way to get still a good impression of the network is to plot it in 3D. ```{r, eval=FALSE} plot(cnet) # rotate 3d plot play3d(spin3d(axis=c(0,1,0), rpm=6), duration=10) # create animated gif file movie3d(spin3d(axis=c(0,1,0), rpm=6), duration=10) ``` which will result in a spinning graph similar to this ![rotatingNetworx](movie.gif) ## neighborNet The function `neighborNet` implements the popular method of @Bryant2004. The Neighbor-Net algorithm is essentially a 2D-version of the Neighbor joining algorithm. The Neighbour-net is computed in two steps: the first computes a circular ordering of the taxa in the data set; the second step involves the estimation of edge weights using non-negative Least-Squares (nnls). ```{r, eval=TRUE} dm <- dist.hamming(yeast) nnet <- neighborNet(dm) par("mar" = rep(1, 4)) plot(nnet, "2D") ``` The advantage of Neighbor-Net is that it returns a circular split system which can be always displayed in a planar (2D) graph. The plots displayed in `phangorn` may (however) not be planar, but re-plotting can give you a planar graph. This unwanted behavior will be improved in future version. The rendering of the `networx` is done using the the fantastic igraph package [@Csardi2006]. ## Adding support values We can use the generic function `addConfidences` to add (branch) support values from a tree, i.e. an object of class `phylo` to a `networx`, `splits` or `phylo` object. The Neighbor-Net object we computed above provides no support values. We can add the support values from the tree we computed to the splits (edges) shared by both objects. ```{r, eval=TRUE} nnet <- addConfidences(nnet, tree) par("mar" = rep(1, 4)) plot(nnet, "2D", show.edge.label=TRUE) ``` Analogously, we can also add support values to a tree: ```{r, eval=TRUE} tree2 <- rNNI(tree, 2) tree2 <- addConfidences(tree2, tree) # several support values are missing par("mar" = rep(1, 4)) plot(tree2, show.node.label=TRUE) ``` ## Estimating edge weights (nnls) Consensus networks, on the other hand, provide primarily information about support values corresponding to a split, but no information about the actual difference between the taxon bipartitions defined by that split. For example, one may be interested how the alternative support values correspond with the actual genetic distance between the involved taxa. Given a distance matrix, we can estimate edge weights using non-negative Least-Squares, and plot them onto the consensusNet splits graph. ```{r, eval=TRUE} cnet <- nnls.networx(cnet, dm) par("mar" = rep(1, 4)) plot(cnet, "2D", show.edge.label=TRUE) ``` ### Import and export networks, advanced functions for networx objects The functions `read.nexus.networx` and `write.nexus.networx` can read and write nexus files for or from SplitsTree [@Huson2006]. Check-out the new vignette IntertwiningTreesAndNetworks for additional functions, examples, and advanced applications. ## References phangorn/inst/doc/phangorn-specials.pdf0000644000175100001440000053736313020643153017754 0ustar hornikusers%PDF-1.5 %ÐÔÅØ 28 0 obj << /Length 324 >> stream concordance:phangorn-specials.tex:phangorn-specials.Rnw:1 37 1 1 0 9 1 1 6 19 1 1 2 1 0 1 4 3 0 1 1 9 0 2 2 1 0 1 1 7 0 1 2 1 3 2 0 1 1 7 0 1 2 1 1 1 11 10 0 1 2 1 0 1 1 14 0 2 1 7 0 1 2 53 1 1 2 1 0 1 1 1 2 1 0 1 1 19 0 1 2 60 1 1 2 1 0 9 1 11 0 1 2 8 1 1 2 1 0 2 1 3 0 1 2 1 1 1 -3 1 7 6 1 1 2 4 0 1 2 8 1 1 2 11 0 1 2 1 1 endstream endobj 33 0 obj << /Length 2169 /Filter /FlateDecode >> stream xÚ•Y[sã¶~÷¯àä‰êX 𚙶™$3Iw:mã6›}àR´¤Ä’Z¯ÿ}Ïe:Ù6[—šøšü5Ó>lŽ<Òopšiüx@ ø;Ã߆3üœæéÿ6ewÃxÜð$² ´ }ÀšÍû»¾üΦ‘É—–ðº4ÉÓ"ÚºðVŒ÷²ÞEàØ"þצ2q‚íœöÓnlh#°#ÙÅ£,QDuRð…+¤i’Z剅uHÿ_ÖlMš%Y‰P\bŠŒE¿AÍmwÚØ*þ&,h19hÞ2$ Í7Z°îÌF¦L¬É3Ö]TI 'µ-Š¤fÍß“©œ‰§ ·…æîÒN²þ<ƒ4Iç2Bb Ñqwi:3ïX@kÑt'FèŸéQìõ ¶ÅÕ´46:Ñ2ö:—÷÷ü2ê&d¹ ‘™:V€=šó¾h+ûÈ¥E®Âï@,ƒ¿÷²¾ÐlÝ… 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################################################### ### code chunk number 2: Ancestral.Rnw:62-67 ################################################### library(phangorn) primates = read.phyDat("primates.dna", format = "phylip", type = "DNA") tree = pratchet(primates, trace=0) tree = acctran(tree, primates) parsimony(tree, primates) ################################################### ### code chunk number 3: Ancestral.Rnw:73-75 ################################################### anc.acctran = ancestral.pars(tree, primates, "ACCTRAN") anc.mpr = ancestral.pars(tree, primates, "MPR") ################################################### ### code chunk number 4: plotLOGO ################################################### tmp <- require(seqLogo) if(tmp) seqLogo( t(subset(anc.mpr, getRoot(tree), 1:20)[[1]]), ic.scale=FALSE) ################################################### ### code chunk number 5: figLOGO ################################################### getOption("SweaveHooks")[["fig"]]() tmp <- require(seqLogo) if(tmp) seqLogo( t(subset(anc.mpr, getRoot(tree), 1:20)[[1]]), ic.scale=FALSE) ################################################### ### code chunk number 6: Ancestral.Rnw:94-96 ################################################### options(SweaveHooks=list(fig=function() par(mar=c(2.1, 4.1, 2.1, 2.1)))) ################################################### ### code chunk number 7: plotMPR ################################################### par(mfrow=c(2,1)) plotAnc(tree, anc.mpr, 17) title("MPR") plotAnc(tree, anc.acctran, 17) title("ACCTRAN") ################################################### ### code chunk number 8: figMPR ################################################### getOption("SweaveHooks")[["fig"]]() par(mfrow=c(2,1)) plotAnc(tree, anc.mpr, 17) title("MPR") plotAnc(tree, anc.acctran, 17) title("ACCTRAN") ################################################### ### code chunk number 9: Ancestral.Rnw:123-125 ################################################### fit = pml(tree, primates) fit = optim.pml(fit, model="F81", control = pml.control(trace=0)) ################################################### ### code chunk number 10: Ancestral.Rnw:137-139 ################################################### anc.ml = ancestral.pml(fit, "ml") anc.bayes = ancestral.pml(fit, "bayes") ################################################### ### code chunk number 11: plotMLB ################################################### par(mfrow=c(2,1)) plotAnc(tree, anc.ml, 17) title("ML") plotAnc(tree, anc.bayes, 17) title("Bayes") ################################################### ### code chunk number 12: figMLB ################################################### getOption("SweaveHooks")[["fig"]]() par(mfrow=c(2,1)) plotAnc(tree, anc.ml, 17) title("ML") plotAnc(tree, anc.bayes, 17) title("Bayes") ################################################### ### code chunk number 13: Ancestral.Rnw:163-164 ################################################### toLatex(sessionInfo()) phangorn/inst/doc/IntertwiningTreesAndNetworks.R0000644000175100001440000001240213020643153021611 0ustar hornikusers## ----setup, echo=FALSE--------------------------------------------------- # set global chunk options: images will be bigger knitr::opts_chunk$set(fig.width=7, fig.height=7) options(digits = 4) ## ---- eval=FALSE--------------------------------------------------------- # install.packages("phangorn", dependencies=TRUE) # # install latest development version needs devtools # install.packages("devtools", dependencies=TRUE) # library(devtools) # install_github("KlausVigo/phangorn") ## ------------------------------------------------------------------------ library(phangorn) # load the phangorn library ## ------------------------------------------------------------------------ ## automatically set the correct working directory for the examples below # setwd(system.file("extdata/trees", package = "phangorn")) # for this vignette we create a path to the files we want to load fdir <- system.file("extdata/trees", package = "phangorn") ## in your case it may look something like this... # setwd("C:/TreesNetworks/Example Files") ##DNA Matrix, maybe not needed woodmouse <- read.phyDat(file.path(fdir, "woodmouse.fasta"),format="fasta") ## RAxML best-known tree with bipartition support (from previous analysis) raxml.tree <- read.tree(file.path(fdir,"RAxML_bipartitions.woodmouse")) ## RAxML bootstrap trees (from previous analysis) raxml.bootstrap <- read.tree(file.path(fdir,"RAxML_bootstrap.woodmouse")) ## MrBayes consensus tree (50% majority rule) (from previous analysis) mrbayes.tree <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.con")) ## MrBayes sample runs 1 and 2 (from previous analysis) run1 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run1.t")) run2 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run2.t")) ## How many trees are in the MrBayes tree sample? run1 run2 ## Combining the two runs and removing 25% burn-in mrbayes.trees <- c(run1[251:1001],run2[251:1001]) ## NeigbourNet Nexus file generated by SplitsTree (from previous analysis) Nnet <- read.nexus.networx(file.path(fdir,"woodmouse.nxs")) ## ------------------------------------------------------------------------ par(mfrow=c(1,2), mar=c(2,2,2,2)) # Setting plot parameters ### Plotting trees with support values: ## RAxML plot(raxml.tree) nodelabels(raxml.tree$node.label, adj = c(1, 0), frame = "none") ## MrBayes plot(mrbayes.tree) nodelabels(mrbayes.tree$node.label, adj = c(1, 0), frame = "none") par(mfrow=c(1,1)) # Setting plot parameters # NeighbourNet plot.networx(Nnet,"2D") ## alternatively, # plot(Nnet,"2D") ## ------------------------------------------------------------------------ # create a vector of labels for the network corresponding to edges in the tree edge.lab <- createLabel(Nnet, raxml.tree, raxml.tree$edge[,2], "edge") # could be also 1:27 instead of raxml.tree$edge[,2] # Show the correspondingly labelled tree and network in R par(mfrow=c(1,2), mar=c(1,1,1,1)) #plotBS(raxml.tree, rotate.tree = 180) plot(raxml.tree, "u", rotate.tree = 180) edgelabels(raxml.tree$edge[,2],col="blue", frame="none") # find edges that are in the network but not in the tree edge.col <- rep("black", nrow(Nnet$edge)) edge.col[ is.na(edge.lab) ] <- "red" # or a simpler alternative... edge.col <- createLabel(Nnet, raxml.tree, "black", nomatch="red") x <- plot.networx(Nnet, edge.label = edge.lab, show.edge.label = T, "2D", edge.color = edge.col, col.edge.label = "blue") # the above plot function returns an invisible networx object and this object also # contains the colors for the edges. ## ------------------------------------------------------------------------ x <- addConfidences(Nnet,raxml.tree) # find splits that are in the network but not in the tree split.col <- rep("black", length(x$splits)) split.col[ !matchSplits(as.splits(x), as.splits(raxml.tree)) ] <- "red" # simpler alternative... split.col2 <- createLabel(x, raxml.tree, label="black", "split", nomatch="red") # Plotting in R par(mfrow=c(1,1)) out.x <- plot.networx(x,"2D",show.edge.label=TRUE, split.color=split.col, col.edge.label = "blue") ## ------------------------------------------------------------------------ # write.nexus.networx(out.x,"woodmouse.tree.support.nxs") ## or we can also export the splits alone (for usage in software other than SplitsTree) # write.nexus.splits(as.splits(out.x),"woodmouse.splits.support.nxs") ## ------------------------------------------------------------------------ y <- addConfidences(Nnet, as.splits(raxml.bootstrap)) edge.col <- createLabel(y, raxml.tree, label="black", "edge", nomatch="grey") y <- plot(y,"2D",show.edge.label=TRUE, edge.color=edge.col) ## Write to SplitsTree for viewing # write.nexus.networx(y,"NN.with.bs.support.nxs") ## ------------------------------------------------------------------------ cnet <- consensusNet(raxml.bootstrap,prob=0.10) edge.col <- createLabel(cnet, Nnet, label="black", "edge", nomatch="grey") cnet <- plot.networx(cnet, "2D", show.edge.label = TRUE, edge.color=edge.col) edge.col <- createLabel(Nnet, cnet, label="black", "edge", nomatch="grey") z <- plot.networx(Nnet, "2D", show.edge.label = TRUE, edge.color=edge.col) obj <- addConfidences(Nnet,cnet) plot.networx(obj,"2D",show.edge.label=T, edge.color=edge.col, col.edge.label = "blue") ## Write to SplitsTree for viewing # write.nexus.networx(obj,"Nnet.with.ML.Cnet.Bootstrap.nxs") phangorn/inst/doc/Trees.R0000644000175100001440000001744613020643153015044 0ustar hornikusers### R code from vignette source 'Trees.Rnw' ################################################### ### code chunk number 1: Trees.Rnw:49-52 ################################################### options(width=70) foo <- packageDescription("phangorn") options("show.signif.stars" = FALSE) ################################################### ### code chunk number 2: Trees.Rnw:68-71 ################################################### library(phangorn) primates <- read.phyDat("primates.dna", format="phylip", type="DNA") ################################################### ### code chunk number 3: Trees.Rnw:77-80 ################################################### dm <- dist.ml(primates) treeUPGMA <- upgma(dm) treeNJ <- NJ(dm) ################################################### ### code chunk number 4: plotNJ ################################################### layout(matrix(c(1,2), 2, 1), height=c(1,2)) par(mar = c(0,0,2,0)+ 0.1) plot(treeUPGMA, main="UPGMA") plot(treeNJ, "unrooted", main="NJ") ################################################### ### code chunk number 5: figNJ ################################################### getOption("SweaveHooks")[["fig"]]() layout(matrix(c(1,2), 2, 1), height=c(1,2)) par(mar = c(0,0,2,0)+ 0.1) plot(treeUPGMA, main="UPGMA") plot(treeNJ, "unrooted", main="NJ") ################################################### ### code chunk number 6: Trees.Rnw:102-104 ################################################### parsimony(treeUPGMA, primates) parsimony(treeNJ, primates) ################################################### ### code chunk number 7: Trees.Rnw:107-110 ################################################### treePars <- optim.parsimony(treeUPGMA, primates) treeRatchet <- pratchet(primates, trace = 0) parsimony(c(treePars, treeRatchet), primates) ################################################### ### code chunk number 8: Trees.Rnw:113-114 (eval = FALSE) ################################################### ## (trees <- bab(subset(primates,1:10))) ################################################### ### code chunk number 9: Trees.Rnw:120-122 ################################################### fit = pml(treeNJ, data=primates) fit ################################################### ### code chunk number 10: Trees.Rnw:125-126 ################################################### methods(class="pml") ################################################### ### code chunk number 11: Trees.Rnw:129-131 ################################################### fitJC <- optim.pml(fit, TRUE) logLik(fitJC) ################################################### ### code chunk number 12: Trees.Rnw:134-138 ################################################### fitGTR <- update(fit, k=4, inv=0.2) fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE, rearrangement = "NNI", control = pml.control(trace = 0)) fitGTR ################################################### ### code chunk number 13: Trees.Rnw:143-146 ################################################### fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE, rearrangement = "stochastic", control = pml.control(trace = 0)) fitGTR ################################################### ### code chunk number 14: Trees.Rnw:150-151 ################################################### anova(fitJC, fitGTR) ################################################### ### code chunk number 15: Trees.Rnw:154-155 ################################################### SH.test(fitGTR, fitJC) ################################################### ### code chunk number 16: Trees.Rnw:158-162 ################################################### AIC(fitJC) AIC(fitGTR) AICc(fitGTR) BIC(fitGTR) ################################################### ### code chunk number 17: Trees.Rnw:165-166 ################################################### load("Trees.RData") ################################################### ### code chunk number 18: Trees.Rnw:168-169 (eval = FALSE) ################################################### ## mt = modelTest(primates) ################################################### ### code chunk number 19: Trees.Rnw:173-175 ################################################### library(xtable) print(xtable(mt, caption="Summary table of modelTest", label="tab:modelTest"), include.rownames=FALSE) ################################################### ### code chunk number 20: Trees.Rnw:179-182 ################################################### env <- attr(mt, "env") ls(envir=env) (fit <- eval(get("HKY+G+I", env), env)) ################################################### ### code chunk number 21: Trees.Rnw:185-187 (eval = FALSE) ################################################### ## bs = bootstrap.pml(fitJC, bs=100, optNni=TRUE, ## control = pml.control(trace = 0)) ################################################### ### code chunk number 22: plotBS ################################################### par(mfrow=c(2,1)) par(mar=c(1,1,3,1)) plotBS(midpoint(fitJC$tree), bs, p = 50, type="p") title("a)") cnet <- consensusNet(bs, p=0.2) plot(cnet, "2D", show.edge.label=TRUE) title("b)") ################################################### ### code chunk number 23: figBS ################################################### getOption("SweaveHooks")[["fig"]]() par(mfrow=c(2,1)) par(mar=c(1,1,3,1)) plotBS(midpoint(fitJC$tree), bs, p = 50, type="p") title("a)") cnet <- consensusNet(bs, p=0.2) plot(cnet, "2D", show.edge.label=TRUE) title("b)") ################################################### ### code chunk number 24: Trees.Rnw:218-220 ################################################### options(prompt=" ") options(continue=" ") ################################################### ### code chunk number 25: Trees.Rnw:222-251 (eval = FALSE) ################################################### ## library(phangorn) ## file="myfile" ## dat = read.phyDat(file) ## dm = dist.ml(dat, "F81") ## tree = NJ(dm) ## # as alternative for a starting tree: ## tree <- pratchet(dat) # parsimony tree ## tree <- nnls.phylo(tree, dm) # need edge weights ## ## ## # 1. alternative: quick and dirty: GTR + G ## fitStart = pml(tree, dat, k=4) ## fit = optim.pml(fitStart, model="GTR", optGamma=TRUE, rearrangement="stochastic") ## ## # 2. alternative: preper with modelTest ## mt <- modelTest(dat, tree=tree, multicore=TRUE) ## mt[order(mt$AICc),] ## # choose best model from the table according to AICc ## bestmodel <- mt$Model[which.min(mt$AICc)] ## ## env = attr(mt, "env") ## fitStart = eval(get("GTR+G+I", env), env) ## ## # or let R search the table ## fitStart = eval(get(bestmodel, env), env) ## # equivalent to: fitStart = eval(get("GTR+G+I", env), env) ## fit = optim.pml(fitStart, rearrangement = "stochastic", ## optGamma=TRUE, optInv=TRUE, model="GTR") ## bs = bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE) ################################################### ### code chunk number 26: Trees.Rnw:257-278 (eval = FALSE) ################################################### ## library(phangorn) ## file="myfile" ## dat = read.phyDat(file, type = "AA") ## dm = dist.ml(dat, model="JTT") ## tree = NJ(dm) ## ## # parallel will only work safely from command line ## # and not at all windows ## (mt <- modelTest(dat, model=c("JTT", "LG", "WAG"), ## multicore=TRUE)) ## # run all available amino acid models ## (mt <- modelTest(dat, model="all", multicore=TRUE)) ## ## fitStart = eval(get(mt$Model[which.min(mt$BIC)], env), env) ## ## fitNJ = pml(tree, dat, model="JTT", k=4, inv=.2) ## fit = optim.pml(fitNJ, rearrangement = "stochastic", ## optInv=TRUE, optGamma=TRUE) ## fit ## ## bs = bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE) ################################################### ### code chunk number 27: Trees.Rnw:290-291 ################################################### toLatex(sessionInfo()) phangorn/tests/0000755000175100001440000000000012761653173013261 5ustar hornikusersphangorn/tests/testthat.R0000644000175100001440000000013412761653173015242 0ustar hornikuserslibrary(testthat) suppressPackageStartupMessages(library(phangorn)) test_check("phangorn") phangorn/tests/testthat/0000755000175100001440000000000013020771374015112 5ustar hornikusersphangorn/tests/testthat/test_superTree.R0000644000175100001440000000141313005450715020245 0ustar hornikuserscontext("superTree") tree <- rtree(50, rooted=FALSE) trees_simple <- nni(tree) trees <- rNNI(tree, sample(10, 100, replace = TRUE)) trees <- .uncompressTipLabel(trees) labels <- paste0("t", 1:50) trees2 <- lapply(trees, function(x)drop.tip(x, sample(labels, 10))) class(trees2) <- "multiPhylo" test_that("superTree", { ## check superTree skip_on_cran() simple_superTree <- superTree(trees_simple, rooted=FALSE) difficult_superTree <- superTree(trees2, rooted=FALSE) expect_equal(RF.dist(simple_superTree, tree) , 0) expect_equal(RF.dist(difficult_superTree, tree) , 0) rf_superTree <- superTree(trees, method="RF") spr_superTree <- superTree(trees, method="SPR") expect_gte(attr(rf_superTree, "score"), attr(spr_superTree, "score")) }) phangorn/tests/testthat/test_treeManipulation.R0000644000175100001440000000300713020400224021573 0ustar hornikuserscontext("treeManipulation") set.seed(42) tree <- rtree(100, rooted=FALSE) trees <- lapply(sample(10:500,50), function(x)tree <- rtree(x, rooted=FALSE) ) #Ancestors(x, node, type=c("all","parent")) #Children(x, node) #Siblings(x, node, include.self=FALSE) #Descendants(x, node, type=c("tips","children","all")) #Siblings(tree, 3) #mrca.phylo(tree, 1:3) #midpoint desc_108 <- Descendants(tree, 108)[[1]] node_108 <- mrca.phylo(tree, node=desc_108) test_that("ancestor, mrca, descendants", { ## check RF.dist and tree dist expect_equal(mrca.phylo(tree, node=desc_108), 108L) kids_108 <- Descendants(tree, 108, "children") expect_equal(Ancestors(tree, kids_108, "parent"), rep(108L, length(kids_108))) expect_equal(Siblings(tree, kids_108[1], include.self=TRUE), kids_108) }) test_that("allTrees", { ## allTrees expect_is(allTrees(6), "multiPhylo") expect_true(all(RF.dist(allTrees(6))>0)) }) test_that("nni", { ## nni expect_is(nni(tree), "multiPhylo") expect_true(all(RF.dist(nni(tree), tree)>0)) }) # TODO: check why rooted trees give error in development version test_that("midpoint", { # topology stays the same expect_equal( max( sapply(trees, function(x)RF.dist(x,midpoint(x)))), 0) # 2 * max(height) == max(cophenetic) expect_equal( max( node.depth.edgelength(midpoint(tree)) *2) , max(cophenetic(tree))) }) test_that("maxCladeCred", { tree <- rcoal(100) trees <- nni(tree) expect_equal(maxCladeCred(c(tree, trees)), tree) }) phangorn/tests/testthat/test_treedist.R0000644000175100001440000001037212761653173020131 0ustar hornikuserscontext("treedist") ## generate data set.seed(42) tree <- rtree(10, FALSE) trees <- nni(tree) tree1 <- read.tree(text="(t5:1.0,(t4:1.0,t3:1.0):1.0,(t1:1.0,t2:1.0):1.0);") tree2 <- read.tree(text="(t4:1.0,(t5:1.0,t3:1.0):1.0,(t1:1.0,t2:1.0):1.0);") tree3 <- read.tree(text="(t5:1.0,t4:1.0,t3:1.0,(t1:1.0,t2:1.0):1.0);") test_that("Robinson-Foulds distance", { ## check RF.dist and tree dist expect_that(RF.dist(tree1, tree1), is_equivalent_to(0)) expect_that(RF.dist(tree1, tree2), is_equivalent_to(2)) expect_that(RF.dist(tree1, tree3), is_equivalent_to(1)) expect_true( all(RF.dist(tree, trees)==2) ) expect_is(RF.dist(trees),"dist") expect_that(treedist(tree1, tree1)[1], is_equivalent_to(0)) expect_that(treedist(tree1, tree2)[1], is_equivalent_to(2)) expect_that(treedist(tree1, tree3)[1], is_equivalent_to(1)) }) test_that("Kuhner-Felsenstein distance (branch score difference)", { ## check RF.dist and tree dist expect_that(KF.dist(tree1, tree1), is_equivalent_to(0)) expect_that(KF.dist(tree1, tree2), is_equivalent_to(sqrt(2))) expect_that(KF.dist(tree1, tree3), is_equivalent_to(1)) expect_is(KF.dist(trees),"dist") expect_that(treedist(tree1, tree1)[2], is_equivalent_to(0)) expect_that(treedist(tree1, tree2)[2], is_equivalent_to(sqrt(2))) expect_that(treedist(tree1, tree3)[2], is_equivalent_to(1)) expect_equal(KF.dist(tree1, c(tree1, tree2, tree3)), c(0, sqrt(2), 1)) }) test_that("path distance", { ## check path dist expect_that(path.dist(tree1, tree1), is_equivalent_to(0)) expect_equal(path.dist(trees)[1:13] , path.dist(trees[[1]], trees[2:14])) expect_is(path.dist(trees),"dist") }) ############################ # new tests from Michelle ############################ # make simple trees with one pair of unmatched edges, unit branch lengths tr1 <- read.tree(text="((A:1,B:1):1,C:1,(D:1,E:1):1);") tr2 <- read.tree(text="((A:1,C:1):1,B:1,(D:1,E:1):1);") # make a tree with same topology as tr1 but varied branch lengths tr3 <- read.tree(text="((A:2,B:1):3,C:1,(D:1,E:2):1);") test_that("Distance between known trees matches calculation by hand", { expect_equal(wRF.dist(tr1,tr2),2) # one pair of unmatched edges, unit branch lengths expect_equal(RF.dist(tr1,tr2),2) # one pair of unmatched edges (branch lengths irrelevant) expect_equal(wRF.dist(tr1,tr3),4) # same topology, different branch lengths expect_equal(RF.dist(tr1,tr3),0) # same topology (branch lengths irrelevant) expect_equal(wRF.dist(tr2,tr3),6) # one pair of unmatched edges, varied branch lengths }) ############################ # test that RF and wRF give same values for trees with every edge = 1 ############################ test_that("When each tree has unit branch lengths, RF = wRF", { skip_on_cran() expect_equal( max( sapply(sample(10:500,50), function(x) { # test some random numbers of tips between 10 and 500 trees <- rmtree(20, x, rooted=FALSE, br=1) # generate 20 unrooted trees with the given number of tips and unit branch lengths max(abs(RF.dist(trees) - wRF.dist(trees))) # find maximum abs difference between RF and wRF distance (expect 0) })), # find max of all these 0) # expect equal to 0 # now the same for comparison of one tree with many expect_equal( max( sapply(sample(10:500,50), function(x) { # test some random numbers of tips between 10 and 500 trees <- rmtree(20, x, rooted=FALSE, br=1) # generate 20 unrooted trees with the given number of tips and unit branch lengths max(abs(RF.dist(trees[[1]], trees) - wRF.dist(trees[[1]], trees))) # find maximum abs difference between RF and wRF distance (expect 0) })), # find max of all these 0) # expect equal to 0 }) ############################# # test sprdist from leomrtns ############################# test_that("SPR distance", { ## check spr dist set.seed(123) tree1 <- rtree(100, rooted = FALSE) tree2 <- rSPR(tree1, 1) trees <- rSPR(tree1, 1:5) expect_equal(sprdist(tree1, tree2)[[1]], 1) expect_equal(sprdist(tree1, tree2)[[3]], RF.dist(tree1, tree2)) expect_equal(SPR.dist(tree1, trees), 1:5) expect_is(SPR.dist(trees), "dist") }) phangorn/tests/testthat/test_distances.R0000644000175100001440000000162413017642502020250 0ustar hornikuserscontext("distances") X <- allSitePattern(5) tree <- read.tree(text = "((t1:0.3,t2:0.3):0.1,(t3:0.3,t4:0.3):0.1,t5:0.5);") fit <- pml(tree,X, k=4, shape=0.5) weights <- as.vector(1000*exp(fit$site)) attr(X, "weight") <- weights dm <- cophenetic(tree) Y <- phyDat(matrix(c("A", "C", "G", "T", "A", "C", "G", "A"), 2, 4, dimnames=list(c("a", "b", NULL)), byrow=TRUE)) fun <- function(s) - 3/4 * log(1 - 4/3 * s) test_that("dist.ml works properly", { # skip_on_cran() expect_that(dist.logDet(X), is_a("dist")) expect_that(dist.hamming(X), is_a("dist")) expect_that(dist.ml(X), is_a("dist")) expect_equal(as.matrix(dist.ml(X, k=4, shape=.5)), dm) expect_equal(as.matrix(dist.ml(Y)), as.matrix(fun(dist.hamming(Y)))) }) test_that("read/write of distances works", { skip_on_cran() writeDist(dm, "dm.txt") expect_equal(as.dist(dm), readDist("dm.txt")) unlink("dm.txt") }) phangorn/tests/testthat/test_dist_tree.R0000644000175100001440000000117013010227131020237 0ustar hornikuserscontext("dist_tree") data(Laurasiatherian) dm <- dist.ml(Laurasiatherian) test_that("check nnls functions", { tree_nj <- NJ(dm) tree_unj <- UNJ(dm) tree_nnls_unj <- nnls.phylo(tree_unj, dm) tree_nnls_nj <- nnls.phylo(tree_nj, dm) tree_upgma <- upgma(dm) tree_wpgma <- wpgma(dm) tree_nnls_upgma <- nnls.phylo(tree_upgma, dm, rooted=TRUE) tree_nnls_wpgma <- nnls.phylo(tree_wpgma, dm, rooted=TRUE) expect_equal(tree_upgma, tree_nnls_upgma) expect_false(all.equal(tree_wpgma, tree_nnls_wpgma)) expect_equal(tree_unj, tree_nnls_unj) expect_false(all.equal(tree_nj, tree_nnls_nj)) }) phangorn/tests/testthat/test_pml.R0000644000175100001440000001103512761653173017073 0ustar hornikuserscontext("likelihood") treeU1 = read.tree(text = "((t1:.1,t2:.1):.1,t3:.2,(t4:.1,t5:.1):.3);") treeU2 = read.tree(text = "((t1:.15,t2:.15):.05,t3:.2,(t4:.1,t5:.1):.3);") # edge length changed treeU3 = read.tree(text = "((t1:.1,t3:.1):.1,t2:.2,(t4:.1,t5:.1):.3);") # 1 NNI move treeR1 = read.tree(text = "(((t1:.1,t2:.1):.1,t3:.2):.1,(t4:.1,t5:.1):.2);") treeR2 = read.tree(text = "(((t1:.15,t2:.15):.05,t3:.2):.1,(t4:.1,t5:.1):.2);") # edge length changed treeR3 = read.tree(text = "(((t1:.1,t3:.1):.1,t2:.2):.1,(t4:.1,t5:.1):.2);") # 1 NNI move # dat <- phyDat(c(t1="a", t2="a",t3="t",t4="t"), type="USER", levels=c("a","c","g","t")) #tree2 = read.tree(text = "((t1,t3),t2,t4);") dat = allSitePattern(5) weights = as.vector(1000 * exp(pml(treeR1, dat)$siteLik)) attr(dat, "weight") = weights dat_Mk <- subset(dat, select = -c(1,342,683, 1024)) Q = c(6:1) pmlU1 = pml(treeU1, dat) pmlU2 = pml(treeU2, dat) pmlU2.fitted = optim.pml(pmlU2, control = pml.control(trace=0)) pmlU3 = pml(treeU3, dat) pmlU3.fitted = optim.pml(pmlU3, TRUE, control = pml.control(trace=0)) pmlR1 = pml(treeR1, dat) pmlR2 = pml(treeR2, dat) pmlR2.fitted = optim.pml(pmlR2, optRooted = TRUE, control = pml.control(epsilon=1e-10, trace=0)) pmlR3 = pml(treeR3, dat) pmlR3.fitted = optim.pml(pmlR3, TRUE, optRooted = TRUE, control = pml.control(epsilon=1e-10, trace=0)) test_that("edge length optimisation works properly", { skip_on_cran() expect_equal(logLik(pmlU2.fitted), logLik(pmlU1)) expect_equal(logLik(pmlR2.fitted), logLik(pmlR1)) expect_equal(pmlU2.fitted$tree, pmlU1$tree, tolerance=1e-6) # expect_equal(pmlR2.fitted$tree, pmlR1$tree, tolerance=5e-5) }) test_that("NNI optimisation works properly", { skip_on_cran() expect_equal(logLik(pmlU3.fitted), logLik(pmlU1)) expect_equal(logLik(pmlR3.fitted), logLik(pmlR1)) expect_equal(pmlU3.fitted$tree, pmlU1$tree, tolerance=1e-6) # expect_equal(pmlR3.fitted$tree, pmlR1$tree, tolerance=5e-6) }) test_that("bf optimisation works properly", { skip_on_cran() bf <- c(.1,.2,.3,.4) fit_T <- pml(treeU1, dat, bf=bf) weights <- as.vector(1000 * exp(fit_T$siteLik)) dat_tmp <- dat attr(dat_tmp, "weight") <- weights fit0 <- pml(treeU1, dat_tmp) fit.bf <- optim.pml(fit0, optEdge=FALSE, optBf = TRUE, control = pml.control(epsilon=1e-10, trace=0)) expect_equal(logLik(fit.bf), logLik(pml(treeU1, dat_tmp, bf=bf))) expect_equal(bf, fit.bf$bf, tolerance=5e-4) }) test_that("Q optimisation works properly", { skip_on_cran() Q <- c(6:1) fit_T <- pml(treeU1, dat, Q=Q) weights <- as.vector(1000 * exp(fit_T$siteLik)) dat_tmp <- dat attr(dat_tmp, "weight") <- weights fit0 <- pml(treeU1, dat_tmp) fit.Q <- optim.pml(fit0, optEdge=FALSE, optQ = TRUE, control = pml.control(epsilon=1e-10, trace=0)) expect_equal(logLik(fit.Q), logLik(pml(treeU1, dat_tmp, Q=Q))) expect_equal(Q, fit.Q$Q, tolerance=5e-4) }) test_that("Inv optimisation works properly", { skip_on_cran() inv <- 0.25 fit_T <- pml(treeU1, dat, inv=inv) weights <- as.vector(1000 * exp(fit_T$siteLik)) dat_tmp <- dat attr(dat_tmp, "weight") <- weights fit0 <- pml(treeU1, dat_tmp) fit.Inv <- optim.pml(fit0, optEdge=FALSE, optInv = TRUE, control = pml.control(epsilon=1e-10, trace=0)) expect_equal(logLik(fit.Inv), logLik(pml(treeU1, dat_tmp, inv=inv))) expect_equal(inv, fit.Inv$inv, tolerance=5e-4) }) test_that("Gamma optimisation works properly", { skip_on_cran() shape <- 2 fit_T <- pml(treeU1, dat, shape=shape, k=4) weights <- as.vector(1000 * exp(fit_T$siteLik)) dat_tmp <- dat attr(dat_tmp, "weight") <- weights fit0 <- pml(treeU1, dat_tmp, k=4) fit.Gamma <- optim.pml(fit0, optEdge=FALSE, optGamma = TRUE, control = pml.control(epsilon=1e-10, trace=0)) expect_equal(logLik(fit.Gamma), logLik(pml(treeU1, dat_tmp, shape = shape, k=4))) expect_equal(shape, fit.Gamma$shape, tolerance=5e-4) }) test_that("rate optimisation works properly", { skip_on_cran() rate <- 2 fit_T <- pml(treeU1, dat, rate=rate) weights <- as.vector(1000 * exp(fit_T$siteLik)) dat_tmp <- dat attr(dat_tmp, "weight") <- weights fit0 <- pml(treeU1, dat_tmp) fit.rate <- optim.pml(fit0, optEdge=FALSE, optRate = TRUE, control = pml.control(epsilon=1e-10, trace=0)) expect_equal(logLik(fit.rate), logLik(pml(treeU1, dat_tmp, rate=rate))) expect_equal(rate, fit.rate$rate, tolerance=5e-4) }) test_that("Mkv model works properly", { skip_on_cran() expect_equal(logLik(pmlU2.fitted), logLik(pmlU1)) }) ## 31.91%phangorn/tests/testthat/test_hadamard.R0000644000175100001440000000104712761653173020046 0ustar hornikuserscontext("Hadamard conjugation") v <- 1:8 data(yeast) dm <- dist.hamming(yeast) # RY-coding yeast_ry <- acgt2ry(yeast) # delete ambiguous states # dat4 <- phyDat(as.character(yeast), type="USER", levels=c("a","c", "g", "t"), ambiguity=NULL) # fit4 <- h4st(dat4) test_that("Hadamard conjugation works as expected", { # fast fft like multiplication expect_is(H <- hadamard(3), "matrix") expect_equal(as.vector(H %*% v), fhm(v)) expect_is(spl_ry <- h2st(yeast_ry), "splits") expect_is(spl_dm <- distanceHadamard(dm), "splits") }) phangorn/tests/testthat/test_splits.R0000644000175100001440000000312412766054523017620 0ustar hornikuserscontext("Test conversions") ## generate data set.seed(1) tree <- rtree(10, FALSE) tree2spl <- as.splits(tree) spl2tree <- as.phylo(tree2spl) dm <- cophenetic(tree2spl) mat <- as.matrix(tree2spl) Mat <- as.Matrix(tree2spl) trees <- nni(tree) test_that("splits ", { ## skip on CRAN skip_on_cran() ## check classes expect_is(as.splits(trees), "splits") expect_is(tree2spl, "splits") expect_is(spl2tree,"phylo") expect_is(dm,"dist") expect_is(mat, "matrix") expect_is(Mat, "Matrix") expect_equal(spl2tree , tree) spl <- allCircularSplits(6) spl <- phangorn:::oneWise(spl, 6) write.nexus.splits(spl, "tmp.nex") spl2 <- read.nexus.splits("tmp.nex") attr(spl2, "splitlabels") <- NULL attr(spl2, "weights") <- NULL class(spl2) <- "splits" expect_equal(spl2 , spl) unlink("tmp.nex") }) test_that("networx ", { net1 <- neighborNet(dm) write.nexus.networx(net1, "tmp.nex") net2 <- read.nexus.networx("tmp.nex") net3 <- as.networx(tree) # delete some additional attributes net2$.plot <- net2$translate <- NULL attr(net1, "order") = NULL expect_is(net1, "networx") expect_is(net2, "networx") expect_is(net3, "networx") # expect_equal(net1, net2, tolerance=1e-6) # expect_equal(net3, net2, tolerance=1e-6) expect_equal(net1, net3) unlink("tmp.nex") cnet <- consensusNet(as.splits(trees)) expect_is(cnet, "networx") net1$edge.length <- cnet$edge.length <- cnet$edge.labels <- NULL attr(cnet, "order") = NULL expect_equal(cnet, net1) }) phangorn/tests/testthat/test_phyDat.R0000644000175100001440000000547613005450715017535 0ustar hornikuserscontext("conversion_and_subsetting") # to test: phyDat.codon, data(Laurasiatherian) data(chloroplast) set.seed(42) tree <- rtree(10) codon_align <- simSeq(tree, l=100, type = "CODON") phy_matrix <- as.character(Laurasiatherian) phy_df <- as.data.frame(Laurasiatherian) phy_vec_dna <- phy_matrix[,1] phy_vec_user <- sample(c("0","1"), 26, replace=TRUE) names(phy_vec_user) <- letters phy_dnabin <- as.DNAbin(Laurasiatherian) phy_align <- phyDat2alignment(Laurasiatherian) test_that("conversion work as expected", { ## skip_on_cran() expect_is(phy_matrix, "matrix") expect_is(phy_df, "data.frame") expect_is(phy_dnabin, "DNAbin") expect_is(phy_align, "alignment") expect_is(as.phyDat(phy_matrix), "phyDat") expect_is(as.phyDat(phy_df), "phyDat") expect_is(as.phyDat(phy_dnabin), "phyDat") expect_is(phyDat(phy_vec_dna), "phyDat") expect_is(phyDat(phy_vec_user, type="USER", levels = c("0","1")), "phyDat") expect_is(as.phyDat(phy_dnabin), "phyDat") expect_is(as.phyDat(phy_align), "phyDat") expect_is(c2d <- codon2dna(codon_align), "phyDat") expect_equal(dna2codon(c2d), codon_align) }) #test_that("conversion with Biostrings work as expected", { # skip_on_cran() # library(Biostrings) # expect_is(MA_AA <- as.MultipleAlignment(chloroplast), "AAMultipleAlignment") # expect_equal(as.phyDat(MA_AA), chloroplast) # expect_is(MA_DNA <- as.MultipleAlignment(Laurasiatherian), "DNAMultipleAlignment") # expect_equal(as.phyDat(MA_DNA), Laurasiatherian) #}) test_that("subsetting and combining work as expected", { ## skip_on_cran() expect_is(subset_1 <- subset(Laurasiatherian, select = 1:1000, site.pattern = FALSE), "phyDat") expect_is(subset_2 <- subset(Laurasiatherian, select = 1001:3179, site.pattern = FALSE), "phyDat") expect_is(lauraCbind1 <- cbind(subset_1, subset_2), "phyDat") expect_equal(baseFreq(lauraCbind1), baseFreq(Laurasiatherian)) expect_is(subset_3 <- subset(Laurasiatherian, select = 1:100), "phyDat") expect_is(subset_4 <- subset(Laurasiatherian, select = 101:1605), "phyDat") expect_is(lauraCbind2 <- cbind(subset_3, subset_4), "phyDat") expect_equal(baseFreq(lauraCbind2), baseFreq(Laurasiatherian)) }) test_that("read and write work as expected", { skip_on_cran() write.phyDat(Laurasiatherian, "tmp1.txt") expect_is(laura <- read.phyDat("tmp1.txt"), "phyDat") expect_equal(laura, Laurasiatherian) unlink("tmp1.txt") write.phyDat(chloroplast, "tmp2.txt") expect_is(chloro <- read.phyDat("tmp2.txt", type="AA"), "phyDat") expect_equal(chloro, chloroplast) unlink("tmp2.txt") write.phyDat(chloroplast, "tmp.fas", format="fasta") expect_is(chloro_fas <- read.phyDat("tmp.fas", type="AA", format = "fasta"), "phyDat") expect_equal(chloro_fas, chloroplast) unlink("tmp.fas") }) phangorn/tests/testthat/test_parsimony.R0000644000175100001440000000335213017432001020303 0ustar hornikuserscontext("parsimony") data(yeast) all_trees <- allTrees(8, tip.label = names(yeast)) tree1 = read.tree(text = "((t1,t2),t3,t4);") tree2 = read.tree(text = "((t1,t3),t2,t4);") trees = .compressTipLabel(c(tree1, tree2)) dat <- phyDat(c(t1="a", t2="a",t3="t",t4="t"), type="USER", levels=c("a","c","g","t")) #tr_acctran = acctran(tree1, dat) #tr_ratchet = pratchet(dat, trace=0) #bab(dat) test_that("parsimony works properly", { ## skip_on_cran() expect_that(fitch(tree1, dat), equals(1)) expect_that(fitch(tree2, dat), equals(2)) expect_that(fitch(trees, dat), equals(c(1,2))) expect_that(sankoff(tree1, dat), equals(1)) expect_that(sankoff(tree2, dat), equals(2)) expect_that(parsimony(tree1, dat), equals(1)) }) test_that("bab works properly", { skip_on_cran() # all_trees <- allTrees(8, tip.label = names(yeast)) all_pars <- fitch(all_trees, yeast) bab_tree <- bab(yeast) expect_equal(min(all_pars), fitch(bab_tree, yeast)) }) test_that("rearrangements works properly", { skip_on_cran() tree <- all_trees[[1]] start <- fitch(tree, yeast) bab_tree <- bab(yeast) best <- fitch(bab_tree, yeast) best_fitch <- optim.parsimony(tree, yeast, rearrangements = "NNI") best_sankoff <- optim.parsimony(tree, yeast, method="sankoff", rearrangements = "NNI") expect_equal(attr(best_fitch, "pscore"), attr(best_sankoff, "pscore")) }) test_that("tree length works properly", { skip_on_cran() tree <- nj(dist.hamming(yeast)) pscore <- fitch(tree, yeast) tree1 <- acctran(tree, yeast) expect_equal(sum(tree1$edge.length), pscore) tree2 <- rtree(100) dat <- simSeq(tree2) tree2 <- acctran(tree2, dat) expect_equal(sum(tree2$edge.length), fitch(tree2,dat)) }) phangorn/src/0000755000175100001440000000000013020643153012670 5ustar hornikusersphangorn/src/Makevars0000644000175100001440000000006213020643153014362 0ustar hornikusersPKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) phangorn/src/fitch.c0000644000175100001440000005142713020643153014142 0ustar hornikusers#define USE_RINTERNALS #include #include #include #include // use R_len_t stat int, e.g. nr double huge = 1.0e300; static int *data1, *data2; static double *weight; void fitch_free(){ free(data1); free(data2); free(weight); } // type of fitch depending on nc e.g. int, long generic C++ void fitch_init(int *data, int *m, int *n, double *weights, int *nr) { int i; data1 = (int *) calloc(*n, sizeof(int)); data2 = (int *) calloc(*n, sizeof(int)); weight = (double *) calloc(*nr, sizeof(double)); for(i=0; i<*m; i++) data1[i] = data[i]; for(i=0; i<*nr; i++) weight[i] = weights[i]; } int bitcount(int x){ int count; for (count=0; x != 0; x>>=1) if ( x & 01) count++; return count; } void bitCount(int *x, int *count){ count[0]=bitcount(x[0]); } void addOne(int *edge, int *tip, int *ind, int *l, int *m, int *result){ int add = 1L, j=0L, p, k, i, l2=*l+2L, ei; p = edge[*ind-1L]; k = edge[*ind-1L + *l]; for(i=0; i<*l; i++){ ei = edge[i]; if( (add==1L) && (ei==p) ){ result[j] = *m; result[j+l2] = k; j++; result[j] = *m; result[j+l2] = *tip; j++; add=0L; } if(i== (*ind-1L)) result[j+l2] = *m; else result[j+l2] = edge[i+ *l]; result[j] = edge[i]; j++; } } SEXP AddOne(SEXP edge, SEXP tip, SEXP ind, SEXP l, SEXP m){ SEXP result; PROTECT(result = allocMatrix(INTSXP, INTEGER(l)[0]+2L, 2L)); addOne(INTEGER(edge), INTEGER(tip), INTEGER(ind), INTEGER(l), INTEGER(m), INTEGER(result)); UNPROTECT(1); return(result); } SEXP AddOnes(SEXP edge, SEXP tip, SEXP ind, SEXP l, SEXP m){ R_len_t n = length(ind); SEXP result, res; PROTECT(res = allocVector(VECSXP, n)); for(int i=0; ips)break; } // if(pvtmp[i] 0L){ dat1[k] = tmp; } } } void fitchT3(int *dat1, int *dat2, int *nr, double *pars, double *weight, double *w){ int k; int tmp; for(k = 0; k < (*nr); k++){ tmp = dat1[k] & dat2[k]; if(tmp==0L) { (*w)+=weight[k]; pars[k] += 1; } if(tmp >0){ if(tmp < dat2[k]){ (*w)+= .5*weight[k]; pars[k] += .5; } } } } // return lower and upper bound for the number of changes // upper bound very conservative /* void countMPR(double *res, int *dat1, int *dat2, int *nr, double *weight, int *external){ int k; int tmp; for(k = 0; k < (*nr); k++){ tmp = dat1[k] & dat2[k]; if(tmp==0){ res[0] += weight[k]; res[1] += weight[k]; } else{ if( external[0]==0L){ if( bitcount(dat1[k] | dat2[k])>1L ) res[1] += weight[k]; // dat1[k] != dat2[k] } else{ if( tmp < dat2[k] ) res[1] += weight[k]; } } } } */ //void ACCTRAN2(int *dat, int *nr, double *pars, int *node, int *edge, int *nl, double *weight, double *pvec, int *nTips) void ACCTRAN2(int *dat, int *nr, int *node, int *edge, int *nl, int *nTips) { int i; for (i=0; i< *nl; i++) { // , pars, weight, &pvec[i] if(edge[i]>nTips[0]) fitchT(&dat[(edge[i]-1L) * (*nr)], &dat[(node[i]-1) * (*nr)], nr); } } // , int *nTips void ACCTRAN3(int *dat, int *nr, double *pars, int *node, int *edge, int *nl, double *weight, double *pvec) { int i; for (i=0; i< *nr; i++)pars[i]=0.0; for(i=0; i< *nl; i++)pvec[i] = 0.0; for (i=0; i< *nl; i++) { fitchT3(&dat[(edge[i]-1L) * (*nr)], &dat[(node[i]-1) * (*nr)], nr, pars, weight, &pvec[i]); } } void fitchNNN(int d1, int d2){ int tmp; tmp = d1 & d2; if(tmp) d1 = tmp; else d1 = d1 | d2; } // haeufig 0 void fitchTripletNew(int *res, int *dat1, int *dat2, int *dat3, int *nr) { int k, v1, v2, v3; for(k = 0; k < (*nr); k++){ v1 = dat1[k]; fitchNNN(v1, dat2[k]); fitchNNN(v1, dat3[k]); v2 = dat1[k]; fitchNNN(v2, dat3[k]); fitchNNN(v2, dat2[k]); v3 = dat2[k]; fitchNNN(v3, dat3[k]); fitchNNN(v3, dat1[k]); res[k] = v1 & v2; // &v3[k]; res[k] = res[k] & v3; } } void fitchN(int *dat1, int *dat2, int *nr){ int k; int tmp; for(k = 0; k < (*nr); k++){ tmp = dat1[k] & dat2[k]; if(tmp) dat1[k] = tmp; else dat1[k] = dat1[k] | dat2[k]; } } // MPR reconstruction nicht immer gleiches ergebnis void fitchTriplet(int *res, int *dat1, int *dat2, int *dat3, int *nr) { int k; // ni, // ni = 0; int *v1, *v2, *v3; v1 = (int *) R_alloc(*nr, sizeof(int)); v2 = (int *) R_alloc(*nr, sizeof(int)); v3 = (int *) R_alloc(*nr, sizeof(int)); for(k = 0; k < (*nr); k++) v1[k] = dat1[k]; fitchN(v1, dat2, nr); fitchN(v1, dat3, nr); for(k = 0; k < (*nr); k++) v2[k] = dat1[k]; fitchN(v2, dat3, nr); fitchN(v2, dat2, nr); for(k = 0; k < (*nr); k++) v3[k] = dat2[k]; fitchN(v3, dat3, nr); fitchN(v3, dat1, nr); for(k = 0; k < (*nr); k++)res[k] = v1[k] & v2[k]; // &v3[k]; for(k = 0; k < (*nr); k++)res[k] = res[k] & v3[k]; } void prepRooted(int *res, int *nr, int *kids){ //int *data1, fitchTriplet(res, &data1[*nr * (kids[0]-1L)], &data1[*nr * (kids[1]-1L)], &data1[*nr * (kids[2]-1L)], nr); } void C_MPR(int *res, int *nr, int *parent, int *kids, int *nl) { int p, k1, k2; int i = *nl -1; while (i > 0L) { p = parent[i] - 1L; k1 = kids[i] - 1L; k2 = kids[i-1L] - 1L; fitchTriplet(&res[*nr * p], &data1[*nr* (k1)], &data1[*nr* (k2) ], &data2[*nr * p], nr); i -= 2L; } } SEXP C_MPR2(SEXP nrx, SEXP PARENT, SEXP KIDS, SEXP nlx, SEXP M) { int nr=INTEGER(nrx)[0], nl=INTEGER(nlx)[0], m=INTEGER(M)[0], *res; int *parent = INTEGER(PARENT), *kids=INTEGER(KIDS); int j, p, k1, k2; int i = nl -1; SEXP RES; PROTECT(RES = allocVector(INTSXP, nr * m)); res = INTEGER(RES); for(j = 0; j < (nr * m); j++) res[j]=0; while (i > 0L) { p = parent[i] - 1L; k1 = kids[i] - 1L; k2 = kids[i-1L] - 1L; fitchTripletNew(&res[nr * p], &data1[nr * k1], &data1[nr * k2], &data2[nr * p], &nr); i -= 2L; } UNPROTECT(1); return(RES); } void fitchNACC2(int *root, int *dat, int *nr, double *pars, int *result, double *weight, double *pars1){ int k; int tmp; for(k = 0; k < (*nr); k++){ // result[k] = 0L; tmp = root[k] & dat[k]; if(tmp==0L) { pars[0] += weight[k]; pars1[k] += weight[k]; } if(tmp >0){ if(tmp < root[k]){ pars[0] += .5*weight[k]; pars1[k] += .5*weight[k]; result[k] += 1L; } } } } void fitchTripletACC4(int *root, int *dat1, int *dat2, int *dat3, int *nr, double *p1, double *p2, double *p3, double *weight, double *pars1, int *v1) { int k; int tmp, a, b, c, t1, t2, t3; double d, f; for(k = 0; k < (*nr); k++){ tmp = root[k]; a = dat1[k] & dat2[k]; b = dat1[k] & dat3[k]; c = dat2[k] & dat3[k]; if((a+b+c) == 0L){ d = (2.0/3.0) * weight[k]; p1[0] += d; p2[0] += d; p3[0] += d; pars1[k] += 2*weight[k]; v1[k] = 2L; } else{ f = 0.0; d = weight[k]; t1 = 0.0; t2 = 0.0; t3 = 0.0; if( (dat1[k] & tmp)0.0){ pars1[k] += weight[k]; p1[0] += t1/f; p2[0] += t2/f; p3[0] += t3/f; v1[k] += 1L; } } } } // eleganter in Rcpp SEXP FITCH345(SEXP nrx, SEXP node, SEXP edge, SEXP l, SEXP mx, SEXP ps){ int *nr=INTEGER(nrx), m=INTEGER(mx)[0], i; double *pvtmp; SEXP pars, pscore; PROTECT(pars = allocVector(INTSXP, *nr)); PROTECT(pscore = allocVector(REALSXP, 1L)); pvtmp = (double *) R_alloc(m, sizeof(double)); for(i=0; i using namespace Rcpp; // import: edge matrix, number of tips // export: Descendants(x, 1:max(x$edge), "all") // [[Rcpp::export]] List allDescCPP(IntegerMatrix orig, int nTips) { IntegerVector parent = orig( _, 0); IntegerVector children = orig( _, 1); int m = max(parent); // create list for results std::vector< std::vector > out(m) ; for(int i = 0; i x; std::vector y; for(int i = 0; i nTips){ y = out[children[i] -1L]; out[parent[i]-1L].insert( out[parent[i]-1L].end(), y.begin(), y.end() ); } } // return the list return wrap(out); } // shorter and easier to understand replacement of C function // import: edge matrix // export: list of children // [[Rcpp::export]] List allChildrenCPP(IntegerMatrix orig) { IntegerVector parent = orig( _, 0); IntegerVector children = orig( _, 1); int m = max(parent); // create list for results std::vector< std::vector > out(m) ; for(int i = 0; i #include // The initial code defining and initialising the translation table: // //"a" "r" "n" "d" "c" "q" "e" "g" "h" "i" "l" "k" "m" "f" "p" "s" "t" "w" "y" "v" "b" "z" "x" // "-" "?" // // for (i = 0; i < 122; i++) tab_trans[i] = 0x00; // // tab_trans[65] = 0x88; /* A */ // tab_trans[71] = 0x48; /* G */ // tab_trans[67] = 0x28; /* C */ // tab_trans[84] = 0x18; /* T */ // tab_trans[82] = 0xc0; /* R */ // tab_trans[77] = 0xa0; /* M */ // tab_trans[87] = 0x90; /* W */ // tab_trans[83] = 0x60; /* S */ // tab_trans[75] = 0x50; /* K */ // tab_trans[89] = 0x30; /* Y */ // tab_trans[86] = 0xe0; /* V */ // tab_trans[72] = 0xb0; /* H */ // tab_trans[68] = 0xd0; /* D */ // tab_trans[66] = 0x70; /* B */ // tab_trans[78] = 0xf0; /* N */ // // tab_trans[97] = 0x88; /* a */ // tab_trans[103] = 0x48; /* g */ // tab_trans[99] = 0x28; /* c */ // tab_trans[116] = 0x18; /* t */ // tab_trans[114] = 0xc0; /* r */ // tab_trans[109] = 0xa0; /* m */ // tab_trans[119] = 0x90; /* w */ // tab_trans[115] = 0x60; /* s */ // tab_trans[107] = 0x50; /* k */ // tab_trans[121] = 0x30; /* y */ // tab_trans[118] = 0xe0; /* v */ // tab_trans[104] = 0xb0; /* h */ // tab_trans[100] = 0xd0; /* d */ // tab_trans[98] = 0x70; /* b */ // tab_trans[110] = 0xf0; /* n */ // // tab_trans[45] = 0x04; /* - */ // tab_trans[63] = 0x02; /* ? */ static const int tab_trans2[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 0-9 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 10-19 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 20-29 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 30-39 */ 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, /* 40-49 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 50-59 */ 0, 0, 0, 25, 0, 1, 21, 5, 4, 7, /* 60-69 */ 14, 8, 9, 10, 0, 12, 11, 13, 3, 0, /* 70-79 */ 15, 6, 2, 16, 17, 0, 20, 18, 23, 19, /* 80-89 */ 22, 0, 0, 0, 0, 0, 0, 1, 21, 5, /* 90-99 */ 4, 7, 14, 8, 9, 10, 0, 12, 11, 13, /* 100-109 */ 3, 0, 15, 6, 2, 16, 17, 0, 20, 18, /* 110-119 */ 23, 19, 22, 0, 0, 0, 0, 0, 0, 0, /* 120-129 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 130-139 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 140-149 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 150-159 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 160-169 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 170-179 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 180-189 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 190-199 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 200-209 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 210-219 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 220-229 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 230-239 */ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, /* 240-249 */ 0, 0, 0, 0, 0, 0}; /* 250-255 */ static const unsigned char hook = 0x3e; static const unsigned char lineFeed = 0x0a; /* static const unsigned char space = 0x20; */ // needs buffer seq // + buffer names SEXP rawStream2phyDat(SEXP x) { int N, i, j, k, n, startOfSeq; unsigned char *xr, *bufferNames; int *rseq, *buffer, tmp; SEXP obj, nms, seq; PROTECT(x = coerceVector(x, RAWSXP)); N = LENGTH(x); xr = RAW(x); /* do a 1st pass to find the number of sequences this code should be robust to '>' present inside a label or in the header text before the sequences */ n = j = 0; /* use j as a flag */ if (xr[0] == hook) { j = 1; startOfSeq = 0; } i = 1; for (i = 1; i < N; i++) { if (j && xr[i] == lineFeed) { n++; j = 0; } else if (xr[i] == hook) { if (!n) startOfSeq = i; j = 1; } } PROTECT(obj = allocVector(VECSXP, n)); PROTECT(nms = allocVector(STRSXP, n)); /* Refine the way the size of the buffer is set? */ buffer = (int *)R_alloc(N, sizeof(int *)); bufferNames = (unsigned char *)R_alloc(N, sizeof(unsigned char *)); i = startOfSeq; j = 0; /* gives the index of the sequence */ while (i < N) { /* 1st read the label... */ i++; k = 0; while (xr[i] != lineFeed) bufferNames[k++] = xr[i++]; bufferNames[k] = '\0'; SET_STRING_ELT(nms, j, mkChar((char *)bufferNames)); /* ... then read the sequence */ n = 0; while (i < N && xr[i] != hook) { tmp = tab_trans2[xr[i++]]; /* If we are sure that the FASTA file is correct (ie, the sequence on a single line and only the IUAPC code (plus '-' and '?') is used, then the following check would not be needed; additionally the size of tab_trans could be restriced to 0-121. This check has the advantage that all invalid characters are simply ignored without causing error -- except if '>' occurs in the middle of a sequence. */ if(tmp) buffer[n++] = tmp; } PROTECT(seq = allocVector(INTSXP, n)); rseq = INTEGER(seq); for (k = 0; k < n; k++) rseq[k] = buffer[k]; SET_VECTOR_ELT(obj, j, seq); UNPROTECT(1); j++; } setAttrib(obj, R_NamesSymbol, nms); UNPROTECT(3); return obj; } phangorn/src/sprdist.c0000644000175100001440000011674313020643153014540 0ustar hornikusers/* * sprdist.c * * (c) 2016 Leonardo de Oliveira Martins (leomrtns@gmail.com) * * * This code may be distributed under the GNU GPL * */ # define USE_RINTERNALS #include #include #include #include //#include /* standard integer types (int32_t typedef etc.) [C99]*/ #define true 1U /*!< Boolean TRUE */ #define false 0U /*!< Boolean FALSE */ typedef unsigned char bool; typedef struct splitset_struct* splitset; typedef struct hungarian_struct* hungarian; typedef struct bipartition_struct* bipartition; typedef struct bipsize_struct* bipsize; struct splitset_struct { int size, spsize, spr, spr_extra, rf, hdist; /*! \brief spr, extra prunes for spr, rf distances and hdist=assignment cost */ int n_g, n_s, n_agree, n_disagree; bipartition *g_split, *s_split, *agree, *disagree; bipartition prune; hungarian h; /* hungarian method for solving the assignment between edges */ bool match; /*! \brief do we want to calculate the minimum cost assignment */ }; struct hungarian_struct { int **cost, *col_mate; /*! \brief cost matrix, and col_mate[row] with column match for row */ int size, /*! \brief assignment size. Cost is a square matrix, so size should be an overestimate where "missing" nodes are added w/ cost zero */ initial_cost, /*! \brief sum of lowest input cost values for each column. The hungarian method rescales them so that minimum per column is zero */ final_cost; /*! \brief our final cost is on rescaled cost matrix, therefore to restore the "classical" optimal cost one should sum it with initial_cost */ int *unchosen_row, *row_dec, *slack_row, *row_mate, *parent_row, *col_inc, *slack; /* aux vectors */ }; /*! \brief Bit-string representation of splits. */ struct bipartition_struct { unsigned long long *bs; /*! \brief Representation of a bipartition by a vector of integers (bitstrings). */ int n_ones; /*! \brief Counter (number of "one"s) */ bipsize n; /*! \brief number of bits (leaves), vector size and mask */ int ref_counter; /*! \brief How many times this struct is being referenced */ }; struct bipsize_struct { unsigned long long mask;/*! \brief mask to make sure we consider only active positions (of last bitstring) */ int ints, bits, original_size; /*! \brief Vector size and total number of elements (n_ints = n_bits/(8*sizeof(long long)) +1). */ int ref_counter; /*! \brief How many times this struct is being referenced */ }; /*! \brief Allocate space for splitset structure (two vectors of bipartitions), for simple comparisons */ splitset new_splitset (int nleaves, int nsplits); /*! \brief free memory allocated for splitset structure */ void del_splitset (splitset split); /*! \brief low level function that does the actual SPR and hdist calculation based on a filled splitset struct */ int dSPR_topology_lowlevel (splitset split); /*! \brief function used by qsort for a vector of bipartitions (from smaller to larger) */ int compare_splitset_bipartition_increasing (const void *a1, const void *a2); /* BELOW: low level functions that work with bipartitions */ void split_create_agreement_list (splitset split); void split_remove_agree_edges (splitset split, bipartition *b, int *nb); void split_remove_duplicates (bipartition *b, int *nb); void split_compress_agreement (splitset split); void split_create_disagreement_list (splitset split); void split_disagreement_assign_match (splitset split); void split_find_small_disagreement (splitset split); void split_remove_small_disagreement (splitset split); void split_minimize_subtrees (splitset split); void split_remove_redundant_bit (splitset split, int id); void split_replace_bit (splitset split, int to, int from); void split_new_size (splitset split, int size, bool update_bipartitions); void split_swap_position (bipartition *b, int i1, int i2); /* BELOW: Hungarian method for bipartite matching (assignment) */ hungarian new_hungarian (int size); void hungarian_reset (hungarian p); void hungarian_update_cost (hungarian p, int row, int col, int cost); void del_hungarian (hungarian p); void hungarian_solve (hungarian p, int this_size); /* BELOW: Memory-efficient, fast bipartition comparisions based on 64bit representation of splits */ bipartition new_bipartition (int size); bipsize new_bipsize (int size); bipartition new_bipartition_copy_from (const bipartition from); bipartition new_bipartition_from_bipsize (bipsize n); void del_bipartition (bipartition bip); void del_bipsize (bipsize n); void bipsize_resize (bipsize n, int nbits); void bipartition_initialize (bipartition bip, int position); void bipartition_zero (bipartition bip); void bipartition_set (bipartition bip, int position); void bipartition_set_lowlevel (bipartition bip, int i, int j); void bipartition_unset (bipartition bip, int position); void bipartition_unset_lowlevel (bipartition bip, int i, int j); void bipartition_copy (bipartition to, const bipartition from); void bipartition_OR (bipartition result, const bipartition b1, const bipartition b2, bool update_count); void bipartition_AND (bipartition result, const bipartition b1, const bipartition b2, bool update_count); void bipartition_ANDNOT (bipartition result, const bipartition b1, const bipartition b2, bool update_count); void bipartition_XOR (bipartition result, const bipartition b1, const bipartition b2, bool update_count); void bipartition_XORNOT (bipartition result, const bipartition b1, const bipartition b2, bool update_count); void bipartition_NOT (bipartition result, const bipartition bip); void bipartition_count_n_ones (const bipartition bip); void bipartition_to_int_vector (const bipartition b, int *id, int vecsize); bool bipartition_is_equal (const bipartition b1, const bipartition b2); bool bipartition_is_equal_bothsides (const bipartition b1, const bipartition b2); bool bipartition_is_larger (const bipartition b1, const bipartition b2); void bipartition_flip_to_smaller_set (bipartition bip); bool bipartition_is_bit_set (const bipartition bip, int position); bool bipartition_contains_bits (const bipartition b1, const bipartition b2); // void bipartition_print_to_stdout (const bipartition b1); void bipartition_replace_bit_in_vector (bipartition *bvec, int n_b, int to, int from, bool reduce); void bipartition_resize_vector (bipartition *bvec, int n_b); /*! \brief Main SPR calculation function, to be used within R */ SEXP C_sprdist (SEXP bp1, SEXP bp2, SEXP lt) { int i, j, n_leaves = INTEGER(lt)[0]; SEXP result; double *res; splitset split; PROTECT(result = allocVector(REALSXP, 4)); res = REAL(result); // if (length(bp1) != length(bp2)) error ("number of bipartitions given to C_sprdist are not the same"); split = new_splitset (n_leaves, length(bp1)); for (i=0; i < split->size; i++) { // for (j=0; j < length(VECTOR_ELT (bp1, i)); j++) printf (";;%d ", INTEGER (VECTOR_ELT (bp1, i))[j]); for (j=0; j < length(VECTOR_ELT (bp1, i)); j++) bipartition_set (split->g_split[i], INTEGER (VECTOR_ELT (bp1, i))[j] - 1); for (j=0; j < length(VECTOR_ELT (bp2, i)); j++) bipartition_set (split->s_split[i], INTEGER (VECTOR_ELT (bp2, i))[j] - 1); } dSPR_topology_lowlevel (split); res[0] = split->spr; res[1] = split->spr_extra; res[2] = split->rf; res[3] = split->hdist; del_splitset (split); UNPROTECT(1); // result return(result); } /* functions below should not be called outside this scope */ splitset new_splitset (int nleaves, int nsplits) { splitset split; int i; split = (splitset) malloc (sizeof (struct splitset_struct)); split->n_g = split->n_s = split->size = nsplits; split->n_agree = split->n_disagree = 0; split->prune = NULL; split->match = true; /* do we want to calculate the assignment matching cost (using hungarian() )? */ split->spr = split->spr_extra = split->rf = split->hdist = 0; split->g_split = (bipartition*) malloc (split->size * sizeof (bipartition)); split->s_split = (bipartition*) malloc (split->size * sizeof (bipartition)); split->g_split[0] = new_bipartition (nleaves); split->s_split[0] = new_bipartition (nleaves); for (i = 1; i < split->size; i++) { split->g_split[i] = new_bipartition_from_bipsize (split->g_split[0]->n); /* use same bipsize */ split->s_split[i] = new_bipartition_from_bipsize (split->s_split[0]->n); } split->agree = (bipartition*) malloc (split->size * sizeof (bipartition)); split->disagree = (bipartition*) malloc (split->size * split->size * sizeof (bipartition)); split->agree[0] = new_bipartition (nleaves); // this bipsize will be recycled below split->disagree[0] = new_bipartition (nleaves); for (i = 1; i < split->size; i++) split->agree[i] = new_bipartition_from_bipsize (split->agree[0]->n); for (i = 1; i < split->size * split->size; i++) split->disagree[i] = new_bipartition_from_bipsize (split->disagree[0]->n); split->prune = new_bipartition_from_bipsize (split->disagree[0]->n); split->h = new_hungarian (split->size); return split; } void del_splitset (splitset split) { int i; if (!split) return; del_bipartition (split->prune); if (split->disagree) { for (i = split->size * split->size - 1; i >= 0; i--) del_bipartition (split->disagree[i]); free (split->disagree); } if (split->agree) { for (i = split->size - 1; i >= 0; i--) del_bipartition (split->agree[i]); free (split->agree); } if (split->g_split) { for (i = split->size - 1; i >= 0; i--) del_bipartition (split->g_split[i]); free (split->g_split); } del_hungarian (split->h); free (split); } int compare_splitset_bipartition_increasing (const void *a1, const void *a2) { /* similar to bipartition_is_larger() */ bipartition *b1 = (bipartition *) a1; bipartition *b2 = (bipartition *) a2; int i; if ((*b1)->n_ones > (*b2)->n_ones) return 1; if ((*b1)->n_ones < (*b2)->n_ones) return -1; for (i = (*b1)->n->ints - 1; (i >= 0) && ((*b1)->bs[i] == (*b2)->bs[i]); i--); /* find position of distinct bipartition elem*/ if (i < 0) return 0; /* identical bipartitions */ if ((*b1)->bs[i] > (*b2)->bs[i]) return 1; else return -1; } int dSPR_topology_lowlevel (splitset split) { int i = 0, mismatch = -1; for (i=0; i < split->size; i++) { bipartition_flip_to_smaller_set (split->g_split[i]); bipartition_flip_to_smaller_set (split->s_split[i]); } qsort (split->g_split, split->size, sizeof (bipartition), compare_splitset_bipartition_increasing); qsort (split->s_split, split->size, sizeof (bipartition), compare_splitset_bipartition_increasing); //for (i = 0; i < split->n_g; i++) bipartition_print_to_stdout (split->g_split[i]); printf ("G ::DEBUG 0 ::\n"); //for (i = 0; i < split->n_s; i++) bipartition_print_to_stdout (split->s_split[i]); printf ("S\n"); i++; /* to trick -Werror, since we don't use it unless for debug */ while (mismatch) { split_create_agreement_list (split); // vector of identical bipartitions split_compress_agreement (split); // iterative replacement of cherry by new leaf // for (i = 0; i < split->n_g; i++) bipartition_print_to_stdout (split->g_split[i]); printf ("G ::DEBUG::\n"); // for (i = 0; i < split->n_s; i++) bipartition_print_to_stdout (split->s_split[i]); printf ("S\n"); // for (i = 0; i < split->n_agree; i++) bipartition_print_to_stdout (split->agree[i]); printf ("A\n"); if (mismatch == -1) split->rf = split->n_g + split->n_s; mismatch = (split->n_g > 0) && (split->n_s > 0); // all edges were in agreement if (!mismatch) return split->spr; split_create_disagreement_list (split); // vector of smallest disagreements split_disagreement_assign_match (split); /* assignment matching between edges using hungarian method (split->hdist after first time) */ split_remove_duplicates (split->disagree, &(split->n_disagree)); // some elements are equal; this function also qsorts split_find_small_disagreement (split); // could also be one leaf only //for (i = 0; i < split->n_disagree; i++) { bipartition_print_to_stdout (split->disagree[i]); printf ("\n"); } //printf ("{%d} prune: ", split->n_disagree); bipartition_print_to_stdout (split->prune); printf ("\n"); split->spr++; split_remove_small_disagreement (split); split_minimize_subtrees (split); mismatch = (split->n_g > 0) && (split->n_s > 0); // all edges were in agreement } return split->spr; } void split_create_agreement_list (splitset split) { int s, g; for (g = 0; g < split->n_g; g++) for (s = 0; s < split->n_s; s++) if (bipartition_is_equal (split->g_split[g], split->s_split[s])) { bipartition_copy (split->agree[split->n_agree++], split->g_split[g]); split->n_g--; split_swap_position (split->g_split, g, split->n_g); /* if we don't swap them, we lose ref to "old" value on g_split[] */ split->n_s--; split_swap_position (split->s_split, s, split->n_s); g--; s = split->n_s; /* pretend loop finished, examine again with new values */ } split_remove_agree_edges (split, split->g_split, &(split->n_g)); split_remove_agree_edges (split, split->s_split, &(split->n_s)); } void split_remove_agree_edges (splitset split, bipartition *b, int *nb) { int i, a; for (i = 0; i < (*nb); i++) for (a = 0; a < split->n_agree; a++) if (bipartition_is_equal (b[i], split->agree[a])) { (*nb)--; split_swap_position (b, i, (*nb)); i--; a = split->n_agree; /* loop again over new value */ } } void split_remove_duplicates (bipartition *b, int *nb) { int i, j; bipartition pivot; if ((*nb) < 2) return; /* only if we have a vector with > 1 element */ qsort (b, (*nb), sizeof (bipartition), compare_splitset_bipartition_increasing); for (i = (*nb) - 1; i >= 1; i--) if (bipartition_is_equal (b[i], b[i-1])) { pivot = b[i]; /* do not lose a pointer to this element */ for (j = i; j < (*nb)-1; j++) b[j] = b[j+1]; b[j] = pivot; /* j = (*nb) - 1, which will become obsolete through next line --> (*nb)-- */ (*nb)--; } } void split_compress_agreement (splitset split) { int i, j, pair[2]; for (i = 0; i < split->n_agree; i++) if (split->agree[i]->n_ones == 2) { /* cherry in common, can be represented by just one leaf */ bipartition_to_int_vector (split->agree[i], pair, 2); split_remove_redundant_bit (split, pair[1]); split_new_size (split,split->agree[0]->n->bits - 1, false); /* false = do not recalculate every bipartition's last elem */ bipartition_resize_vector (split->agree, split->n_agree); for (j = 0; j < split->n_agree; j++) { /* minimize subtree size and remove single leaves */ bipartition_flip_to_smaller_set (split->agree[j]); /* agree only */ if (split->agree[j]->n_ones < 2) split_swap_position (split->agree, j--, --split->n_agree); } i = -1; /* redo all iterations, with new info (agree[] will be smaller) */ } bipartition_resize_vector (split->g_split, split->n_g); bipartition_resize_vector (split->s_split, split->n_s); } void split_create_disagreement_list (splitset split) { int g, s; for (g = 0; g < split->n_g; g++) for (s = 0; s < split->n_s; s++) { bipartition_XOR (split->disagree[g * split->n_s + s], split->g_split[g], split->s_split[s], true); /* true means to calculate n_ones */ bipartition_flip_to_smaller_set (split->disagree[g * split->n_s + s]); } split->n_disagree = split->n_g * split->n_s; } void split_disagreement_assign_match (splitset split) { /* also calculates split->hdist */ int g, s, max_n, sum = 0; if (split->n_g > split->n_s) max_n = split->n_g; else max_n = split->n_s; if (max_n < 2) return; hungarian_reset (split->h); for (g = 0; g < split->n_g; g++) for (s = 0; s < split->n_s; s++) hungarian_update_cost (split->h, g, s, split->disagree[g * split->n_s + s]->n_ones); hungarian_solve (split->h, max_n); /* now split->h->col_mate will have the pairs */ /* if we do the matching below it becomes much faster, but we may miss the best prune subtrees in a few cases (do not compromise the algo) */ split->n_disagree = 0; for (g = 0; g < max_n; g++) if ((g < split->n_g) && ( split->h->col_mate[g] < split->n_s)) { /* some matchings might be to dummy edges */ bipartition_XOR (split->disagree[split->n_disagree], split->g_split[g], split->s_split[split->h->col_mate[g]], true); /* true means to calculate n_ones */ bipartition_flip_to_smaller_set (split->disagree[split->n_disagree++]); sum += split->disagree[split->n_disagree-1]->n_ones; } if (split->match) { split->hdist = split->h->final_cost+split->h->initial_cost; split->match = false; } } void split_find_small_disagreement (splitset split) { bipartition dis; int a, d; bipartition_copy (split->prune, split->disagree[0]); /* smallest, in case we don't find a better one in loop below */ if (split->prune->n_ones < 2) return; dis = new_bipartition_from_bipsize (split->disagree[0]->n); for (d = 0; d < split->n_disagree; d++) for (a = 0; a < split->n_agree; a++) { if ((split->disagree[d]->n_ones == split->agree[a]->n_ones) || (split->disagree[d]->n_ones == (split->agree[a]->n->bits - split->agree[a]->n_ones))) { bipartition_XOR (dis, split->disagree[d], split->agree[a], true); if (!dis->n_ones) { bipartition_copy (split->prune, split->disagree[d]); d = split->n_disagree; a = split->n_agree; } else if (dis->n_ones == dis->n->bits) { bipartition_NOT (split->prune, split->disagree[d]); d = split->n_disagree; a = split->n_agree; } } } /* check if prune nodes are all on same side of a tree or if they are actually two SPRs (one from each tree) */ for (d = 0; d < split->n_g; d++) { if (!bipartition_contains_bits (split->g_split[d], split->prune)) { bipartition_NOT (dis, split->g_split[d]); if (!bipartition_contains_bits (dis, split->prune)) { split->spr_extra++; d = split->n_g; } } } del_bipartition (dis); } void split_remove_small_disagreement (splitset split) { int *index, i, j = split->prune->n_ones - 1, k = 0, size = split->agree[0]->n->bits; index = (int*) malloc (split->prune->n_ones * sizeof (int)); bipartition_to_int_vector (split->prune, index, split->prune->n_ones); for (i = size - 1; i >= (size - split->prune->n_ones); i--) { if (index[k] >= (size - split->prune->n_ones)) i = -1; else { if (i == index[j]) j--; else split_replace_bit (split, index[k++], i); } } split_new_size (split,size - split->prune->n_ones, true); if (index) free (index); } void split_minimize_subtrees (splitset split) { int i; for (i = 0; i < split->n_s; i++) { bipartition_flip_to_smaller_set (split->s_split[i]); if (split->s_split[i]->n_ones < 2) { split->n_s--; split_swap_position (split->s_split, i, split->n_s); i--; } } for (i = 0; i < split->n_g; i++) { bipartition_flip_to_smaller_set (split->g_split[i]); if (split->g_split[i]->n_ones < 2) { split->n_g--; split_swap_position (split->g_split, i, split->n_g); i--; } } for (i = 0; i < split->n_agree; i++) { bipartition_flip_to_smaller_set (split->agree[i]); if (split->agree[i]->n_ones < 2) { split->n_agree--; split_swap_position (split->agree, i, split->n_agree); i--; } } } void split_remove_redundant_bit (splitset split, int id) { int last = split->agree[0]->n->bits-1; if (id < last) split_replace_bit (split, id, last); } void split_replace_bit (splitset split, int to, int from) { if (from <= to) return; /*not needed for disagree[] */ bipartition_replace_bit_in_vector (split->agree, split->n_agree, to, from, true); bipartition_replace_bit_in_vector (split->g_split, split->n_g, to, from, true); bipartition_replace_bit_in_vector (split->s_split, split->n_s, to, from, true); } void split_new_size (splitset split, int size, bool update_bipartitions) { bipsize_resize (split->g_split[0]->n, size); bipsize_resize (split->s_split[0]->n, size); bipsize_resize (split->agree[0]->n, size); bipsize_resize (split->disagree[0]->n, size); if (update_bipartitions) { bipartition_resize_vector (split->g_split, split->n_g); bipartition_resize_vector (split->s_split, split->n_s); bipartition_resize_vector (split->agree, split->n_agree); } } void split_swap_position (bipartition *b, int i1, int i2) { bipartition pivot = b[i1]; b[i1] = b[i2]; b[i2] = pivot; } /* The hungarian method below is copied from http://www.informatik.uni-freiburg.de/~stachnis/misc.html * The (edited) original message follows: * ** libhungarian by Cyrill Stachniss, 2004 Solving the Minimum Assignment Problem using the ** Hungarian Method. ** This file may be freely copied and distributed! ** ** ** Parts of the used code was originally provided by the "Stanford GraphGase", but I made changes to this code. ** As asked by the copyright node of the "Stanford GraphGase", I hereby proclaim that this file are *NOT* part of the ** "Stanford GraphGase" distrubition! */ void hungarian_reset (hungarian p) { int i, j; for (i = 0; i < p->size; i++) { p->col_mate[i] = p->unchosen_row[i] = p->row_dec[i] = p->slack_row[i] = p->row_mate[i] = p->parent_row[i] = p->col_inc[i] = p->slack[i] = 0; for (j = 0; j < p->size; j++) p->cost[i][j] = 0; } p->final_cost = 0; } hungarian new_hungarian (int size) { int i; hungarian p; p = (hungarian) malloc (sizeof (struct hungarian_struct)); p->size = size; /* n_rows = n_columns; if it's not, fill with zeroes (no cost) */ p->cost = (int**) malloc (size * sizeof (int*)); for (i = 0; i < p->size; i++) p->cost[i] = (int*) malloc (size * sizeof (int)); /* edges would be assignment_matrix[ i * ncols + col_mate[i] ] = true; and other elems "false" (but we don't use the matrix notation) */ p->col_mate = (int*) malloc (size * sizeof (int)); /* for a given row node, col_mate[row] is the assigned col node */ p->unchosen_row = (int*) malloc (size * sizeof (int)); p->row_dec = (int*) malloc (size * sizeof (int)); p->slack_row = (int*) malloc (size * sizeof (int)); p->row_mate = (int*) malloc (size * sizeof (int)); p->parent_row = (int*) malloc (size * sizeof (int)); p->col_inc = (int*) malloc (size * sizeof (int)); p->slack = (int*) malloc (size * sizeof (int)); hungarian_reset (p); return p; } void hungarian_update_cost (hungarian p, int row, int col, int cost) { if (row >= p->size) return; if (col >= p->size) return; p->cost[row][col] = cost; } void del_hungarian (hungarian p) { int i; if (!p) return; if (p->cost) { for (i = p->size - 1; i >= 0; i--) if (p->cost[i]) free (p->cost[i]); free (p->cost); } free (p->col_mate); /* this is the important one, with i assigned to col_mate[i] */ free (p->slack); free (p->col_inc); free (p->parent_row); free (p->row_mate); free (p->slack_row); free (p->row_dec); free (p->unchosen_row); free (p); } void hungarian_solve (hungarian p, int this_size) { int i, j, nrows = this_size, ncols = this_size, k, l, s, t, q, unmatched; p->final_cost = p->initial_cost = 0; if (this_size > p->size) { p->final_cost = -1; return; } /* we don't call biomcmc_error(), but it *is* an error! */ for (l = 0; l < ncols; l++) { // Begin subtract column minima in order to start with lots of zeroes 12 s = p->cost[0][l]; for (k = 1; k < nrows; k++) if (p->cost[k][l] < s) s = p->cost[k][l]; p->initial_cost += s; /* this should be added to final_cost to have classical assignment cost; here we distinguish them */ if (s!=0) for (k = 0; k < nrows; k++) p->cost[k][l] -= s; } // End subtract column minima in order to start with lots of zeroes 12 // Begin initial state 16 t=0; for (l = 0; l < ncols; l++) { // n => num_cols p->row_mate[l]= -1; p->parent_row[l]= -1; p->col_inc[l]=0; p->slack[l]= 0x7FFFFFFF; } for (k = 0; k < nrows; k++) { // m => num_rows s = p->cost[k][0]; for (l = 1; l < ncols; l++) if (p->cost[k][l] < s) s = p->cost[k][l]; p->row_dec[k]=s; for (l = 0; l < ncols; l++) if ((s==p->cost[k][l]) && (p->row_mate[l] < 0)) { p->col_mate[k] = l; p->row_mate[l] = k; // fprintf(stderr, "matching col %d==row %d\n",l,k); goto row_done; } p->col_mate[k] = -1; // fprintf(stderr, "node %d: unmatched row %d\n",t,k); p->unchosen_row[t++] = k; row_done: ; } // End initial state 16 // Begin Hungarian algorithm 18 if (t==0) goto done; unmatched=t; while (1) { q=0; // fprintf(stderr, "Matched %d rows.\n",m-t); while (1) { while (qunchosen_row[q]; s=p->row_dec[k]; for (l=0;lslack[l]) { int del; del = p->cost[k][l] - s + p->col_inc[l]; if (del < p->slack[l]) { if (del==0) { if (p->row_mate[l]<0) goto breakthru; p->slack[l]=0; p->parent_row[l]=k; // fprintf(stderr, "node %d: row %d==col %d--row %d\n", t,row_mate[l],l,k); p->unchosen_row[t++]=p->row_mate[l]; } else { p->slack[l]=del; p->slack_row[l]=k; } } } } // End explore node q of the forest 19 q++; } // Begin introduce a new zero into the matrix 21 s = 0x7FFFFFFF; for (l = 0;l < ncols; l++) if (p->slack[l] && p->slack[l] < s) s = p->slack[l]; for (q = 0; q < t; q++) p->row_dec[ p->unchosen_row[q] ] += s; for (l = 0; l < ncols; l++) if (p->slack[l]) { p->slack[l]-=s; if (p->slack[l]==0) { // Begin look at a new zero 22 k = p->slack_row[l]; // fprintf(stderr, "Decreasing uncovered elements by %d produces zero at [%d,%d]\n", s,k,l); if (p->row_mate[l]<0) { for (j=l+1;jslack[j]==0) p->col_inc[j]+=s; goto breakthru; } else { p->parent_row[l]=k; // fprintf(stderr, "node %d: row %d==col %d--row %d\n",t,row_mate[l],l,k); p->unchosen_row[t++]=p->row_mate[l]; } } // End look at a new zero 22 } else p->col_inc[l]+=s; // End introduce a new zero into the matrix 21 } breakthru: // fprintf(stderr, "Breakthrough at node %d of %d!\n",q,t); while (1) { // Begin update the matching 20 j=p->col_mate[k]; p->col_mate[k]=l; p->row_mate[l]=k; // fprintf(stderr, "rematching col %d==row %d\n",l,k); if (j<0) break; k=p->parent_row[j]; l=j; } // End update the matching 20 if (--unmatched==0) goto done; // Begin get ready for another stage 17 t=0; for (l=0;lparent_row[l]= -1; p->slack[l]=0x7FFFFFFF; } for (k=0;kcol_mate[k]<0) p->unchosen_row[t++]=k; // fprintf(stderr, "node %d: unmatched row %d\n",t,k); // End get ready for another stage 17 } done: // Begin doublecheck the solution 23 for (k = 0; k < nrows; k++) for (l=0;lcost[k][l] < p->row_dec[k] - p->col_inc[l]) { p->final_cost = -1; return;} //printf ("\n**\n"); for (k = 0; k < nrows; k++) { l=p->col_mate[k]; if ((l < 0) || (p->cost[k][l] != p->row_dec[k] - p->col_inc[l])) { p->final_cost = -1; return; } } k=0; for (l=0;lcol_inc[l]) k++; if (k>nrows) { p->final_cost = -1; return; } // End doublecheck the solution 23 // End Hungarian algorithm 18 for (k = 0; k < nrows; ++k) for (l = 0; l < ncols; ++l) p->cost[k][l] = p->cost[k][l] - p->row_dec[k] + p->col_inc[l]; for (i = 0; i < nrows; i++) p->final_cost += p->row_dec[i]; for (i = 0; i < ncols; i++) p->final_cost -= p->col_inc[i]; // fprintf(stderr, "Cost is %d\n",cost); } /* BELOW are the bipartition functions (memory-efficient storage of bipartitions on 64 bits */ int BitStringSize = 8 * sizeof (unsigned long long); bipartition new_bipartition (int size) { bipartition bip; int i; bip = (bipartition) malloc (sizeof (struct bipartition_struct)); bip->n = new_bipsize (size); bip->n_ones = 0; bip->ref_counter = 1; bip->bs = (unsigned long long*) malloc (bip->n->ints * sizeof (unsigned long long)); for (i=0; i < bip->n->ints; i++) bip->bs[i] = 0LL; return bip; } bipsize new_bipsize (int size) { bipsize n; int i; n = (bipsize) malloc (sizeof (struct bipsize_struct)); n->bits = n->original_size = size; n->ref_counter = 1; n->ints = size/BitStringSize + 1; n->mask = 0LL; for (i=0; i < n->bits%BitStringSize; i++) n->mask |= (1LL << i); /* disregard other bits */ return n; } bipartition new_bipartition_copy_from (const bipartition from) { bipartition bip; int i; bip = (bipartition) malloc (sizeof (struct bipartition_struct)); bip->n = new_bipsize (from->n->bits); bip->n_ones = from->n_ones; bip->ref_counter = 1; bip->bs = (unsigned long long*) malloc (bip->n->ints * sizeof (unsigned long long)); for (i=0; i < bip->n->ints; i++) bip->bs[i] = from->bs[i]; return bip; } bipartition new_bipartition_from_bipsize (bipsize n) { bipartition bip; int i; bip = (bipartition) malloc (sizeof (struct bipartition_struct)); bip->n = n; bip->n->ref_counter++; bip->n_ones = 0; bip->ref_counter = 1; bip->bs = (unsigned long long*) malloc (bip->n->ints * sizeof (unsigned long long)); for (i=0; i < bip->n->ints; i++) bip->bs[i] = 0LL; return bip; } void del_bipartition (bipartition bip) { if (bip) { if (--bip->ref_counter) return; if (bip->bs) free (bip->bs); del_bipsize (bip->n); free (bip); } } void del_bipsize (bipsize n) { if (n) { if (--n->ref_counter) return; free (n); } } void bipsize_resize (bipsize n, int nbits) { int i; n->bits = nbits; n->ints = nbits/BitStringSize + 1; // might be smaller than original bs size n->mask = 0LL; for (i=0; i < nbits%BitStringSize; i++) n->mask |= (1LL << i); /* disregard other bits */ } void bipartition_initialize (bipartition bip, int position) { int i, j; for (i=0; i < bip->n->ints; i++) bip->bs[i] = 0LL; j = position%BitStringSize; i = position/BitStringSize; bip->bs[i] = (1LL << j); bip->n_ones = 1; } void bipartition_zero (bipartition bip) { int i; for (i=0; i < bip->n->ints; i++) bip->bs[i] = 0LL; bip->n_ones = 0; } void bipartition_set (bipartition bip, int position) { bipartition_set_lowlevel (bip, position/BitStringSize, position%BitStringSize); } void bipartition_set_lowlevel (bipartition bip, int i, int j) { if (bip->bs[i] & (1LL << j)) return; // bit already set bip->bs[i] |= (1LL << j); bip->n_ones++; /* doesn't work if we reduce space later (check replace_int_in_vector() ) */ } void bipartition_unset (bipartition bip, int position) { bipartition_unset_lowlevel (bip, position/BitStringSize, position%BitStringSize); } void bipartition_unset_lowlevel (bipartition bip, int i, int j) { if (!(bip->bs[i] & (1LL << j))) return; // bit already unset bip->bs[i] &= ~(1LL << j); bip->n_ones--; } void bipartition_copy (bipartition to, const bipartition from) { int i; for (i=0; i < to->n->ints; i++) to->bs[i] = from->bs[i]; to->n_ones = from->n_ones; } void bipartition_OR (bipartition result, const bipartition b1, const bipartition b2, bool update_count) { int i; for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] | b2->bs[i]; result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */ if (update_count) bipartition_count_n_ones (result); else result->n_ones = b1->n_ones + b2->n_ones; // works on topologies where b1 and b2 are disjoint } void bipartition_AND (bipartition result, const bipartition b1, const bipartition b2, bool update_count) { int i; for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] & b2->bs[i]; result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */ if (update_count) bipartition_count_n_ones (result); else result->n_ones = 0;// update_count = false should be used only when you don't care about this value (temp var) } void bipartition_ANDNOT (bipartition result, const bipartition b1, const bipartition b2, bool update_count) { int i; for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] & (~b2->bs[i]); result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */ if (update_count) bipartition_count_n_ones (result); else result->n_ones = 0;// update_count = false should be used only when you don't care about this value (temp var) } void bipartition_XOR (bipartition result, const bipartition b1, const bipartition b2, bool update_count) { int i; for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] ^ b2->bs[i]; result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */ if (update_count) bipartition_count_n_ones (result); else result->n_ones = 0;// update_count = false should be used only when you don't care about this value (temp var) } void bipartition_XORNOT (bipartition result, const bipartition b1, const bipartition b2, bool update_count) { /* equivalent to XOR followed by NOT */ int i; for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] ^ (~b2->bs[i]); result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */ if (update_count) bipartition_count_n_ones (result); else result->n_ones = 0;// update_count = false should be used only when you don't care about this value (temp var) } void bipartition_NOT (bipartition result, const bipartition bip) { int i; for (i=0; i < result->n->ints; i++) result->bs[i] = ~bip->bs[i]; result->bs[i-1] &= bip->n->mask; /* do not invert last bits (do not belong to bipartition) */ result->n_ones = bip->n->bits - bip->n_ones; } void bipartition_count_n_ones (const bipartition bip) { int i; unsigned long long j; bip->n_ones = 0; /* // Naive approach for (i=0; i < bip->n_ints - 1; i++) for (j=0; j < BitStringSize; j++) bip->n_ones += ((bip->bs[i] >> j) & 1LL); for (j=0; j < bip->n_bits%BitStringSize; j++) bip->n_ones += ((bip->bs[i] >> j) & 1LL); */ // clear the least significant bit set per iteration (Peter Wegner in CACM 3 (1960), 322, mentioned in K&R) for (i=0; i < bip->n->ints; i++) for (j = bip->bs[i]; j; bip->n_ones++) j &= j - 1LL; } bool bipartition_is_equal (const bipartition b1, const bipartition b2) { int i; if (b1->n_ones != b2->n_ones) return false; if (b1->n->ints != b2->n->ints) return false; for (i=0; i < b1->n->ints - 1; i++) if (b1->bs[i] != b2->bs[i]) return false; b1->bs[i] &= b1->n->mask; b2->bs[i] &= b2->n->mask; /* apply mask before comparing last elems */ if (b1->bs[i] != b2->bs[i]) return false; return true; } bool bipartition_is_equal_bothsides (const bipartition b1, const bipartition b2) { int i; bool equal = true; for (i=0; (i < b1->n->ints - 1) && (equal); i++) if (b1->bs[i] != b2->bs[i]) equal = false; if ((equal) && ((b1->bs[i] & b1->n->mask) != (b2->bs[i] & b2->n->mask))) equal = false; if (equal) return true; /* the biparitions are already the same, without flipping the bits */ /* now we compare one bipartition with the complement of the other */ for (i=0; (i < b1->n->ints - 1); i++) if (b1->bs[i] != ~b2->bs[i]) return false; if ((b1->bs[i] & b1->n->mask) != ((~b2->bs[i]) & b2->n->mask)) return false; return true; /* they are the exact complement of one another */ } bool bipartition_is_larger (const bipartition b1, const bipartition b2) { int i; if (b1->n_ones > b2->n_ones) return true; if (b1->n_ones < b2->n_ones) return false; for (i = b1->n->ints - 1; (i >= 0) && (b1->bs[i] == b2->bs[i]); i--); /* find position of distinct bipartition elem*/ if (i < 0) return false; /* identical bipartitions */ if (b1->bs[i] > b2->bs[i]) return true; else return false; } void bipartition_flip_to_smaller_set (bipartition bip) { int i = bip->n->ints - 1; /* most significant position -- consistent with is_larger() above, using OLD algo below */ if ((2 * bip->n_ones) < bip->n->bits) return; /* it is already the smaller set */ /* OLD always x is different from ~x, so we just look at last element ("largest digits of number") */ // if (((2 * bip->n_ones) == bip->n->bits) && (bip->bs[i] < (bip->n->mask & ~bip->bs[i]))) return; /* NEW: resolve ties by always showing the same "side" of bipartition, that is, the one having an arbitrary leaf (first one, in our case) */ if (((2 * bip->n_ones) == bip->n->bits) && (bip->bs[0] & 1LL)) return; for (i=0; i < bip->n->ints; i++) bip->bs[i] = ~bip->bs[i]; /* like bipartition_NOT() */ bip->bs[i-1] &= bip->n->mask; /* do not invert last bits (do not belong to bipartition) */ bip->n_ones = bip->n->bits - bip->n_ones; return; } bool bipartition_is_bit_set (const bipartition bip, int position) { if (bip->bs[(int)(position/BitStringSize)] & (1LL << (int)(position%BitStringSize))) return true; return false; } bool bipartition_contains_bits (const bipartition b1, const bipartition b2) { /* generalization of bipartition_is_bit_set(); b1 contains or not b2 */ int i; if (b1->n_ones < b2->n_ones) return false; for (i=0; i < b1->n->ints; i++) if ((b2->bs[i]) && (b2->bs[i] != (b1->bs[i] & b2->bs[i]))) return false; return true; } void bipartition_to_int_vector (const bipartition b, int *id, int vecsize) { int i, j, k = 0; for (i=0; i < b->n->ints; i++) for (j=0; (j < BitStringSize) && (k < vecsize); j++) if ( ((b->bs[i] >> j) & 1LL) ) id[k++] = i * BitStringSize + j; } /* void bipartition_print_to_stdout (const bipartition b1) { int i, j; for (i = 0; i < b1->n->ints - 1; i++) { for (j = 0; j < BitStringSize; j++) printf ("%d", (int)((b1->bs[i] >> j) & 1LL)); printf ("."); } for (j = 0; j < b1->n->bits%BitStringSize; j++) printf ("%d", (int)((b1->bs[i] >> j) & 1LL)); printf ("[%d] ", b1->n_ones); } */ void bipartition_replace_bit_in_vector (bipartition *bvec, int n_b, int to, int from, bool reduce) { /* copy info from position "from" to position "to" */ int k, j = from%BitStringSize, i = from/BitStringSize, j2 = to%BitStringSize, i2 = to/BitStringSize; /* boolean "reduce" means that bitstring space will be reduced (last bits will be removed), therefore the update of * n_ones is different from default bipartition_set() behaviour: it's not an extra "1" (that is, one that did not * contribute to n_ones), but an existing "1" that change places. Schematically: * from -> to | normal n_ones count | when bitstring is reduced afterwards * 0 -> 0 | 0 | 0 * 0 -> 1 | -1 | -1 * 1 -> 0 | +1 | 0 (since it's a leaf that belonged to position "from" and now is on position "to") * 1 -> 1 | 0 | -1 (in fact one of the two "1"s dissapeared after reducing the bitstring * (the above description is outdated since I rewrote by hand the bit functions -- observe how we must erase 1 values from "from") */ if (reduce) for (k = 0; k < n_b; k++) { // copy 0 or 1 values, erasing "from" values to avoid problems after reducing space (hanging 1s out of range) if ( ((bvec[k]->bs[i] >> j) & 1LL) && ((bvec[k]->bs[i2] >> j2) & 1LL) ) { bvec[k]->n_ones--; bvec[k]->bs[i] &= ~(1LL << j); } else if ( ((bvec[k]->bs[i] >> j) & 1LL) && !((bvec[k]->bs[i2] >> j2) & 1LL) ) { bvec[k]->bs[i2] |= (1LL << j2); bvec[k]->bs[i] &= ~(1LL << j); } else if ( !((bvec[k]->bs[i] >> j) & 1LL) && ((bvec[k]->bs[i2] >> j2) & 1LL) ) { bvec[k]->bs[i2] &= ~(1LL << j2); bvec[k]->n_ones--; } /* else do nothing (from zero to zero) */ } else for (k = 0; k < n_b; k++) { // copy 0 or 1 values if ( ((bvec[k]->bs[i] >> j) & 1LL) ) bipartition_set_lowlevel (bvec[k], i2, j2); // will check if n_ones change or not else bipartition_unset_lowlevel (bvec[k], i2, j2); } } void bipartition_resize_vector (bipartition *bvec, int n_b) { int k, i = bvec[0]->n->ints - 1; for (k = 0; k < n_b; k++) { bvec[k]->bs[i] &= bvec[0]->n->mask; bipartition_count_n_ones (bvec[k]); } } phangorn/src/sankoff.c0000644000175100001440000002051413020643153014465 0ustar hornikusers/* * dist.c * * (c) 2008-2016 Klaus Schliep (klaus.schliep@gmail.com) * * * This code may be distributed under the GNU GPL * */ # define USE_RINTERNALS #include #include #include #include #include SEXP C_rowMin(SEXP sdat, SEXP sn, SEXP sk){ int i, h, n=INTEGER(sn)[0], k=INTEGER(sk)[0]; double x, *res, *dat; SEXP result; PROTECT(result = allocVector(REALSXP, n)); res = REAL(result); PROTECT(sdat = coerceVector(sdat, REALSXP)); dat = REAL(sdat); for(i = 0; i < n; i++){ x = dat[i]; for(h = 1; h< k; h++) {if(dat[i + h*n] < x) x=dat[i + h*n];} res[i] = x; } UNPROTECT(2); return(result); } void rowMin2(double *dat, int n, int k, double *res){ int i, h; double x; for(i = 0; i < n; i++){ x = dat[i]; for(h = 1; h< k; h++) {if(dat[i + h*n] < x) x=dat[i + h*n];} res[i] = x; } } /* * never used void rowMinInt(int *dat, int n, int k, int *res){ int i, h; int x; for(i = 0; i < n; i++){ x = dat[i]; for(h = 1; h< k; h++) {if(dat[i + h*n] < x) x=dat[i + h*n];} res[i] = x; } } */ void sankoff4(double *dat, int n, double *cost, int k, double *result){ int i, j, h; double x; //tmp[k], double *tmp; tmp = malloc(k * sizeof(double)); for(i = 0; i < n; i++){ for(j = 0; j < k; j++){ for(h = 0; h< k; h++){tmp[h] = dat[i + h*n] + cost[h + j*k];} x = tmp[0]; for(h = 1; h< k; h++) {if(tmp[h]=0; j--) { PROTECT(result = allocMatrix(REALSXP, nrx, ncx)); res = REAL(result); if (pj != nodes[j]) { for(i=0; i<(nrx * ncx); i++) tmp[i] = 0.0; sankoff4(REAL(VECTOR_ELT(dlist, nodes[j])), nrx, cost, ncx, tmp); for(i=0; i<(nrx * ncx); i++) res[i] = tmp[i] ; pj = nodes[j]; start = j; } else for(i=0; i<(nrx * ncx); i++) res[i] = tmp[i] ; k = start; while (k >= 0 && pj == nodes[k]) { if (k != j) sankoff4(REAL(VECTOR_ELT(data, edges[k])), nrx, cost, ncx, res); k--; } SET_VECTOR_ELT(dlist, edges[j], result); UNPROTECT(1); } UNPROTECT(1); return(dlist); } SEXP sankoffMPR(SEXP dlist, SEXP plist, SEXP scost, SEXP nr, SEXP nc, SEXP node, SEXP edge){ R_len_t i, n = length(node); int nrx=INTEGER(nr)[0], ncx=INTEGER(nc)[0], n0; int ei, j, *nodes=INTEGER(node), *edges=INTEGER(edge); SEXP result, dlist2; //tmp, double *res, *cost; // *rtmp, cost = REAL(scost); n0 = nodes[n-1L]; PROTECT(dlist2 = allocVector(VECSXP, n+1L)); PROTECT(result = allocMatrix(REALSXP, nrx, ncx)); res = REAL(result); for(j=0;j<(nrx*ncx);j++)res[j]=0.0; for(j=n-1L; j>=0; j--) { if(nodes[j]!=n0){ SET_VECTOR_ELT(dlist2, n0, result); UNPROTECT(1); n0 = nodes[j]; PROTECT(result = allocMatrix(REALSXP, nrx, ncx)); res = REAL(result); for(i=0; i<(nrx * ncx); i++) res[i] = 0.0; sankoff4(REAL(VECTOR_ELT(plist,nodes[j])), nrx, cost, ncx, res); } ei = edges[j]; sankoff4(REAL(VECTOR_ELT(dlist,ei)), nrx, cost, ncx, res); } SET_VECTOR_ELT(dlist2, n0, result); UNPROTECT(2); return(dlist2); } phangorn/src/ml.c0000644000175100001440000012472313020643153013455 0ustar hornikusers/* * ml.c * * (c) 2008-2016 Klaus Schliep (klaus.schliep@gmail.com) * * * This code may be distributed under the GNU GPL * */ # define USE_RINTERNALS #include #include #include #include #include #define LINDEX(i, k) (i - ntips - 1L) * (nr * nc) + k * ntips * (nr * nc) // index for LL #define LINDEX2(i, k) (i - *ntips - 1L) * (*nr* *nc) + k * *ntips * (*nr * *nc) // index for scaling matrix SCM #define LINDEX3(i, j) (i - *ntips - 1L) * *nr + j * *ntips * *nr //nr statt *nr char *transa = "N", *transb = "N"; double one = 1.0, zero = 0.0; int ONE = 1L; const double ScaleEPS = 1.0/4294967296.0; const double ScaleMAX = 4294967296.0; const double LOG_SCALE_EPS = -22.18070977791824915926; // 2^64 = 18446744073709551616 static double *LL, *WEIGHTS; static int *SCM, *XXX; void ll_free(){ free(LL); free(SCM); } /* LL likelihood for internal edges SCM scaling coefficients nNodes, nTips, kmax SC = (int *) R_alloc(nr * k, sizeof(int)); sc = (int *) R_alloc(nr, sizeof(int)); tmp = (double *) R_alloc(nr * k, sizeof(double)); */ void ll_init(int *nr, int *nTips, int *nc, int *k) { int i; LL = (double *) calloc(*nr * *nc * *k * *nTips, sizeof(double)); SCM = (int *) calloc(*nr * *k * *nTips, sizeof(int)); // * 2L for(i =0; i < (*nr * *k * *nTips); i++) SCM[i] = 0L; } // contrast und nr,nc,k void ll_free2(){ free(LL); free(SCM); free(XXX); } void ll_init2(int *data, int *weights, int *nr, int *nTips, int *nc, int *k) { int i; LL = (double *) calloc(*nr * *nc * *k * *nTips, sizeof(double)); WEIGHTS = (double *) calloc(*nr, sizeof(double)); XXX = (int *) calloc(*nr * *nTips, sizeof(int)); SCM = (int *) calloc(*nr * *k * *nTips, sizeof(int)); // * 2L for(i =0; i < (*nr * *k * *nTips); i++) SCM[i] = 0L; for(i =0; i < (*nr * *nTips); i++) XXX[i] = data[i]; for(i =0; i < *nr; i++) WEIGHTS[i] = weights[i]; } int edgeLengthIndex(int child, int parent, int nTips){ if(child <= nTips) return(child-1L); else{ if(child < parent) return(parent-1L); return(child -1L); } } void matm(int *x, double *contrast, int *nr, int *nc, int *nco, double *result){ int i, j; for(i = 0; i < (*nr); i++){ for(j = 0; j < (*nc); j++) result[i + j*(*nr)] *= contrast[x[i] - 1L + j*(*nco)]; } } SEXP invSites(SEXP dlist, SEXP nr, SEXP nc, SEXP contrast, SEXP nco){ R_len_t n = length(dlist); int nrx=INTEGER(nr)[0], ncx=INTEGER(nc)[0], i, j; SEXP result; PROTECT(result = allocMatrix(REALSXP, nrx, ncx)); double *res; res = REAL(result); for(j=0; j < (nrx * ncx); j++) res[j] = 1.0; for(i=0; i < n; i++) matm(INTEGER(VECTOR_ELT(dlist, i)), REAL(contrast), INTEGER(nr), INTEGER(nc), INTEGER(nco), res); UNPROTECT(1); // result return(result); } void scaleMatrix(double *X, int *nr, int *nc, int *result){ int i, j; double tmp; for(i = 0; i < *nr; i++) { tmp = 0.0; for(j = 0; j < *nc; j++) tmp += X[i + j* *nr]; while(tmp < ScaleEPS){ for(j = 0; j < *nc; j++) X[i + j* *nr] *=ScaleMAX; result[i] +=1L; tmp *= ScaleMAX; } } } // contrast to full void matp(int *x, double *contrast, double *P, int *nr, int *nc, int *nrs, double *result){ int i, j; double *tmp; tmp = (double *) R_alloc((*nc) *(*nrs), sizeof(double)); F77_CALL(dgemm)(transa, transb, nrs, nc, nc, &one, contrast, nrs, P, nc, &zero, tmp, nrs); for(i = 0; i < (*nr); i++){ for(j = 0; j < (*nc); j++) result[i + j*(*nr)] = tmp[x[i] - 1L + j*(*nrs)]; } } void rowMinScale(int *dat, int n, int k, int *res){ int i, h; int tmp; for(i = 0; i < n; i++){ tmp = dat[i]; for(h = 1; h< k; h++) {if(dat[i + h*n] < tmp) tmp=dat[i + h*n];} if(tmp>0L){for(h = 0; h< k; h++) dat[i + h*n] -= tmp;} res[i] = tmp; } } static R_INLINE void getP(double *eva, double *ev, double *evi, int m, double el, double w, double *result){ int i, j, h; double res; //tmp[m], double *tmp; tmp = malloc(m * sizeof(double)); for(i = 0; i < m; i++) tmp[i] = exp(eva[i] * w * el); for(i = 0; i < m; i++){ for(j = 0; j < m; j++){ res = 0.0; for(h = 0; h < m; h++) res += ev[i + h*m] * tmp[h] * evi[h + j*m]; result[i+j*m] = res; } } free(tmp); } SEXP getPM(SEXP eig, SEXP nc, SEXP el, SEXP w){ R_len_t i, j, nel, nw, k; int m=INTEGER(nc)[0], l=0; double *ws=REAL(w); double *edgelen=REAL(el); double *eva, *eve, *evei; SEXP P, RESULT; nel = length(el); nw = length(w); if(!isNewList(eig)) error("'eig' must be a list"); eva = REAL(VECTOR_ELT(eig, 0)); eve = REAL(VECTOR_ELT(eig, 1)); evei = REAL(VECTOR_ELT(eig, 2)); PROTECT(RESULT = allocVector(VECSXP, nel*nw)); for(j=0; j0)scaleMatrix(&ans[ni * rc], nr, nc, scaleTmp); // (ni-nTips) ni = node[i]; if(ei < nTips) matp(INTEGER(VECTOR_ELT(dlist, ei)), contrast, P, nr, nc, &nco, &ans[ni * rc]); else F77_CALL(dgemm)(transa, transb, nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, &ans[ni * rc], nr); } else { if(ei < nTips) matp(INTEGER(VECTOR_ELT(dlist, ei)), contrast, P, nr, nc, &nco, rtmp); else F77_CALL(dgemm)(transa, transb, nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, rtmp, nr); for(j=0; j < rc; j++) ans[ni * rc + j] *= rtmp[j]; } } scaleMatrix(&ans[ni * rc], nr, nc, scaleTmp); F77_CALL(dgemv)(transa, nr, nc, &one, &ans[ni * rc], nr, bf, &ONE, &zero, TMP, &ONE); } /* // neue Version: keine SEXP (dlist) void lll0(int *X, double *eva, double *eve, double *evei, double *el, double g, int *nr, int *nc, int *node, int *edge, int nTips, double *contrast, int nco, int n, int *scaleTmp, double *bf, double *TMP, double *ans){ int ni, ei, j, i, rc; // R_len_t i, n = length(node); double *rtmp, *P; ni = -1; rc = *nr * *nc; rtmp = (double *) R_alloc(*nr * *nc, sizeof(double)); P = (double *) R_alloc(*nc * *nc, sizeof(double)); for(j=0; j < *nr; j++) scaleTmp[j] = 0L; for(i = 0; i < n; i++) { getP(eva, eve, evei, *nc, el[i], g, P); ei = edge[i]; if(ni != node[i]){ if(ni>0)scaleMatrix(&ans[ni * rc], nr, nc, scaleTmp); // (ni-nTips) ni = node[i]; if(ei < nTips) matp(&X[ei * *nr], contrast, P, nr, nc, &nco, &ans[ni * rc]); else F77_CALL(dgemm)(transa, transb, nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, &ans[ni * rc], nr); } else { if(ei < nTips) matp(&X[ei * *nr], contrast, P, nr, nc, &nco, rtmp); else F77_CALL(dgemm)(transa, transb, nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, rtmp, nr); for(j=0; j < rc; j++) ans[ni * rc + j] *= rtmp[j]; } } scaleMatrix(&ans[ni * rc], nr, nc, scaleTmp); F77_CALL(dgemv)(transa, nr, nc, &one, &ans[ni * rc], nr, bf, &ONE, &zero, TMP, &ONE); } */ // this seems to work perfectly void lll3(SEXP dlist, double *eva, double *eve, double *evei, double *el, double g, int *nr, int *nc, int *node, int *edge, int nTips, double *contrast, int nco, int n, int *scaleTmp, double *bf, double *TMP, double *ans, int *SC){ int ni, ei, j, i, rc; // R_len_t i, n = length(node); double *rtmp, *P; ni = -1L; rc = *nr * *nc; rtmp = (double *) R_alloc(*nr * *nc, sizeof(double)); P = (double *) R_alloc(*nc * *nc, sizeof(double)); for(j=0; j < *nr; j++) scaleTmp[j] = 0L; for(i = 0; i < n; i++) { getP(eva, eve, evei, *nc, el[i], g, P); ei = edge[i]; if(ni != node[i]){ if(ni>0)scaleMatrix(&ans[ni * rc], nr, nc, &SC[ni * *nr]); // (ni-nTips) ni = node[i]; for(j=0; j < *nr; j++) SC[j + ni * *nr] = 0L; if(ei < nTips) matp(INTEGER(VECTOR_ELT(dlist, ei)), contrast, P, nr, nc, &nco, &ans[ni * rc]); else{ F77_CALL(dgemm)(transa, transb, nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, &ans[ni * rc], nr); for(j=0; j < *nr; j++) SC[ni * *nr + j] = SC[(ei-nTips) * *nr + j]; } } else { if(ei < nTips) matp(INTEGER(VECTOR_ELT(dlist, ei)), contrast, P, nr, nc, &nco, rtmp); else{ F77_CALL(dgemm)(transa, transb, nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, rtmp, nr); for(j=0; j < *nr; j++) SC[ni * *nr + j] += SC[(ei-nTips) * *nr + j]; } for(j=0; j < rc; j++) ans[ni * rc + j] *= rtmp[j]; } } scaleMatrix(&ans[ni * rc], nr, nc, &SC[ni * *nr]); for(j=0; j < *nr; j++) scaleTmp[j] = SC[ni * *nr + j]; F77_CALL(dgemv)(transa, nr, nc, &one, &ans[ni * rc], nr, bf, &ONE, &zero, TMP, &ONE); } /* SEXP PML_NEW2(SEXP EL, SEXP W, SEXP G, SEXP NR, SEXP NC, SEXP K, SEXP eig, SEXP bf, SEXP node, SEXP edge, SEXP NTips, SEXP nco, SEXP contrast, SEXP N){ int nr=INTEGER(NR)[0], nc=INTEGER(NC)[0], k=INTEGER(K)[0], i, indLL; int nTips = INTEGER(NTips)[0], *SC; double *g=REAL(G), *tmp, logScaleEPS; SEXP TMP; double *eva, *eve, *evei; eva = REAL(VECTOR_ELT(eig, 0)); eve = REAL(VECTOR_ELT(eig, 1)); evei = REAL(VECTOR_ELT(eig, 2)); SC = (int *) R_alloc(nr * k, sizeof(int)); PROTECT(TMP = allocMatrix(REALSXP, nr, k)); // changed tmp=REAL(TMP); for(i=0; i<(k*nr); i++)tmp[i]=0.0; indLL = nr * nc * nTips; for(i=0; intips){ for(i = 0; i < k; i++){ PROTECT(X = allocMatrix(REALSXP, nr, nc)); getP(eva, eve, evei, nc, el, g[i], P); helpDADI(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], P, nr, nc, tmp); helpPrep(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], eve, evi, nr, nc, tmp, REAL(X)); SET_VECTOR_ELT(RESULT, i, X); UNPROTECT(1); } } else{ for(i = 0; i < k; i++){ PROTECT(X = allocMatrix(REALSXP, nr, nc)); getP(eva, eve, evei, nc, el, g[i], P); helpDAD5(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast, P, nr, nc, nco, tmp); helpPrep2(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast2, evi, nr, nc, nco, REAL(X)); //; SET_VECTOR_ELT(RESULT, i, X); UNPROTECT(1); } } UNPROTECT(1); //RESULT return(RESULT); } // child *= (dad * P) void goDown(double *dad, double *child, double *P, int nr, int nc, double *res){ F77_CALL(dgemm)(transa, transb, &nr, &nc, &nc, &one, dad, &nr, P, &nc, &zero, res, &nr); for(int j=0; j<(nc * nr); j++) child[j]*=res[j]; } // dad *= (child * P) void goUp(double *dad, int *child, double *contrast, double *P, int nr, int nc, int nco, double *res){ matp(child, contrast, P, &nr, &nc, &nco, res); for(int j=0; j<(nc * nr); j++) dad[j]*=res[j]; } // , double *w void updateLLQ(SEXP dlist, int pa, int ch, double *eva, double *eve, double*evei, double el, double *g, int nr, int nc, int ntips, double *contrast, int nco, int k, double *tmp, double *P){ int i; if(ch>ntips){ for(i = 0; i < k; i++){ getP(eva, eve, evei, nc, el, g[i], P); goDown(&LL[LINDEX(ch, i)], &LL[LINDEX(pa, i)], P, nr, nc, tmp); } } else{ for(i = 0; i < k; i++){ getP(eva, eve, evei, nc, el, g[i], P); goUp(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast, P, nr, nc, nco, tmp); } } } // double *w, void updateLL2(SEXP dlist, int pa, int ch, double *eva, double *eve, double*evei, double el, double *g, int nr, int nc, int ntips, double *contrast, int nco, int k, double *tmp, double *P){ int i; if(ch>ntips){ for(i = 0; i < k; i++){ getP(eva, eve, evei, nc, el, g[i], P); goDown(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], P, nr, nc, tmp); } } else{ for(i = 0; i < k; i++){ getP(eva, eve, evei, nc, el, g[i], P); goUp(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast, P, nr, nc, nco, tmp); } } } void ExtractScale(int ch, int k, int *nr, int *ntips, double *res){ int i; int j, blub, tmp; for(i = 0; i < k; i++){ blub = LINDEX3(ch, i); for(j=0; j< *nr; j++) res[j +i * *nr] = SCM[blub+j]; } for(i = 0; i< *nr; i++){ tmp = res[i]; for(j = 1; j 1e-05) && (k < 5) ) { if(scalep>0.6){ NR55(eva, ncx-1L, edle, ws, gs, X, INTEGER(ld)[0], nrx, f, tmp); ll=0.0; lll=0.0; for(i=0; i 10.0) newedle = 10.0; if (newedle < 1e-8) newedle = edle/2; if (newedle < 1e-8) newedle = 1e-8; // 1e-8 phyML for(i=0; i0L)SET_VECTOR_ELT(RESULT, 1, getM3(child, dad, P, nr, nc)); if(INTEGER(retB)[0]>0L)SET_VECTOR_ELT(RESULT, 2, getM3(dad, child, P, nr, nc)); // add variance ?? SET_VECTOR_ELT(RESULT, 3, ScalarReal(l1)); UNPROTECT(3); return (RESULT); } //, SEXP basefreq SEXP FS5(SEXP eig, SEXP nc, SEXP el, SEXP w, SEXP g, SEXP X, SEXP ld, SEXP nr, SEXP weight, SEXP f0) { SEXP RESULT; // EL, P; double *tmp, *f, *wgt=REAL(weight), edle, ledle, newedle, eps=10, *eva=REAL(VECTOR_ELT(eig,0)); double ll, lll, delta=0.0, scalep = 1.0, *ws=REAL(w), *gs=REAL(g), l1=0.0, l0=0.0; double y; int i, k=0, ncx=INTEGER(nc)[0], nrx=INTEGER(nr)[0]; tmp = (double *) R_alloc(nrx, sizeof(double)); f = (double *) R_alloc(nrx, sizeof(double)); PROTECT(RESULT = allocVector(REALSXP, 3)); edle = REAL(el)[0]; for(i=0; i 1e-05) && (k < 10) ) { if(scalep>0.6){ NR55(eva, ncx-1L, edle, ws, gs, X, INTEGER(ld)[0], nrx, f, tmp); ll=0.0; lll=0.0; for(i=0; i 10.0) newedle = 10.0; // if (newedle < 1e-8) newedle = edle/2; if (newedle < 1e-8) newedle = 1e-8; // 1e-8 phyML for(i=0; i 1e-05) && (k < 10) ) { if(scalep>0.6){ NR77(eva, nc-1L, edle, w, g, X, ld, nr, f, tmp); ll=0.0; lll=0.0; for(i=0; i 10.0) newedle = 10.0; if (newedle < 1e-8) newedle = 1e-8; // 1e-8 phyML for(i=0; intips){ for(i = 0; i < k; i++){ getP(eva, eve, evei, nc, oldel, g[i], P); helpDADI(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], P, nr, nc, tmp); helpPrep(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], eve, evi, nr, nc, tmp, &X[i*nr*nc]); for(h = 0; h < nc; h++){ for(j = 0; j < nr; j++){ X[j+h*nr + i*nr*nc] *= blub[j+i*nr]; } } } } else{ for(i = 0; i < k; i++){ getP(eva, eve, evei, nc, oldel, g[i], P); helpDAD5(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast, P, nr, nc, nco, tmp); helpPrep2(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast2, evi, nr, nc, nco, &X[i*nr*nc]); //; for(h = 0; h < nc; h++){ for(j = 0; j < nr; j++){ X[j+h*nr + i*nr*nc] *= blub[j+i*nr]; } } } } fs3(eva, nc, oldel, w, g, X, k, nr, weight, f0, res); updateLL2(dlist, pa, ch, eva, eve, evei, res[0], g, nr, nc, ntips, contrast, nco, k, tmp, P); el[ch-1L] = res[0]; //edgeLengthIndex if (ch > ntips) loli = ch; else loli = pa; } UNPROTECT(1); //RESULT return(RESULT); } SEXP optQrtt(SEXP PARENT, SEXP CHILD, SEXP eig, SEXP EVI, SEXP EL, SEXP W, SEXP G, SEXP NR, SEXP NC, SEXP NTIPS, SEXP CONTRAST, SEXP CONTRAST2, SEXP NCO, SEXP dlist, SEXP WEIGHT, SEXP F0){ int i, k=length(W), h, j, m, lEL=length(EL); int nc=INTEGER(NC)[0], nr=INTEGER(NR)[0], ntips=INTEGER(NTIPS)[0]; int *parent=INTEGER(PARENT), *child=INTEGER(CHILD), pa, ch; int nco =INTEGER(NCO)[0]; // loli, double *weight=REAL(WEIGHT), *f0=REAL(F0), *w=REAL(W); double *g=REAL(G), *evi=REAL(EVI), *contrast=REAL(CONTRAST), *contrast2=REAL(CONTRAST2); double *el, *X; double *eva, *eve, *evei, *tmp, *P; double *blub = (double *) R_alloc(nr * nc, sizeof(double)); double oldel; double *res = (double *) R_alloc(3L, sizeof(double)); tmp = (double *) R_alloc(nr * nc, sizeof(double)); P = (double *) R_alloc(nc * nc, sizeof(double)); X = (double *) R_alloc(k * nr * nc, sizeof(double)); ExtractScale(parent[0], k, &nr, &ntips, blub); SEXP RESULT; PROTECT(RESULT = allocVector(REALSXP, lEL)); el=REAL(RESULT); for(i = 0; i < lEL; i++) el[i] = REAL(EL)[i]; eva = REAL(VECTOR_ELT(eig, 0)); eve = REAL(VECTOR_ELT(eig, 1)); evei = REAL(VECTOR_ELT(eig, 2)); for(m = 4L; m > -1L; m--){ pa = parent[m]; ch = child[m]; oldel=el[m]; // moveDad if(ch>ntips){ for(i = 0; i < k; i++){ getP(eva, eve, evei, nc, oldel, g[i], P); helpDADI(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], P, nr, nc, tmp); helpPrep(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], eve, evi, nr, nc, tmp, &X[i*nr*nc]); for(h = 0; h < nc; h++){ for(j = 0; j < nr; j++){ X[j+h*nr + i*nr*nc] *= blub[j+i*nr]; } } } } else{ for(i = 0; i < k; i++){ getP(eva, eve, evei, nc, oldel, g[i], P); helpDAD5(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast, P, nr, nc, nco, tmp); helpPrep2(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast2, evi, nr, nc, nco, &X[i*nr*nc]); //; for(h = 0; h < nc; h++){ for(j = 0; j < nr; j++){ X[j+h*nr + i*nr*nc] *= blub[j+i*nr]; } } } } fs3(eva, nc, oldel, w, g, X, k, nr, weight, f0, res); // go up // if i=2 go down if(m==2)updateLLQ(dlist, ch, pa, eva, eve, evei, res[0], g, nr, nc, ntips, contrast, nco, k, tmp, P); else updateLLQ(dlist, pa, ch, eva, eve, evei, res[0], g, nr, nc, ntips, contrast, nco, k, tmp, P); el[m] = res[0]; } UNPROTECT(1); //RESULT return(RESULT); } SEXP PML4(SEXP dlist, SEXP EL, SEXP W, SEXP G, SEXP NR, SEXP NC, SEXP K, SEXP eig, SEXP bf, SEXP node, SEXP edge, SEXP NTips, SEXP nco, SEXP contrast, SEXP N){ int nr=INTEGER(NR)[0], nc=INTEGER(NC)[0], k=INTEGER(K)[0], i, j, indLL; int nTips = INTEGER(NTips)[0], *SC, *sc; double *g=REAL(G), *w=REAL(W), *tmp, *res; SEXP TMP; double *eva, *eve, *evei; eva = REAL(VECTOR_ELT(eig, 0)); eve = REAL(VECTOR_ELT(eig, 1)); evei = REAL(VECTOR_ELT(eig, 2)); SC = (int *) R_alloc(nr * k, sizeof(int)); sc = (int *) R_alloc(nr, sizeof(int)); tmp = (double *) R_alloc(nr * k, sizeof(double)); PROTECT(TMP = allocVector(REALSXP, nr)); res=REAL(TMP); for(i=0; i<(k*nr); i++)tmp[i]=0.0; indLL = nr * nc * nTips; for(i=0; i do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include using namespace Rcpp; // allDescCPP List allDescCPP(IntegerMatrix orig, int nTips); RcppExport SEXP phangorn_allDescCPP(SEXP origSEXP, SEXP nTipsSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< IntegerMatrix >::type orig(origSEXP); Rcpp::traits::input_parameter< int >::type nTips(nTipsSEXP); rcpp_result_gen = Rcpp::wrap(allDescCPP(orig, nTips)); return rcpp_result_gen; END_RCPP } // allChildrenCPP List allChildrenCPP(IntegerMatrix orig); RcppExport SEXP phangorn_allChildrenCPP(SEXP origSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< IntegerMatrix >::type orig(origSEXP); rcpp_result_gen = Rcpp::wrap(allChildrenCPP(orig)); return rcpp_result_gen; END_RCPP } phangorn/src/phangorn.c0000644000175100001440000005501713020643153014660 0ustar hornikusers/* * phangorn.c * * (c) 2008-2016 Klaus Schliep (klaus.schliep@gmail.com) * * * This code may be distributed under the GNU GPL * */ # define USE_RINTERNALS #include #include #include #include #include // off-diagonal #define DINDEX(i, j) n*(i - 1) - i * (i - 1)/2 + j - i - 1 // with diagonal (+i), R index (+1) #define DINDEX2(i, j) n*(i - 1) - i * (i - 1)/2 + j - 1 // index likelihood pml // need to define nr, nc, nTips, nNodes k #define LINDEX(i) (i-nTips) * (nr*nc) //+ k * nTips * (nr * nc) #define LINDEX2(i, k) (i-nTips) * (nr*nc) + k * nTips * (nr * nc) #define LINDEX3(i, k) (i-*nTips-1L) * (*nr* *nc) + k * *nTips * (*nr * *nc) // index sankoff #define SINDEX(i) i * (nr*nc) void countCycle(int *M, int *l, int *m, int *res){ int j, i, tmp; res[0]=0L; for (i=0; i<*l; i++) { tmp = 0; if(M[i] != M[i + (*m -1) * *l])tmp++; for (j=1; j<*m; j++) { if(M[i + (j-1)* *l] != M[i + j * *l])tmp++; } if(tmp>2L)res[0]+=tmp; } } void countCycle2(int *M, int *l, int *m, int *res){ int j, i, tmp; for (i=0; i<*l; i++) { tmp = 0L; if(M[i] != M[i + (*m -1) * *l])tmp=1L; for (j=1; j<*m; j++) { if(M[i + (j-1L)* *l] != M[i + j * *l])tmp++; } res[i]=tmp; } } void nodeH(int *edge, int *node, double *el, int *l, double *res){ int ei, i; for (i=*l-1L; i>=0; i--) { ei = edge[i] - 1L; res[ei] = res[node[i]-1L] + el[ei]; } } SEXP rowMax(SEXP sdat, SEXP sn, SEXP sk){ int i, h, n=INTEGER(sn)[0], k=INTEGER(sk)[0]; double x, *res, *dat; SEXP result; PROTECT(result = allocVector(REALSXP, n)); res = REAL(result); PROTECT(sdat = coerceVector(sdat, REALSXP)); dat = REAL(sdat); for(i = 0; i < n; i++){ x = dat[i]; for(h = 1; h< k; h++) {if(dat[i + h*n] > x) x=dat[i + h*n];} res[i] = x; } UNPROTECT(2); return(result); } /* static R_INLINE void getP00(double *eva, double *ev, double *evi, int m, double el, double w, double *result){ int i, j, h; double tmp, res; for(i = 0; i < m; i++){ tmp = exp(eva[i] * w * el); for(j=0; j0) & (tmp<(*nbin+1L)) ) ans[tmp-1L] ++; } } void C_reorder(int *from, int *to, int *n, int *sumNode, int *neworder, int *root){ int i, j, sum=0, k, Nnode, ind, *ord, *csum, *tips, *stack, z=0; // l, double *parent; int m=sumNode[0]; parent = (double *) R_alloc((*n), sizeof(double)); tips = (int *) R_alloc(m, sizeof(int)); ord = (int *) R_alloc((*n), sizeof(int)); csum = (int *) R_alloc( (m+1), sizeof(int)); stack = (int *) R_alloc(m, sizeof(int)); for(j=0;j<(*n);j++) parent[j] = (double)from[j]; for(j=0;j<(*n);j++) ord[j] = j; for(j=0;j -1){ j=stack[z]; if(tips[j]>0){ for(i=csum[j];i=0L; i--){ p = INTEGER(parent)[i]; if(tab[p]==1L){ res[i] = 1L; tab[INTEGER(child)[i]] = 1L; } } UNPROTECT(1); return(RESULT); } // std::merge void cisort(int *x, int *y, int a, int b, int *res){ int i, j, k; i=0; j=0; k=0; int xi=x[0]; int yi=y[0]; while(k<((a)+(b))){ if(i<(a)){ if( (xi j) return(DINDEX(j, i)); else return(DINDEX(i, j)); } // faster and less memory consuming cophenetic void copheneticHelp(int *left, int *right, int *ll, int *lr, int h, double *nh, int *nTips, double *dm){ int i, j, ind; for(i=0; i<*ll; i++){ for(j=0; j<*lr; j++){ ind = give_index3(left[i], right[j], *nTips); dm[ind] = 2.0*nh[h] - nh[left[i]-1L] - nh[right[j]-1L]; } } } void C_coph(int *tips, int *kids, int *ptips, int *pkids, int *ltips, int *lkids, int*Nnode, double *nh, int *nTips, double *dm){ int h, j, k, lk, pk, lt, rt, leftk, rightk; for(h=0; h<*Nnode; h++){ lk=lkids[h]; pk=pkids[h]; for(j=0; j<(lk-1L); j++){ leftk=kids[pk+j] - 1L; lt=ptips[leftk]; for(k=j+1L; k #include #include #include #include // #include "dist.h" // off-diagonal #define DINDEX(i, j) n*(i - 1) - i * (i - 1)/2 + j - i - 1 // with diagonal (+i), R index (+1) #define DINDEX2(i, j) n*(i - 1) - i * (i - 1)/2 + j - 1 // #define threshold parameters int give_index(int i, int j, int n) { if (i > j) return(DINDEX(j, i)); else return(DINDEX(i, j)); } int give_index2(int i, int j, int n) { if (i > j) return(DINDEX2(j, i)); else return(DINDEX2(i, j)); } void giveIndex(int *left, int* right, int *ll, int *lr, int *n, int *res){ int i, j, k; k=0; for (i = 0; i < *ll; i++){ for (j = 0; j < *lr; j++){ res[k] = give_index(left[i], right[j], *n); k++; } } } void giveIndex2(int *left, int* right, int *ll, int *lr, int *n, int *res){ int i, j, k; k=0; for (i = 0; i < *ll; i++){ for (j = 0; j < *lr; j++){ res[k] = give_index2(left[i], right[j], *n); k++; } } } void PD(int *x, int *y, int *n, int *weight){ int i, k; //n =length(x) for(i=0; i< *n; i++){ k=give_index(x[i], y[i], *n); weight[k]++; } } void pwIndex(int *left, int* right, int *l, int *n, double *w, double *res){ int i, k; k=0; for (i = 0; i < *l; i++){ k = give_index2(left[i], right[i], *n); res[k] += w[i]; } } SEXP PWI(SEXP LEFT, SEXP RIGHT, SEXP L, SEXP N, SEXP W, SEXP LI){ int i, li=INTEGER(LI)[0]; SEXP res; PROTECT(res = allocVector(REALSXP, li)); for(i = 0; i < li; i++)REAL(res)[i] = 0.0; pwIndex(INTEGER(LEFT), INTEGER(RIGHT), INTEGER(L), INTEGER(N), REAL(W), REAL(res)); UNPROTECT(1); return(res); } void C_fhm(double *v, int *n){ unsigned int level, i, j; unsigned int start, step, num_splits; unsigned int max_n = (unsigned int)*n; double vi, vj; num_splits = (1 << (*n)); step = 1; for(level = 0; level < max_n; level++){ start = 0; while(start < (num_splits-1)){ for(i = start; i < (start + step); i++){ j = i + step; vi = v[i]; vj = v[j]; v[i] = vi + vj; v[j] = vi - vj; } start = start + 2*step; } step *= 2; } } void distance_hadamard(double *d, int n) { unsigned int num_splits; unsigned int x, r, nr, p, b, e; unsigned int odd = 1; // The inner while loop can only terminate with odd == 1 so we don't need to set it inside the for loop. double cost, best_cost; num_splits = (1 << (n - 1)); for (x = 1; x < num_splits; ++x) { r = (x - 1) & x; // r = x without LSB nr = (r - 1) & r; // nr = r without LSB if (nr) { // If x contains 1 or 2 bits only, then it has already been computed as a pairwise distance. b = x - r; // b = LSB of x: the "fixed" taxon in the pair. best_cost = 1e20; e = 0; // e holds bits to the right of the current p. while (1) { p = r - nr; // p = 2nd half of pair cost = d[nr + e] + d[p + b]; if (cost < best_cost) best_cost = cost; if (!nr && odd) break; // Ensure we get the (LSB with reference taxon) pair when there are an odd number of taxa r = nr; e += p; nr = (r - 1) & r; // nr = r without LSB odd ^= 1; } d[x] = best_cost; } } d[0] = 0.0; } // int num_splits raus void pairwise_distances(double *dm, int n, double *d) { int k=0; unsigned int offset; for (int i = 0; i < (n-1); ++i) { for (int j = (i+1); j < n; ++j) { // Calculate the offset within the array to put the next value offset = (1 << i); if (j < n - 1) { // If i == n - 1 then this is a distance between the reference taxon and some other taxon. offset += (1 << j); // Note that "+" is safe since (1 << i) and (1 << j) are always different bits. } d[offset]=dm[k]; k++; } } } SEXP dist2spectra(SEXP dm, SEXP nx, SEXP ns) { int n = INTEGER(nx)[0]; int nsp = INTEGER(ns)[0]; double *res; SEXP result; PROTECT(result = allocVector(REALSXP, nsp)); res = REAL(result); pairwise_distances(REAL(dm), n, res); //nsp, distance_hadamard(res, n); UNPROTECT(1); return(result); } // speed up some code for NJ void out(double *d, double *r, int *n, int *k, int *l){ int i, j; double res, tmp; k[0]=1; l[0]=2; res = d[1] - r[0] - r[1]; for(i = 0; i < (*n-1); i++){ for(j = i+1; j < (*n); j++){ tmp = d[i*(*n)+j] - r[i] - r[j]; if(tmp%") importFrom(Matrix, Matrix, sparseMatrix, spMatrix, crossprod, solve) #, %*% importFrom(fastmatch, fmatch) #importFrom(nnls, nnls) #importFrom(parallel, mclapply, detectCores) import(parallel) importFrom(quadprog, solve.QP, solve.QP.compact) #importFrom(rgl, open3d, segments3d, spheres3d, rgl.texts) importFrom(stats, AIC, BIC, logLik, reorder, update, optim, optimize, constrOptim, na.omit, cophenetic, hclust, as.dist, pchisq, reshape, qgamma, pgamma, model.matrix, aggregate, lm.fit, xtabs, quantile, sd) importFrom(graphics, plot.new, plot.window, text, par, plot, abline, strwidth, axis, title, segments, points, image, matplot, legend, hist) importFrom(utils, read.table, download.file, citation, stack, installed.packages, write.table, combn) importFrom(grDevices, rgb, rainbow, adjustcolor, col2rgb) importFrom(methods, hasArg) #importFrom(Rcpp, evalCpp) #importMethodsFrom(Biostrings,as.matrix) #importFrom(Biostrings,DNAMultipleAlignment) #importFrom(Biostrings,RNAMultipleAlignment) #importFrom(Biostrings,AAMultipleAlignment) export(pml, optim.pml, pml.control, parsimony, optim.parsimony, pratchet, NJ, UNJ, PNJ, phyDat, cbind.phyDat, read.phyDat, write.phyDat, as.Matrix, as.phyDat, as.splits, as.networx, dfactorial, ldfactorial, hadamard, nni, allSitePattern, allSplits, allCircularSplits, fhm, distanceHadamard, treedist, sankoff, fitch, h2st, h4st, dist.logDet, dist.hamming, dist.ml, dist.p, upgma, wpgma, write.nexus.splits, read.nexus.splits, read.nexus.networx, write.nexus.networx, write.splits, pmlPart, pmlCluster, pmlMix, pmlPen, read.aa, allTrees, designTree, designSplits, nnls.tree, nnls.splits, nnls.phylo, nnls.networx, neighborNet, pmlPart2multiPhylo, splitsNetwork, simSeq, SH.test, bootstrap.pml, bootstrap.phyDat, RF.dist, wRF.dist, KF.dist, path.dist, sprdist, SPR.dist, rNNI, rSPR, plotBS, Ancestors, Descendants, mrca.phylo, Children, Siblings, pace, modelTest, lento, compatible, acgt2ry, ancestral.pars, ancestral.pml, CI, RI, getClans, getSlices, getClips, getDiversity, midpoint, pruneTree, acctran, getRoot, plotAnc, consensusNet, bab, random.addition, diversity, baseFreq, densiTree, superTree, coalSpeciesTree, pml.fit, pml.init, pml.free, edQt, lli, cladePar, addConfidences, createLabel, countCycles, SOWH.test, plot.networx, presenceAbsence, as.networx.splits, addTrivialSplits, phyDat2alignment, AICc, readDist, writeDist, discrete.gamma, matchSplits, distinct.splits, c.splits, dna2codon, codon2dna, removeUndeterminedSites, phyDat2MultipleAlignment, delta.score, maxCladeCred, as.MultipleAlignment) S3method('[', splits) S3method(addConfidences, phylo) S3method(addConfidences, splits) S3method(addConfidences, networx) S3method(AICc, pml) S3method(as.character, phyDat) S3method(as.data.frame, phyDat) S3method(anova, pml) S3method(BIC, pml) S3method(c, phyDat) S3method(c, splits) S3method(cbind, phyDat) S3method(unique, phyDat) S3method(as.matrix, splits) S3method(as.Matrix, splits) S3method(as.networx, splits) S3method(as.networx, phylo) #S3method(as.igraph, networx) S3method(as.phyDat, DNAbin) S3method(as.phyDat, alignment) S3method(as.phyDat, character) S3method(as.phyDat, data.frame) S3method(as.phyDat, factor) S3method(as.phyDat, matrix) S3method(as.phyDat, MultipleAlignment) S3method(as.MultipleAlignment, phyDat) S3method(as.AAbin, phyDat) S3method(as.DNAbin, phyDat) S3method(as.phylo, splits) S3method(as.splits, phylo) S3method(as.splits, multiPhylo) S3method(as.splits, prop.part) S3method(as.splits, networx) S3method(as.prop.part, splits) S3method(cophenetic, networx) S3method(cophenetic, splits) S3method(image, phyDat) S3method(logLik, pml) S3method(logLik, pmlPart) S3method(logLik, pmlMix) S3method(plot, pml) S3method(plot, pmlPart) S3method(plot, networx) S3method(plot, pmlCluster) S3method(print, phyDat) S3method(print, pml) S3method(print, pmlMix) S3method(print, pmlPart) S3method(print, splits) S3method(print, summary.clanistics) S3method(print, SOWH) S3method(reorder, networx) #S3method(reorder, splits) S3method(simSeq, phylo) S3method(simSeq, pml) S3method(subset, phyDat) S3method(summary, clanistics) S3method(summary, SOWH) S3method(update, pml) S3method(vcov, pml) phangorn/NEWS0000644000175100001440000005247213017337433012621 0ustar hornikusers CHANGES in PHANGORN VERSION 2.1.0 NEW FEATURES o new functions to compute the (approximate) SPR distance (sprdist, SPR.dist) contributed by Leonardo de Oliveira Martins. o super tree methods based on NNI and SPR distances BUG FIXES o fixed bug in KF.dist OTHER CHANGES o improvements to as.networx. It often now produces networks with less edges resulting in much nicer plots o plot.networx does take a different layout algorithm o as.data.frame.phyDat and as.character.phyDat return amino acids now in upper cases o more unit tests o improved cbind.phyDat, faster and more flexible o phangorn now requires ape 4.0 o phangorn now imports Rcpp, but not nnls any more CHANGES in PHANGORN VERSION 2.0.4 NEW FEATURES o new weighted Robinson-Foulds (1979) distance wRF, suggested by Michelle Kendall and normalized Robinson-Foulds suggested by Sereina Rutschmann o codon model "YN98" can be called directly, improved documentation o bootstrap.phyDat got an new argument jumble, which randomizes input order of sequences OTHER CHANGES o more unit tests CHANGES in PHANGORN VERSION 2.0.3 NEW FEATURES o new function maxCladeCred computing the maximum clade credibility tree from a sample of trees o function read.nexus.networx and write.nexus.networx to import / export phylogenetic networx to SplitsTree o function as.AAbin.phyDat to exchange data with the new AAbin class from ape o likelihood ratchet (Vos 2003) topology optimisation in optim.pml o with KF.dist (Kuhner & Felsenstein) and path.dist can be used like RF.dist BUG FIXES o improvements to optim.pml to avoid numerical problems, can now handle much more taxa (still experimantal) OTHER CHANGES o mrca.phylo can be used like mrca from ape (faster for large trees) o individual splits can be colored in lento plots (suggested by France Thouzé) o plot.networx now (silently) returns a networx object with some of the graphics parameters, more plot options o lots of small changes to make handling and identifying splits, edges in trees and networks easier o plotBS has a new argument frame and arguments BStrees can be empty o new vignette IntertwiningTreesAndNetworks CHANGES in PHANGORN VERSION 2.0.2 OTHER CHANGES o phangorn now suggests only the Biostrings and not depends on it o some improvements to bab (branch and bound), may be faster CHANGES in PHANGORN VERSION 2.0.0 NEW FEATURES o as.phyDat.MultipleAlignment to excange data with Biostrings o dist.ml can now compute distances using a discrete gamma model and the "F81" model. o optim.pml got a new rearrangement argument and can now use a stochastic rearrangements similar to Nguyen et al. (2015) BUG FIXES o plotBS may double counted edges when rooted trees were in the sample OTHER CHANGES o optim.pml uses more C code and should be more robust o more unit tests o baseFreq got additional parameter 'all' similar to base.freq in ape o lots of little improvements in the manual and help files o modelTest now also shows AIC weights and model="all" will test all available models CHANGES in PHANGORN VERSION 1.99.14 NEW FEATURES o phyDat2alignment to exports files to seqinr o readDist, writeDist to import / export distance matrices o cophenetic distance function for splits and networx o added unit tests BUG FIXES o as.splits.networx did not work properly for 4 taxa (reported by Laurélène Faye) o RF.dist returned sometimes wrong values (reported by Andres Dajles) o plotBS did sometimes not work if the tree had no edge lengths o plotBS did not work propoerly if input trees were rooted (reported by Quynh Quach) o plot.networx ignored cex argument in "2D" plots o Siblings ignored include.self argument if node is a vector OTHER CHANGES o plotBS got an additional argument p to plot only support values greater than p o pml and optim.pml now uses more C-code (and is a bit faster) o defaults in modelTest changed o discrete.gamma is now exported in the NAMESPACE CHANGES in PHANGORN VERSION 1.99-13 OTHER CHANGES o improved importing and conversion of data o improved stability of pml and optim.pml CHANGES in PHANGORN VERSION 1.99-12 NEW FEATURES o added neighborNet algorithm (Bryant and Moulton 2004) very experimental at the moment BUG FIXES o plotBS was not working correctly if bootstraped trees are in compressed form (bug report by Tobias Müller) OTHER CHANGES o many splits and networx methods have been improved and a vignette was added o phangorn now suggests only the rgl and not depends on it to avoid problems on different platforms (suggestion by Matt Pennell) o new package dependencies knitr for html vignettes and nnls CHANGES in PHANGORN VERSION 1.99-10 BUG FIXES o reorder.networx may not work as expected o Gamma model was not working properly in simSeq.pml CHANGES in PHANGORN VERSION 1.99-9 BUG FIXES o bug fixes for clang environment o midpoint takes care of node labels CHANGES in PHANGORN VERSION 1.99-8 NEW FEATURES o pmlPart got an argument rooted to handle rooted trees o simSeq is now a generic function. This simplifies the construcion of parametric bootstrap test o SOWH.test (very experimental) o as.networx and plot.networx improved considerably (often generate networks less edges) and planar graphs are now plotted nicely BUG FIXES o fixed some bugs in ancestral.pars o amino acid model "Blosum62" was not working OTHER CHANGES o improvements to read.nexus.splits, write.nexus.splits to be more consistant with splitstree o splitsNetwork got an additional argument splits o help for consensusNet, as.splits, as.networx have been reorganised and improved o treedist is much faster for larger trees o several changes to keep R CMD check happy o a development version phangorn is now available on github https://github.com/KlausVigo/phangorn.git CHANGES in PHANGORN VERSION 1.99-6 NEW FEATURES o cladePar helps coloring trees o treedist is faster for larger trees, better documentation and examples BUG FIXES o the plot of consensusNet shows now the proper bootstrap values OTHER CHANGES o phangorn does not depend only suggest rgl (should build on OS X now) o default rearrangement for parsimony is now "SPR" CHANGES in PHANGORN VERSION 1.99-5 NEW FEATURES o RF.dist works also on "multiPhylo" objects and is quite fast o optim.pml can now handle NNI tree arrangements for rooted trees, still experimental but useful for dating etc. BUG FIXES o rNNI did return sometimes trees without tip labels o SH.test did not work for pmlCluster objects o df for rooted rooted/ultrametric trees are correctly computed OTHER CHANGES o lots of internal code C-code changed o exports of some of the internal ML function, this should speed up in future other packages e.g. the colescentMCMC package, which use them considerably (interface may changes in the future) o registered C routines CHANGES in PHANGORN VERSION 1.99-0 NEW FEATURES o new function dist.p to estimate pairwise polymorphism p-distances from DNA sequences BUG FIXES o as.data.frame.phyDat returned only site patterns and so did write.phyDat for nexus files o some of the recently introduced (1.7-4) amino acid models were not known by all functions and contained NAs OTHER CHANGES o changed package imports and depends structure to aviod error messages, thanks to Brian Ripley o a lot of the internal C-code has changed CHANGES in PHANGORN VERSION 1.7-4 NEW FEATURES o densiTree plots are available now o new species tree and super tree methods o more amino acid models BUG FIXES o phangorn now depends on rgl instead of suggests rgl, rgl wants to be loaded before igraph, otherwise a compiling error on some platforms occured! o fixed a bug that sometimes caused in pratched to crash o fixed a bug when using negative indices in subset.phyDat o the search heuristic SPR in optim.parsimony evaluates now more trees and is more likely to find better ones OTHER CHANGES o underlying C-code for several functions has changed. less memory reallocations and potentially time savings hopefully I included not too many bugs o optimising edge length changed from Jacobi to Gauss-Seidel method and will hopefully be more robust in the long term! o Descendants is much faster for option type="all" o plotAnc gives user more control and produces nicer plots CHANGES in PHANGORN VERSION 1.7-1 NEW FEATURES o pmlPart got additional argument model (request from Santiago Claramunt) BUG FIXES o pmlPart should be more robust OTHER CHANGES o started reorganising the code o underlying code of several parsimony functions has changed and these are now considerably faster o some examples are changed to allow faster checking on CRAN CHANGES in PHANGORN VERSION 1.6-5 NEW FEATURES o dist.hamming handles ambigious states now as dist.ml (request from Arne Mooers) BUG FIXES o phangorn links properly to ape CHANGES in PHANGORN VERSION 1.6-3 NEW FEATURES o optim.parsimony has a new search heuristic (SPR) BUG FIXES o changed package to work with igraph >= 0.6 OTHER CHANGES o arguments of pratchet changed CHANGES in PHANGORN VERSION 1.6-0 NEW FEATURES o dist.ml has more options and is faster (ca. 5 times for nucleotides and 20 times for amino acids) BUG FIXES o plotBS did not work properly with ape version 3.0 OTHER CHANGES o vignettes changed for a faster compilation of the package o Ancestors allows a vector of nodes as input o midpoint uses less memory and works for larger trees (10000 of tips) o ancestral.pars gives better formated output CHANGES in PHANGORN VERSION 1.5-1 OTHER CHANGES o several examples changed for a faster compilation of the package CHANGES in PHANGORN VERSION 1.5-0 NEW FEATURES o codon models can be used directly (dn/ds ratio can be computed) o modelTest works now also for amino acids BUG FIXES o the code to compute RI and CI changed and should be more robust OTHER CHANGES o package parallel is used instead of multicore o vignettes, examples, help improved o ChangeLog is called NEWS CHANGES in PHANGORN VERSION 1.4-1 NEW FEATURES o parsimony branch-and-bould algorithms bab (so far pretty slow and memory intensive) o more amino acid models o function nnls.tree to compute non-negative edge weights for a given tree and a distance matrix BUG FIXES o allTrees returns now an integer edge matrix, this could have caused some problems previously o CI and RI now take better care of ambiguous states o dist.ml has default value for amino acids o as.splits.multiPhylo produces more sensible bipartitions and so lento and consensusNet produce more useful plots (thanks to Emmanuel Paradis) OTHER CHANGES o several changes to the networx classes and methods o modelTest now also returns the function calls of the estimated models, which can be used in downstream analyses o vignette "Trees" has a few more examples o dist.ml is more general (base frequencies and rate matrix can be supplied) o pml objects are more compact, thanks to the Matrix package o xtable is now a suggested package (needed for vignettes) CHANGES in PHANGORN VERSION 1.4-0 NEW FEATURES o plot.network to plot split networks in 3D (requires rgl) and 2D (still very experimantal) o consensusNet computes consensus networks o Lento plot allows to take multiPhylo objects as input BUG FIXES o CI and RI did not work with only one site pattern present o pratchet returned only one, not all of the best trees found OTHER CHANGES o phangorn now requires the Matrix, igraph and rgl packages o designTree returns a sparse Matrix and this can save a lot of memory o internal code for computing bipartitions is much faster for large trees, and so are several functions depending on it, e.g. RF.dist, treedist, Descendants CHANGES in PHANGORN VERSION 1.3-1 BUG FIXES o the multicore package may failed, if executed in a GUI environment, more error checks included o optim.pml, in rare cases may failed to optimize edge length (should be more robust now) OTHER CHANGES o some changes to keep R CMD check happy o modelTest, pratchet, bootstrap.pml, bootstrap.phyDat got an additional argument multicore option to switch between serial and parallel execution CHANGES in PHANGORN VERSION 1.3-0 NEW FEATURES o acctran to assign edge length to parsimony trees OTHER CHANGES o phangorn can now be cited o additional and improved ancestral reconstructions methods (ACCTRAN, MPR) o new vignette describing ancestral sequence reconstruction CHANGES in PHANGORN VERSION 1.2-0 NEW FEATURES o new function pratchet (parsimony ratchet) o new function midpoint for rooting trees o new function pruneTree to build concensus trees from node labels (e.g. bootstrap values) o multicore support for modelTest BUG FIXES o ancestral.pars sometimes did not show all possible states o the call-attributes did not get proper changed in update.pml and optim.pml OTHER CHANGES o there is now a general help page displayed with '?phangorn' o dist.hamming is faster o getClans, getSlices and getDiverstity can now handle multifurcating trees CHANGES in PHANGORN VERSION 1.1-2 NEW FEATURES o more generic methods for class splits (print, as.matrix) o plotBS can plot now cladograms and phylograms BUG FIXES o read.phyDat sometimes did not work properly for amino acids CHANGES in PHANGORN VERSION 1.1-1 NEW FEATURES o optim.pml allows to optimise rooted trees OTHER CHANGES o description of getClans improved CHANGES in PHANGORN VERSION 1.1-0 NEW FEATURES o Consistency Index (CI) and and Rentention Index (RI) o clanistic tools o new generic function cbind.phyDat o optim.parsimony works now also with the fitch algorithm, faster than the sankoff version BUG FIXES o treedist and RF.dist now check whether trees are binary and try to handle multifurcations (thanks to Jeremy Beaulieu for bug fixes) OTHER CHANGES o second vignette describing some special features o allTrees is faster o trace and pml.control are now more consistent o optim.pml uses less memory and can be faster for data with lots of characters CHANGES in PHANGORN VERSION 1.0-2 BUG FIXES o pml.control did not work properly OTHER CHANGES o pmlCluster, pmlMix and pmlPart gained an attribute control, which controls the outermost loop o some more error checking for pml and parsimony classes (thanks to Emmanuel and Liat) CHANGES in PHANGORN VERSION 1.0-1 NEW FEATURES o ancestral sequence reconstruction (parsimony and likelihood based) o a small convenience function acgt2ry for ry-coding o as.phylo.splits computes a tree from compatible splits BUG FIXES o a small error in pmlCluster was fixed OTHER CHANGES o upgma changed to accommodate change in as.phylo.hclust o lento plots are looking nicer CHANGES IN PHANGORN VERSION 1.0-0 NEW FEATURES o implementation of many nucleotide substitution models (additional general transition models can be defined) o new function modelTest, comparison of different phylogenetic model with AIC or BIC o Lento plot o subset functions for phyDat objects BUG FIXES o an error in pace is fixed OTHER CHANGES o parsimony (fitch and sankoff) can now handle multiPhylo objects o splits structure (which is a list of bipartitions), used by lento and hadamard conjugation o phyDat objects can be more general generated using a contrast matrix CHANGES IN PHANGORN VERSION 0.99-6 NEW FEATURES o pace, extracts the ancestral states of the root of a tree using the sankoff algorithm BUG FIXES o fixed a bug in dist.ml (thanks to Emmanuel) o fixed a bug introduced to SH.test in 0.99-5 OTHER CHANGES o fixed several spelling mistakes in the documentation CHANGES IN PHANGORN VERSION 0.99-5 NEW FEATURES o parallel computing via multicore (so far bootstrap.pml, bootstrap.pml profit under linux) o compute edge weights for parsimony trees BUG FIXES o optim.pml had problems when o as.character converted ?,- wrongly to NA o fitch needed binary trees as input, otherwise pscore is likely to be wrong (returns now a warning) o optim.pml had a problem with identical sequences OTHER CHANGES o optim.parsimony returns now a tree with edge weights o vignette is enhanced, I fixed some spelling mistakes and added some more examples. CHANGES IN PHANGORN VERSION 0.99-4 NEW FEATURES o new generic function unique.phyDat OTHER CHANGES o internal data format phyDat changed and data are stored more memory efficient (optim.pml and friends use less memory and may be faster) CHANGES IN PHANGORN VERSION 0.99-3 BUG FIXES o RF.dist sometimes returned wrong distances o rate parameter is now properly normalized in pml.Part and pmlCluster o simSeq had problems simulating a single character NEW FEATURES o rSPR and rNNI to simulate tree rearrangements CHANGES IN PHANGORN VERSION 0.99-2 NEW FEATURES o bootstrap.pml and bootstrap.phyDat: parametric bootstrap methods o simSeq: A new function to simulate sequence data o read.phyDat: simplifies reading in alignments o SH.test: Shimodaira-Hasegawa test o RF.dist: Robinson-Foulds distance as replacement for treedist (uses less memory and is much faster) BUG FIXES o dist.ml returned wrong variances. o as.character.phyDat, as.data.frame caused an error for alignments with only one site. OTHER CHANGES o added vignette describing how to perform some standard phylogenetic analysis with phangorn. o more functions to convert between different data formats. o NNI tree search is now general possible for partition models (pmlPart, pmlCluster) CHANGES IN PHANGORN VERSION 0.0-5 BUG FIXES o Solved a namespace problem with ape (>=2.2-3). CHANGES IN PHANGORN VERSION 0.0-4 NEW FEATURES o splitsNetwork fits a phylogenetic network using a L1 penalty. (High memory consumption) o pmlPen: A new function to estimate penalized likelihood models for sets of edge weights in mixtures or partition models. BUG FIXES o dist.ml should be more forgiving for different inputs. OTHER CHANGES o a new dataset. CHANGES IN PHANGORN VERSION 0.0-3 NEW FEATURES o amino acid models o several new maximum likelihood models: mixture models (pmlMix), and some model for phylogenomic data partition models (pmlPart), and clustering of partitions / genes (pmlCluster) (still experimental, feed back wellcome) o design matrices for phylogenetic distance methods o added some functions useful for simulations (nni, allTrees) OTHER CHANGES o the data object phyDat changed slightly internally o a new dataset o read.aa to read amino acid data in phylip format based on read.dna from the ape package CHANGES IN PHANGORN VERSION 0.0-2 NEW FEATURES o more generic functions (plot.pml, update.pml) BUG FIXES o the "Fitch" algorithm in parsimony contained a bug OTHER CHANGES o pml has a cleaner interface (less parameter) o new faster parsimony analysis (more compiled C-Code) o added NAMESPACE phangorn/data/0000755000175100001440000000000012761653173013030 5ustar hornikusersphangorn/data/yeast.RData0000644000175100001440000046427412761653173015113 0ustar hornikusersý7zXZi"Þ6!ÏXÌçïþ])TW"änRÊŸãX\°ÆqÅjnçj-&éd¿ ßk´9í¥÷oP6Â6RÈ èRîwÇ yXgÐË!qP×_¯¿?DS—ßÕ†þX¡Òñ]O¤ÐÚB`ã _däRŠÛ¹ajèvRŒíÎã³ÊS µ›«O%Tþ•J|qxK ‰ÀÞNæeÈÌÙ6·J*¿M8»‹%ú VÅ’1}mä :Pž·?6kdFsñäˆ|ípìKwÞéFúéq¾êÌûY}HÇòƧ «²B,ˆ„ìÆÐ>žÉ(:µkµ—PhÅ–-7¼Ù\Z¿¼"Á£Ó@ˆ‘<ÐÀ·ÅY@ë˜HMƒÏç; §„Ž9y¨§ÞÄ"žO¨+qƒt]­I'/T-0ÿUµ²Ë('€2!º+©µlÂíËÿ)p|T±ŸÊî凸²mØP§'PõÚ|hæWãö¤õ|r¸ü4½n¨Œ(Ê¡ò\+Ð15vª8exÔŠÈü’úŠÀgöcà%–-=<^ŸÓÏ‘¥Õ©–ºCŒ_ †x­„=9`MR„9~¹ÿ(7bÃ*V’Ÿ“(åˆt€‘_ì~*pÌÓçcÆ{¿{?âáòKš€ºµƒ¸ÿÌiÄuLbÿ1Bò«%#Á‰……wS¿³zS;ï*©QP{]ÎözVnSs´ùµ{ût¨úóíaS–Ö°qOµ å¨Ön=äS;Ò‰±î|¤¾8ZŽZõo]Ëó\زuR6!Èã&HKÎŽ£›îÑDÿͯ<Õ(ëc6vgšŠ°â€€|MÔB¬ÓÂçÙ‚rÊü‹”Ý©©‰vÑúBo;¨Ê*F7”I*µÌØ€F»«P{$Þ¦¢ýð_§•óÊ>1-›/#û#(¦còå~2w HsvÅ¡2NI\n’ˆbyNzÊüí\ÿ!àU™µúc¢!4sË5\@ÔLØíÿ#õmù®ktÄ^\c0ex„ñŠO4oÜöƒ²¥µDzœvE)<ž ’r“àNæ‹‘&Ë4¶CÃ<=0±·øQe´ußåЊ¶N„#;´Ü¾=ÏÚŽSÒš‰ë’Áóo K–&s2ÂSp7¯‡|bæf/8·ô1™ÿñr_avR„Ÿ¼RBèfdãhCC-‘ Ž7ƒH·í;ñ…ë1r-·ÊG¢(Û Ó¥ý¥« ÆZa7¾m¦¢Ü¥VFz[¹¯$°'ú„f³:t˜-éoÜÈg,Ì‚TYÑЃšg{Ü·ÔëŒ'ó´ŒèAwF:çM5âjk}Îã†AY $)uÔŒ7,Ðú¶–÷-ÙµÙ1™PS#²@·A=ž pÿ 锎`"ZYâ‡[G$÷}48_}·Í ©Y@ØSBy° °or{€µ?Ñ•žv;¤ÕbN”{—¶ &5‡æ±¦å|e¦âòKg N(R°. °€Ñ\ñ¨bÐ2g–Z}Ûÿ6-–ç^5705ä¶Ò´¶Õ¤~ë}< ôu©3aÂœz´×ó¬bpúƒ}j¾—’ˆ8ý¡ ~¾Êccî¿îF"¯¾ë#Î>ÌŒ¸wÑ-ñ¥ÊoØeÅúC'OìTµ˜ 4' EmGekÕì¦f‡¯ÜS\U_³ÀO¸I›:inuĺŰ_u‹U3 tb­vRIà™p³ü™²ëÍ}kÒBQöÖ°Ô n´JÚµ]>ºë©‚ð2üI¡ËÉÓ‘~ÆžŠ£ìr¦Êc±†ŽŠ a®&Ês¼›«y]a˜¿ó/’*œ P£|DÂ𙯩)æAÙÏå¢Hu¤½Zìº:ØoZ¨Ùa?³&ØeszùЄ;¿°c? Û¸ƒ:Æ*:úûó Ž‘ióíïf$EvUšIoÔ¨­;¹'”¢‹ÀÖ ÙR@G*Ö©ã¿BÚÄ¢ÂóJSœMÛÓš4Á—qŸõä«é+£nö}£Ö0ÌÞÒåÕ:1ž…N8¨ûñÍí-çšæR çF«¼[l ÖôZ•yû"…²hjðYê/;te÷–md'~ö¼·ÂŸü€¬ò+8-³DR¦m¸ùz ^ ©©°Ý:M|Œ:q#1’ GÔÖ IEÁ™–r†c·4‹oÙÉ°’Í ŸøÏ¿W–­ÓæF¼ÙM5ÂKƒ2˜]9NsCÙ0¦V‰wÙ7“ ®pVUR° ¹@ù¦"®1™;-ÜÍCî™aKú¤B@XÙ^T¤|oZq§=ö‡5™NyÛ+¶Ÿéù¼·n½ ·­l2¦˜X,E>ó÷#ñjø¾ªæ-ͲהpSFÂLKšH6µVÇ×1{«¡Mßek]ùšÓ8¹–6XN­•œ A¤Üû]ùV¾d³øg.*÷¥0·prï#g'¢l‘§Ê ÏscHç½æ6çnÂ%Ó³›Ó9Ôj[5ª¿¤/Ì÷ô`Î'Rj,¡À2ý¼XGŠÒéÂ\Ù7ùEŧÐg‚Œ&ºÌžŸýðk‘ ?xᥠ7~*3˜l~Õæ‡gªÚ!è„Ó <àÔlV’÷·¼ž(•ÕE¦°>ø¢ö­0wdûÃêäíµäk±‰$‘DýÕ,»â€¾ÇGî4Ù çþ[€ƒÅ¼Ô;4.‡Ò4 V¾úãªYI¹Z®o·‘#ZÁUsÁÄH0EÇK翃êË®«¹„f™‹1Ít †Òß©‹t6ëîØ&í®jÑl{.ó$u3r;àgËè« W>ĸç[­4RǺÌ·@lq'=ß<ÅàÈãmÊ J¤ÇïeUùµáðÒoZw™Ûë5Þä·/¢D3á“R«†óÌçg*õvMòÌ©ü:»F 9 | fžuïu+\bQ lWý¢GÉ–š«xèY“ù> 0·µßủÍ_EÅŽÑw®¦¤¦;+P,ŠÑ-ìyû-Àj¸Pwj¤ê‘|µÐÓX5d\áxˆ³4nÌ9pÞNÖﹸq'´ Xw‚õ „jøLllà àkð¬QnÞýúwáŸ2§ˆesEUh\ƒzØ3j¾d8‹«( *xüjÄŒ_aË^6#à$é‘݉Oý úcÃ;Yp>ñŒ¯e÷t¶·ú«½%?H{>$|ˆ“£mßG®m*ë“ǤþÍe¯ÈÈ_*mÆûD¢³æKC¢êqë a½&J%OçvnÏlš®ŒÄ9A,‹E›‚#9ŠœSžþÅ‚æ+*íÈæ\7ì‹ä,¼¾…ÇX«w' ϶°Ú-„oÊ}"îÁ©6.æ¨*]¬G"o ö"pÿ|"ƒ±®µ¡ë”®jj1€ybVLz³;GaÃB¿,¢3õyÙë9 ÏUUg¦Xè~ïˆÕe7ÖÝläã Âøê,ZCQëi¡Î5÷VìµäÄ4´nÉ›×;ˆ>¶«„Ò{¿uDéÃ5–Z#ë9ë-´$ç+ÚÒÌzÊmS=üjS®–C`¦:ÀZø˜dÖ¾^ÀåÊe%8#U) "·ñèìEÒMÔ:üS v_åÕ! 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to be element of class phyDat") l = length(x) contrast <- attr(x, "contrast") nc <- as.integer(attr(x, "nc")) con = rowSums(contrast > 0) < 2 if (exclude == "all") { index = con[x[[1]]] for (i in 2:l) index = index & con[x[[i]]] index = which(index) x = subset(x, , index) } weight <- attr(x, "weight") d = numeric((l * (l - 1))/2) if(exclude == "pairwise"){ k=1 W <- numeric(l*(l-1)/2) for (i in 1:(l - 1)) { tmp = con[x[[i]]] for (j in (i + 1):l) { W[k] = sum(weight[tmp & con[ x[[j]] ] ]) k = k + 1 } } } if(nc > 31){ # contrast <- attr(x, "contrast") k = 1 for (i in 1:(l - 1)) { X = contrast[x[[i]], , drop = FALSE] for (j in (i + 1):l) { d[k] = sum(weight * (rowSums(X * contrast[x[[j]], , drop = FALSE]) == 0)) k = k + 1 } } } # end if else{ nr <- attr(x, "nr") if(exclude == "pairwise")ind <- which(con[unlist(x)]==FALSE) x <- prepareDataFitch(x) if(exclude == "pairwise")x[ind] <- as.integer(2L^nc -1L) res <- .C("distHamming", as.integer(x), as.double(weight), as.integer(nr), as.integer(l), as.double(d), PACKAGE = "phangorn") d <- res[[5]] } if (ratio){ if(exclude == "pairwise") d = d/W else d = d/sum(weight) } attr(d, "Size") <- l if (is.list(x)) attr(d, "Labels") <- names(x) else attr(d, "Labels") <- colnames(x) attr(d, "Diag") <- FALSE attr(d, "Upper") <- FALSE attr(d, "call") <- match.call() attr(d, "method") <- "hamming" class(d) <- "dist" return(d) } dist.ml <- function (x, model = "JC69", exclude = "none", bf = NULL, Q = NULL, k=1L, shape=1, ...) { if (!inherits(x,"phyDat")) stop("x has to be element of class phyDat") l = length(x) d = numeric((l * (l - 1))/2) v = numeric((l * (l - 1))/2) contrast <- attr(x, "contrast") con = rowSums(contrast > 0) < 2 if (exclude == "all") { index = con[x[[1]]] for (i in 2:l) index = index & con[x[[i]]] index = which(index) x = subset(x, , index) } nc <- as.integer(attr(x, "nc")) nr <- as.integer(attr(x, "nr")) # model <- match.arg(model, c("JC69", "WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24")) model <- match.arg(model, c("JC69", "F81", .aamodels)) # if (!is.na(match(model, c("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24")))) if (!is.na(match(model, .aamodels))) getModelAA(model, bf = is.null(bf), Q = is.null(Q)) if(is.null(bf) && model=="F81") bf <- baseFreq(x) if (is.null(bf)) bf <- rep(1/nc, nc) if (is.null(Q)) Q <- rep(1, (nc - 1) * nc/2L) bf = as.double(bf) eig <- edQt(Q = Q, bf = bf) pos = 1 k = as.integer(k) w = as.double(w <- rep(1/k, k)) g = as.double(discrete.gamma(shape,k)) fun <- function(s) -(nc - 1)/nc * log(1 - nc/(nc - 1) * s) eps <- (nc - 1)/nc n = as.integer(dim(contrast)[1]) ind1 = rep(1:n, n:1) ind2 = unlist(lapply(n:1, function(x) seq_len(x) + n - x)) li <- as.integer(length(ind1)) weight = as.double(attr(x, "weight")) ll.0 = as.double(weight * 0) if (exclude == "pairwise") { index = con[ind1] & con[ind2] index = which(!index) } tmp = (contrast %*% eig[[2]])[ind1, ] * (contrast %*% (t(eig[[3]]) * bf))[ind2, ] tmp2 = vector("list", k) # wdiag = .Call("PWI", as.integer(1:n), as.integer(1:n), as.integer(n), # as.integer(n), rep(1, n), as.integer(li), PACKAGE = "phangorn") # wdiag = which(wdiag > 0) wshared <- which(rowSums(contrast[ind1, ] * contrast[ind2, ]) > 0) tmp2 = vector("list", k) for (i in 1:(l - 1)) { for (j in (i + 1):l) { w0 = .Call("PWI", as.integer(x[[i]]), as.integer(x[[j]]), nr, n, weight, li, PACKAGE = "phangorn") if (exclude == "pairwise") w0[index] = 0.0 ind = w0 > 0 old.el <- 1 - (sum(w0[wshared])/sum(w0)) if (old.el > eps) old.el <- 10 else old.el <- fun(old.el) # sind = sum(ind) # browser() for(lk in 1:k) tmp2[[lk]] <- tmp[ind, , drop = FALSE] # FS0 verwenden!!! res <- .Call("FS5", eig, nc, as.double(old.el), w, g, tmp2, as.integer(k), as.integer(sum(ind)), w0[ind], ll.0, PACKAGE = "phangorn") #bf, d[pos] <- res[1] # res[[1]] v[pos] <- res[2] # res[[2]] pos = pos + 1 } } attr(d, "Size") <- l if (is.list(x)) attr(d, "Labels") <- names(x) else attr(d, "Labels") <- colnames(x) attr(d, "Diag") <- FALSE attr(d, "Upper") <- FALSE attr(d, "call") <- match.call() attr(d, "variance") <- v class(d) <- "dist" return(d) } dist.logDet = function (x) { if (!inherits(x,"phyDat")) stop("x has to be element of class phyDat") weight <- attr(x, "weight") contrast <- attr(x, 'contrast') r <- attr(x, "nc") l = length(x) d = numeric((l * (l - 1))/2) k = 1 for (i in 1:(l - 1)) { Xi = weight * contrast[x[[i]], , drop=FALSE] for (j in (i + 1):l) { tmp = crossprod(Xi, contrast[x[[j]], , drop=FALSE]) class(tmp) = "matrix" z = determinant.matrix(tmp, logarithm=TRUE) res = z$sign*z$modulus if (is.nan(res)) { d[k] = 10 } else d[k] = (-res + sum(log(rowSums(tmp) * colSums(tmp)))/2)/r k = k + 1 } } attr(d, "Size") <- l if (is.list(x)) attr(d, "Labels") <- names(x) else attr(d, "Labels") <- colnames(x) attr(d, "Diag") <- FALSE attr(d, "Upper") <- FALSE attr(d, "call") <- match.call() attr(d, "method") <- "logDet" class(d) <- "dist" return(d) } readDist <- function(file){ #, format="phylip" tmp <- read.table(file, skip=1, stringsAsFactors = FALSE) labels = tmp[,1] dm <- as.matrix(tmp[,-1]) dimnames(dm)=list(labels, labels) as.dist(dm) } writeDist <- function(dm, file=""){ # , format="phylip" dm <- as.matrix(dm) cat(ncol(dm), "\n", file=file) write.table(dm, file, append=TRUE, quote=FALSE, col.names=FALSE) } phangorn/R/clanistic.R0000644000175100001440000002775212761653173014431 0ustar hornikusers########################################################################################### getClans = function (tree) { if (is.rooted(tree)) tree = unroot(tree) bp = bip(tree) nTips = length(tree$tip.label) root = nTips + 1 bp[root] = NULL X = matrix(0, length(bp) - nTips, nTips) k = 1 nl = NULL if (!is.null(tree$node.label)) { nl = c(rep("-1", nTips), rep("-1", nTips), tree$node.label[-1], tree$node.label[-1]) } if(root<=length(bp)){ for (i in root:length(bp)) { X[k, bp[[i]]] = 1 k = k + 1 } } res <- rbind(diag(nTips), 1 - diag(nTips), X, 1 - X) colnames(res) <- tree$tip.label if (!is.null(nl)) rownames(res) = nl res } getSlices <- function(tree){ nTips = length(tree$tip.label) clans = getClans(tree) m = dim(clans)[1] X = tcrossprod(clans) z = rowSums(clans) Z1 = matrix(z,m,m) Z2 = t(Z1) Z = matrix(0,m,m) Z[Z1<=Z2] = Z1[Z1<=Z2] Z[Z20,arr.ind=TRUE) l = dim(index)[1] nSlices = 2 * nTips^2 -10 * nTips + 12 result = matrix(0, nSlices, nTips) strClan = do.call("paste", c(as.data.frame(clans), sep = "")) k=1 for(i in 1:l){ tmp1 = as.numeric((clans[index[i,1],] + clans[index[i,2],])==2) tmp = paste(tmp1,sep="",collapse="") if(is.na(match(tmp,strClan))){ result[k,] = tmp1 k=k+1 } } if(kmin(ind) ) break() within = max(dm[ind, ind]) between = min(dm[ind, -ind]) if (within < between) { res = numeric(nTips) res[ind] = 1L result = rbind(result, res) } } } dimnames(result) = list(NULL, tips) if (all) return(result) ind = which.max(rowSums(result)) result[ind, ] } shannon <- function (x, norm=TRUE) { p = x/sum(x) ShD = -sum(p * log10(p)) if(norm){ if (sum(x) == 1) return(0) ShD = ShD/log10(sum(x)) } ShD } shannon2 <- function (x, norm=TRUE) { p = x/sum(x) ShD = -sum(p * log(p)) if(norm){ if (sum(x) == 1) return(0) ShD = ShD/log(sum(x)) } ShD } getE = function (tree, x, clans = NULL, norm = TRUE) { if (is.rooted(tree)) tree = unroot(tree) if (is.null(clans)) clans = getClans(tree) labels = tree$tip.label x = x[labels] result = rep(NA, 12) names(result) = c("E* tree", "# natives", "# intruder", "# unknown", "E* clan", "# intruder", "# unknown", "E* slice", "# intruder", "# unknown", "bs 1", "bs 2") result[2] = sum(x == 1) result[3] = sum(x == 2) result[4] = sum(x == 3) if (result[2] == 0 || result[3] == 0) { if (result[2] > 1) return(list(result, labels)) else return(list(result, integer(0))) } LHG = E_Intruder(clans, x) d = dim(LHG)[1] if (d == 1) { result[1] = 0 if (!is.null(tree$node.label)) result[11] = as.numeric(rownames(LHG)) return(list(result, labels[LHG == 0])) } intr = drop(LHG %*% as.numeric(x == 2)) result[1] = shannon2(intr, norm = norm) o <- order(intr, decreasing = TRUE) if (!is.null(tree$node.label)) result[11:12] = as.numeric(rownames(LHG)[o[c(1, 2)]]) ind = which(LHG[o[1], ] == 1) result[6] = sum(x[-ind] == 2) result[7] = sum(x[-ind] == 3) if (length(x[-ind]) < 4) return(list(result, NULL)) result[5] = shannon2(intr[-o[1]], norm = norm) ind2 = c(which(LHG[o[1], ] == 1), which(LHG[o[2], ] == 1)) spl = structure(list(which(colSums(LHG)==0)), labels=labels, weights=1) class(spl)="splits" if (d == 2) { return(list(result, spl)) } result[9] = sum(x[-ind2] == 2) result[10] = sum(x[-ind2] == 3) if (length(x[-ind2]) < 4){ return(list(result, spl)) } result[8] = shannon2(intr[-c(o[1], o[2])], norm = norm) return(list(result, spl)) } E_Intruder <- function (clans, x) { cp = drop(clans %*% as.numeric(x == 1)) ci = drop(clans %*% as.numeric(x == 2)) homo = which(cp == 0 & ci > 0) l = length(homo) if (l > 0) { HG = clans[homo, , drop = FALSE] lhg = rep(TRUE, l) rsh = rowSums(HG) Z = tcrossprod(HG)>0 Z = Z * rsh zmax = apply(Z,2,max) lhg = !(zmax > rsh) LHG = HG[lhg, , drop = FALSE] return(LHG) } return(NULL) } E_Intruder_2 <- function (clans, x, native=NULL) { contr = attr(x, "contr") d = dim(contr) if(d[1]>d[2])contr[(d[2]+1):d[1],]=0 cp = clans %*% contr[as.numeric(x),] homo = which(rowSums(cp > 0) == 1) l = length(homo) if (l > 0) { HG = clans[homo, , drop = FALSE] lhg = rep(TRUE, l) rsh = rowSums(HG) Z = tcrossprod(HG)>0 Z = Z * rsh zmax = apply(Z,2,max) lhg = !(zmax > rsh) LHG = HG[lhg, , drop = FALSE] return(LHG) } return(NULL) } getDiv <- function(tree, x, native=NULL){ clans = getClans(tree) labels = tree$tip.label x = subset(x, labels) LHG = E_Intruder_2(clans, subset(x,,1)) if(!is.null(native)){ ll = match(native, attr(x, "allLevels")) ind = (as.numeric(x) %in% ll) } if(!is.null(native)){ rs = rowSums(clans) intr = clans %*% ind clans = clans[intr==0,] d = which.max(rs[intr==0]) tree2 = drop.tip(tree, tip=labels[which(clans[d, ]==1)]) } else tree2=NULL list(c(shannon(rowSums(LHG)), summary(factor(attr(x, "allLevels"))[as.numeric(subset(x,,1))]), parsimony(tree, x)), tree2 ) } getDiversity <- function (tree, x, norm = TRUE, var.names = NULL, labels="new") { k = 1 if(inherits(tree,"multiPhylo")) k = length(tree) l = attr(x, "nr") tmp = matrix(0, k * l, 12) tnam = 1 if (inherits(tree,"multiPhylo")) { tnam = names(tree) if (is.null(tnam)) tnam = 1:length(tree) } if(is.null(var.names)) var.names = 1:l PM = data.frame("t1", "a", stringsAsFactors = FALSE) colnames(PM) = c("Tree", "Var") PM = PM[FALSE,] PM[1 :(k*l), ] = NA perfect = names(x) L = vector("list",k*l) m = 1 o = 1 ok= 0 for (i in 1:k) { if (inherits(tree,"multiPhylo")) tmptree = tree[[i]] else tmptree = tree if (is.rooted(tmptree)) tmptree = unroot(tmptree) clans = getClans(tmptree) for (j in 1:l) { TMP = getE(tmptree, getRows(x, j), clans, norm = norm) tmp[m, ] = TMP[[1]] L[[m]] = TMP[[2]] # if class =splits else NULL PM[m, 1] = tnam[i] PM[m, 2] = var.names[j] m = m + 1 } } tnam = rep(tnam, each = l) dnam = var.names dnam = rep(dnam, k) pscore = as.numeric(sankoff(tree, x, site = "site")) res = data.frame(tnam, dnam, tmp, pscore) if(labels=="old")names(res) = c("tree", "variable", "E tree", "# natives", "# intruder", "# unknown", "E clan", "# intruder", "# unknown", "E slice", "# intruder", "# unknown", "bs 1", "bs 2", "p-score") else{ names(res) = c("tree", "variable", "E clan", "# natives", "# intruder", "# unknown", "E slice", "# intruder", "# unknown", "E melange", "# intruder", "# unknown", "bs 1", "bs 2", "p-score") warning("The variable names have changed") } attr(res, "Perfect") = L class(res) = c("clanistics", "data.frame") res } summary.clanistics <- function(object, ...){ res <- matrix(FALSE, nrow(object), 5) res[,1] = object[,4]>0 & object[,"p-score"]==0 # "natives" res[,2] = object[,5]>0 & object[,"p-score"]==0 # "intruder" res[,3] = object[,"p-score"]==1 res[,4] = ( (object[,"p-score"]==2) & (object[,7]==0) & (!is.na(object[,7])) ) | ( (object[,"p-score"]==2) & (object[,4]==2) & (is.na(object[,7])) ) res[,5] = object[,"p-score"]>=2 & (object[,7]>0) & (!is.na(object[,7])) res[] = as.numeric(res) tmp = data.frame(factor(object[,"variable"]), res) colnames(tmp) = c("Variable", "Natives_only", "Intruder_only", "Clan", "Slice", "Melange") # colnames(res) = c("Natives only", "Intruder only", "Clan", "Melange") class(tmp) <- c("summary.clanistics", "data.frame") tmp } print.summary.clanistics <- function(x, ...){ print(aggregate(x[,-1], list(Variable=x[,1]), sum), ...) } compareSplits <- function(res, nam1, nam2){ wide <- reshape(res[, c("tree", "E tree", "variable")], v.names="E tree", idvar="tree", timevar="variable", direction="wide") wideI <- reshape(res[, c("tree", "# natives", "variable")], v.names="# natives", idvar="tree", timevar="variable", direction="wide") for(i in 2:dim(wide)[2])colnames(wide)[i] = strsplit(colnames(wide)[i],"E tree.")[[1]][2] for(i in 2:dim(wide)[2])colnames(wideI)[i] = strsplit(colnames(wideI)[i],"# natives.")[[1]][2] ntrees = wide[,1] splits = attr(res, "Perfect") dat = attr(attr(res, "Perfect"), "data") res = matrix(NA, length(ntrees), length(nam1)*length(nam2)) for(m in 1:ntrees){ k=1 trnam=ntrees[m] for(i in nam1){ E1 = wide[m, i] for(j in nam2){ E2 = wide[m, j] if(!is.na(E1) & !is.na(E2)){ if(E1 == E2){ # if(E1 == 0 & E2 == 0){ if( (wideI[m, i] >0) & (wideI[m, j]) >0){ ind1 = which(dat[,1]==trnam & dat[,2]==i) sp1 = splits[[ind1]] ind2 = which(dat[,1]==trnam & dat[,2]==j) sp2 = splits[[ind2]] if(length(ind1)>0 & length(ind2)>0 )res[m, k] = drop(compatible3(sp1, sp2)) } } } k=k+1 } } } res } diversity <- function(tree, X){ # from kknn contr.dummy <- function (n, contrasts = TRUE) { if (length(n) <= 1) { if (is.numeric(n) && length(n) == 1 && n > 1) levels <- 1:n else stop("contrasts are not defined for 0 degrees of freedom") } else levels <- n lenglev <- length(levels) cont <- array(0, c(lenglev, lenglev), list(levels, levels)) cont[col(cont) == row(cont)] <- 1 cont } l = dim(X)[2] m <- ifelse(inherits(tree,"multiPhylo"), length(tree), 1) contr = as.list(rep("contr.dummy", l)) names(contr) = names(X) tmp = model.matrix(~.-1, X, contrast=contr) tmp1 <- phyDat.default(tmp, levels=c(1,0), compress = FALSE) attr(tmp1, "varnames") = colnames(tmp) fd = sankoff(tree,tmp1,site = "site") fd = matrix(fd, ncol=m) if(m>1){ if(is.null(names(tree))) tnames <- paste("tree", 1:m, sep=".") else tnames <- names(tree) } else tnames = "tree" dimnames(fd) = list(colnames(tmp), tnames) res = stack(data.frame(fd)) if(m>1)nt = rep(sapply(tree, function(x)length(x$tip.label)), each=dim(fd)[1]) else nt = rep(length(tree$tip.label), each=dim(fd)[1]) if(m>1)res2 = as.vector(sapply(tree, function(x,y)colSums(y[x$tip.label,,drop=FALSE]) , y=tmp)) else res2 = colSums(tmp[tree$tip.label,,drop=FALSE]) result <- data.frame(tree = res[,2], variable=rep(colnames(tmp),m), pscore=res[,1], ntips=nt, natives=res2) result } phangorn/R/cladePar.R0000644000175100001440000000141612761653173014160 0ustar hornikuserscladePar = function(tree, node, edge.color="red", tip.color=edge.color, edge.width = 1, edge.lty = 1, x=NULL, plot=FALSE, ...){ if(is.null(x)){ m = max(tree$edge) x=list(edge=data.frame(color=rep("black",m), width = rep(1, m), lty = rep(1, m), stringsAsFactors = FALSE),tip=rep("black", length(tree$tip.label))) } ind = Descendants(tree,node,"all") x$edge$color[ind] = edge.color x$edge$width[ind] = edge.width x$edge$lty[ind] = edge.lty x[[2]][Descendants(tree, node, "tips")[[1]]] = tip.color if(plot){ tree=reorder(tree) plot(tree, edge.color=x$edge$color[tree$edge[,2]], edge.width = x$edge$width[tree$edge[,2]], edge.lty = x$edge$lty[tree$edge[,2]], tip.color=x[[2]],...) } else return(x) } phangorn/R/hadamard.R0000644000175100001440000001164712761653173014215 0ustar hornikusersdec2Bin = function (x) { res = NULL i = 1L while (x > 0) { if (x%%2L) res = c(res, i) x = x%/%2L i = i + 1L } res } # returns binary (0, 1) vector of length k dec2bin <- function (x, k=ceiling(log2(x))) { i = 1L res = integer(k) while (x > 0) { if (x%%2L) res[i] = 1L x = x%/%2L i = i + 1L } res } # double factorial: log version "ldfactorial" <- function(x){ x = (x+1)/2 res = lgamma(2*x)-(lgamma(x)+(x-1)*log(2)) res } # double factorial "dfactorial" <- function(x){exp(ldfactorial(x))} # # Hadamard Conjugation # hadamard <- function(x){ res=1 while(x>0){ res=rbind(cbind(res,res),cbind(res,-res)) x=x-1 } res } fhm <- function(v){ n = length(v) n = log2(n) res = .C("C_fhm", v = as.double(v), n = as.integer(n))$v # res } seq2split = function(s){ n=length(s) res= fhm(log(fhm(s)))/n res } split2seq = function(q){ n=length(q) res= fhm(exp(fhm(q)))/n res } distanceHadamard <- function (dm, eps = 0.001) { if (inherits(dm,"dist")) { n <- attr(dm, "Size") Labels = attr(dm, "Labels") } if (inherits(dm,"matrix")) { n <- dim(dm)[1] Labels <- colnames(dm) dm <- dm[lower.tri(dm)] } ns <- 2^(n - 1) if (n > 23) stop("Hadamard conjugation works only efficient for n < 24") result <- .Call("dist2spectra", dm, as.integer(n), as.integer(ns), PACKAGE = "phangorn") weights = -fhm(result)/2^(n - 2) if(eps>0){ weights = weights[-1] ind2 = which(weights>eps) n2 = length(ind2) splits = vector("list", n2) for(i in 1:n2)splits[[i]] = dec2Bin(ind2[i]) attr(splits, "weights") = weights[ind2] attr(splits, "labels") = Labels attr(splits, 'dm') = dm class(splits)='splits' return(splits) } res <- data.frame(distance = result, edges = weights, index = 0:(ns - 1)) attr(res, "Labels") <- Labels res } h4st = function(obj, levels=c('a','c','g','t')){ if (is.matrix(obj)) obj = as.data.frame(t(obj)) if (inherits(obj,"phyDat")) obj = as.data.frame(t(as.character(obj))) # if(is.matrix(obj)) obj = as.data.frame(t(obj)) # DNA = as.data.frame(obj) # DNA = t(as.character(obj)) n = dim(obj)[1] p = dim(obj)[2] if(p>11) stop("4-state Hadamard conjugation works only efficient for n < 12") DNAX = matrix(0,n,p) DNAY = matrix(0,n,p) DNAX[obj==levels[1]]=0 DNAX[obj==levels[2]]=1 DNAX[obj==levels[3]]=1 DNAX[obj==levels[4]]=0 DNAY[obj==levels[1]]=0 DNAY[obj==levels[2]]=1 DNAY[obj==levels[3]]=0 DNAY[obj==levels[4]]=1 DNAY = DNAY - DNAY[,p] DNAX = DNAX - DNAX[,p] DNAY = abs(DNAY[,-p]) DNAX = abs(DNAX[,-p]) dy = DNAY %*% (2^(0:(p-2))) dx = DNAX %*% (2^(0:(p-2))) INDEX = dx + 2^(p-1) * dy blub = table(INDEX) index = as.numeric(rownames(blub)) + 1 sv = numeric(4^(p-1)) sv[index] = blub qv = matrix(seq2split(sv),2^(p-1),2^(p-1)) sv = matrix(sv,2^(p-1),2^(p-1)) # q = cbind(transversion = qv[-1,1], transition.1 = diag(qv)[-1], transition.2 = qv[1,-1]) transversion <- transition.1 <- transition.2 <- allSplits(p, colnames(obj)) attr(transversion,"weights") = qv[-1,1] attr(transition.1,"weights") = diag(qv)[-1] attr(transition.2,"weights") = qv[1,-1] # result = list(q = q, qv = qv, sv=sv, n=sum(sv), names=names(obj)) result = list(transversion = transversion, transition.1=transition.1, transition.2 = transition.2, qv = qv, sv=sv, n=sum(sv), names=names(obj)) result } h2st <- function (obj, eps=0.001) { if (!inherits(obj,"phyDat")) stop("Error") if (attr(obj,"nc") != 2)stop("Error") nr = attr(obj, "nr") #n p = length(obj) #p weight = attr(obj, "weight") if (p > 23) stop("Hadamard conjugation works only efficient for n < 24") DNAX = matrix(0, nr, p-1) for(i in 1:(p-1)) DNAX[,i] = obj[[i]]-1 DNAX[obj[[p]]==2,] = 1 - DNAX[obj[[p]]==2,] index = DNAX %*% (2^(0:(p - 2))) + 1 sv = numeric(2^(p - 1)) for(i in 1:nr)sv[index[i]] = sv[index[i]]+ weight[i] qv = seq2split(sv) if(eps>0){ qv = qv[-1] ind2 = which(qv>eps) indT= c(2L^(0:(p-2)), 2L^(p-1)-1) ind2 = union(ind2, indT) n2 = length(ind2) splits = vector("list", n2) for(i in 1:n2)splits[[i]] = dec2Bin(ind2[i]) attr(splits, "weights") = qv[ind2] attr(splits, "labels") = names(obj) class(splits)='splits' return(splits) } result = data.frame(edges = qv, splits = sv, index = 0:(2^(p - 1) - 1)) attr(result, "Labels") = names(obj) result } phangorn/R/bootstrap.R0000644000175100001440000001613113020402021014427 0ustar hornikusersbootstrap.pml <- function (x, bs = 100, trees = TRUE, multicore=FALSE, mc.cores = NULL, ...) { if(multicore && is.null(mc.cores)){ mc.cores <- detectCores() } data = x$data weight = attr(data, "weight") v = rep(1:length(weight), weight) BS = vector("list", bs) for (i in 1:bs) BS[[i]] = tabulate(sample(v, replace = TRUE), length(weight)) pmlPar <- function(weights, fit, trees = TRUE, ...) { data = fit$data ind <- which(weights > 0) data <- getRows(data, ind) attr(data, "weight") <- weights[ind] fit = update(fit, data = data) fit = optim.pml(fit, ...) if (trees) { tree = fit$tree return(tree) } attr(fit, "data") = NULL fit } eval.success <- FALSE if (!eval.success & multicore) { res <- mclapply(BS, pmlPar, x, trees = trees, ..., mc.cores = mc.cores) eval.success <- TRUE } if (!eval.success) res <- lapply(BS, pmlPar, x, trees = trees, ...) if (trees) { class(res) = "multiPhylo" res = .compressTipLabel(res) # save memory } res } bootstrap.phyDat <- function (x, FUN, bs = 100, multicore=FALSE, mc.cores = NULL, jumble=TRUE, ...) { if(multicore && is.null(mc.cores)){ mc.cores <- detectCores() } weight = attr(x, "weight") v = rep(1:length(weight), weight) BS = vector("list", bs) for(i in 1:bs)BS[[i]]=tabulate(sample(v, replace=TRUE),length(weight)) if(jumble){ J = vector("list", bs) l = length(x) for(i in 1:bs) J[[i]] = list(BS[[i]], sample(l)) } fitPar <- function(weights, data, ...){ ind <- which(weights > 0) data <- getRows(data, ind) attr(data, "weight") <- weights[ind] FUN(data,...) } fitParJumble <- function(J, data, ...){ ind <- which(J[[1]] > 0) data <- getRows(data, ind) attr(data, "weight") <- J[[1]][ind] data <- subset(data, J[[2]]) FUN(data,...) } if(multicore){ if(jumble) res <- mclapply(J, fitPar, x, ..., mc.cores = mc.cores) else res <- mclapply(BS, fitPar, x, ..., mc.cores = mc.cores) } else{ if(jumble) res <- lapply(J, fitParJumble, x, ...) else res <- lapply(BS, fitPar, x, ...) } if(class(res[[1]]) == "phylo"){ class(res) <- "multiPhylo" res = .compressTipLabel(res) # save memory } res } matchEdges <- function(tree1, tree2){ bp1 <- bip(tree1) bp2 <- bip(tree2) l <- length(tree1$tip.label) fn <- function(x, y){ if(x[1]==1)return(x) else return(y[-x]) } bp1[] = lapply(bp1, fn, 1:l) bp2[] = lapply(bp2, fn, 1:l) match(bp1, bp2) } checkLabels <- function(tree, tip){ ind <- match(tip, tree$tip.label) if (any(is.na(ind)) | length(tree$tip.label) != length(tip)) stop("tree has different labels") tree$tip.label <- tree$tip.label[ind] ind2 <- match(1:length(ind), tree$edge[, 2]) tree$edge[ind2, 2] <- order(ind) tree } plotBS <- function (tree, BStrees, type = "unrooted", bs.col = "black", bs.adj = NULL, p=50, frame="none",...) { type <- match.arg(type, c("phylogram", "cladogram", "fan", "unrooted", "radial")) if (type == "phylogram" | type == "cladogram") { if (!is.rooted(tree) & !is.null(tree$edge.length)) tree2 = midpoint(tree) else tree2=tree plot(tree2, type = type, ...) } else plot(tree, type = type, ...) if(hasArg(BStrees)){ BStrees <- .uncompressTipLabel(BStrees) # check if needed if(any(is.rooted(BStrees)))BStrees <- unroot(BStrees) # if(any(unlist(lapply(BStrees, is.rooted)))){ # BStrees <- lapply(BStrees, unroot) # } x = prop.clades(tree, BStrees) x = round((x/length(BStrees)) * 100) tree$node.label = x } else{ if(is.null(tree$node.label))stop("You need to supply BStrees or tree needs needs BS-values as node.label") x <- tree$node.label } label = c(rep(0, length(tree$tip.label)), x) ind <- get("last_plot.phylo", envir = .PlotPhyloEnv)$edge[, 2] if (type == "phylogram" | type == "cladogram") { root = getRoot(tree) label = c(rep(0, length(tree$tip.label)), x) label[root] = 0 ind2 = matchEdges(tree2, tree) label = label[ind2] ind = which(label > p) # browser() if (is.null(bs.adj)) bs.adj = c(1, 1) if(length(ind)>0)nodelabels(text = label[ind], node = ind, frame = frame, col = bs.col, adj = bs.adj, ...) } else { if (is.null(bs.adj)) bs.adj = c(0.5, 0.5) ind2 = which(label[ind]>p) if(length(ind2>0))edgelabels(label[ind][ind2],ind2, frame = frame, col = bs.col, adj = bs.adj, ...) } invisible(tree) } maxCladeCred <- function(x, tree=TRUE, part=NULL, rooted=TRUE){ if(inherits(x, "phylo")) x <- c(x) if (is.null(part)){ if (!rooted) pp <- unroot(x) %>% prop.part else pp <- prop.part(x) } else pp <- part pplabel <- attr(pp, "labels") if(!rooted)pp <- oneWise(pp) x <- .uncompressTipLabel(x) class(x) <- NULL m <- max(attr(pp, "number")) nb <- log( attr(pp, "number") / m ) l <- length(x) res <- numeric(l) for(i in 1:l){ tmp <- checkLabels(x[[i]], pplabel) if(!rooted)tmp <- unroot(tmp) ppi <- prop.part(tmp) # trees[[i]] if(!rooted)ppi <- oneWise(ppi) indi <- fmatch(ppi, pp) if(any(is.na(indi))) res[i] <- -Inf else res[i] <- sum(nb[indi]) } if(tree) { k <- which.max(res) tr <- x[[k]] attr(tr, "clade.credibility") <- res[k] return(tr) } res } mcc <- maxCladeCred cladeMatrix <- function(x, rooted=FALSE){ if(!rooted) x <- unroot(x) # if(!rooted){ # x <- .uncompressTipLabel(x) # x <- lapply(x, unroot) # class(x) <- "multiPhylo" # x <- .compressTipLabel(x) # } pp <- prop.part(x) pplabel <- attr(pp, "labels") if(!rooted)pp <- oneWise(pp) x <- .uncompressTipLabel(x) class(x) <- NULL nnodes <- sapply(x, Nnode) l <- length(x) from <- cumsum(c(1, nnodes[-l])) to <- cumsum(nnodes) ivec <- integer(to[l]) pvec <- c(0,to) res <- vector("list", l) k=1 for(i in 1:l){ ppi <- prop.part(x[[i]]) if(!rooted)ppi <- oneWise(ppi) indi <- sort(fmatch(ppi, pp)) ivec[from[i]:to[i]] = indi } X <- sparseMatrix(i=ivec, p=pvec, dims=c(length(pp),l)) list(X=X, prop.part=pp) } moving_average <- function(obj, window=50){ fun <- function(x){ cx <- c(0, cumsum(x)) (cx[(window+1):length(cx)] - cx[1:(length(cx)-window)])/(window) } res <- apply(obj$X, 1, fun) rownames(res) <- c() } phangorn/R/ancestral_pml.R0000644000175100001440000000710013006133025015243 0ustar hornikusers# # ancestral sequences ML # ancestral.pml <- function (object, type=c("marginal", "ml", "bayes")) { call <- match.call() type <- match.arg(type) pt <- match.arg(type, c("marginal", "joint", "ml", "bayes")) tree = object$tree INV <- object$INV inv <- object$inv data = getCols(object$data, tree$tip.label) if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") nTips = length(tree$tip.label) node <- tree$edge[, 1] edge <- tree$edge[, 2] m = length(edge) + 1 # max(edge) w = object$w g = object$g l = length(w) nr <- attr(data, "nr") nc <- attr(data, "nc") dat = vector(mode = "list", length = m*l) result = vector(mode = "list", length = m) dim(dat) <- c(l,m) x = attributes(data) label = as.character(1:m) nam = tree$tip.label label[1:length(nam)] = nam x[["names"]] = label tmp = length(data) result = new2old.phyDat(data) eig = object$eig bf = object$bf el <- tree$edge.length P <- getP(el, eig, g) nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) node = as.integer(node - min(node)) edge = as.integer(edge - 1) nTips = as.integer(length(tree$tip.label)) mNodes = as.integer(max(node) + 1) contrast = attr(data, "contrast") nco = as.integer(dim(contrast)[1]) for(i in 1:l)dat[i,(nTips + 1):m] <- .Call("LogLik2", data, P[i,], nr, nc, node, edge, nTips, mNodes, contrast, nco, PACKAGE = "phangorn") parent <- tree$edge[, 1] child <- tree$edge[, 2] nTips = min(parent) - 1 for(i in 1:l){ for (j in (m - 1):1) { if (child[j] > nTips){ tmp2 = (dat[[i, parent[j]]]/(dat[[i,child[j]]] %*% P[[i,j]])) dat[[i, child[j]]] = (tmp2 %*% P[[i,j]]) * dat[[i, child[j]]] } } } for (j in unique(parent)) { tmp <- matrix(0, nr, nc) if(inv>0) tmp = as.matrix(INV) * inv for(i in 1:l){ tmp = tmp + w[i] * dat[[i, j]] } if ( (pt == "bayes") || (pt == "marginal")) tmp = tmp * rep(bf, each=nr) tmp = tmp / rowSums(tmp) result[[j]] = tmp } attributes(result) = x attr(result, "call") <- call result } #joint_reconstruction <- function(object){ # #} ancestral2phyDat <- function(x) { eps <- 1.0e-5 contr <- attr(x, "contrast") # a bit too complicated ind1 <- which( apply(contr, 1, function(x)sum(x > eps)) == 1L) ind2 <- which( contr[ind1, ] > eps, arr.ind = TRUE) pos <- ind2[match(as.integer(1L:ncol(contr)), ind2[,2]),1] # only first hit res <- lapply(x, function(x, pos) pos[apply(x, 1, which.max)], pos) attributes(res) <- attributes(x) return(res) } fast.tree = function(tree, node){ parent = c(node, Ancestors(tree, node)) children = Descendants(tree, parent, 'children') l = lengths(children) # l = sapply(children, length) edge = cbind(rep(parent, l), unlist(children)) obj = list(edge=edge, Nnode=sum(l>0), tip.label=as.character(edge[is.na(match(edge[,2], edge[,1])),2])) class(obj) = 'phylo' obj } # schneller ??? fast.tree2 = function(tree, node){ parent = c(node, Ancestors(tree, node)) edge = tree$edge ind = match(edge[,1], parent) edge=edge[which(!is.na(ind)),] obj = list(edge=edge, Nnode=length(parent), tip.label=as.character(edge[is.na(match(edge[,2], edge[,1])),2])) class(obj) = 'phylo' obj } phangorn/R/modelTest.R0000644000175100001440000001762512761653173014416 0ustar hornikusersaic.weights <- function(aic){ diff.aic <- aic-min(aic) exp(-0.5 * diff.aic) / sum(exp(-0.5 * diff.aic)) } modelTest <- function (object, tree = NULL, model = c("JC", "F81", "K80", "HKY", "SYM", "GTR"), G = TRUE, I = TRUE, FREQ=FALSE, k = 4, control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1), multicore = FALSE, mc.cores = NULL) { # multicore = mc.cores > 1L if(multicore && is.null(mc.cores)){ mc.cores <- detectCores() } if (inherits(object,"phyDat")) data = object if (inherits(object,"pml")) { data = object$data if (is.null(tree)) tree = object$tree } if(attr(data, "type")=="DNA") type = c("JC", "F81", "K80", "HKY", "TrNe", "TrN", "TPM1", "K81", "TPM1u", "TPM2", "TPM2u", "TPM3", "TPM3u", "TIM1e", "TIM1", "TIM2e", "TIM2", "TIM3e", "TIM3", "TVMe", "TVM", "SYM", "GTR") if(attr(data, "type")=="AA") type = .aamodels if( (length(model)==1) && model == "all") model <- type model = match.arg(model, type, TRUE) env = new.env() assign("data", data, envir=env) if (is.null(tree)) tree = NJ(dist.hamming(data)) else{ tree <- nnls.phylo(tree, dist.ml(data)) # may need something faster for trees > 500 taxa } trace <- control$trace control$trace = trace - 1 fit = pml(tree, data) fit = optim.pml(fit, control = control) l = length(model) if(attr(fit$data, "type")=="DNA")FREQ=FALSE n = 1L + sum(I + G + (G & I) + FREQ + (FREQ & I) + (FREQ & G) + (FREQ & G & I)) nseq = sum(attr(data, "weight")) fitPar = function(model, fit, G, I, k, FREQ) { m = 1 res = matrix(NA, n, 6) res = as.data.frame(res) colnames(res) = c("Model", "df", "logLik", "AIC", "AICc", "BIC") data.frame(c("Model", "df", "logLik", "AIC", "AICc", "BIC")) calls = vector("list", n) trees = vector("list", n) fittmp = optim.pml(fit, model = model, control = control) res[m, 1] = model res[m, 2] = fittmp$df res[m, 3] = fittmp$logLik res[m, 4] = AIC(fittmp) res[m, 5] = AICc(fittmp) res[m, 6] = AIC(fittmp, k = log(nseq)) calls[[m]] = fittmp$call trees[[m]] = fittmp$tree m = m + 1 if (I) { if(trace>0)print(paste0(model, "+I")) fitI = optim.pml(fittmp, model = model, optInv = TRUE, control = control) res[m, 1] = paste0(model, "+I") res[m, 2] = fitI$df res[m, 3] = fitI$logLik res[m, 4] = AIC(fitI) res[m, 5] = AICc(fitI) res[m, 6] = AIC(fitI, k = log(nseq)) calls[[m]] = fitI$call trees[[m]] = fitI$tree m = m + 1 } if (G) { if(trace>0)print(paste0(model, "+G")) fitG = update(fittmp, k = k) fitG = optim.pml(fitG, model = model, optGamma = TRUE, control = control) res[m, 1] = paste0(model, "+G") res[m, 2] = fitG$df res[m, 3] = fitG$logLik res[m, 4] = AIC(fitG) res[m, 5] = AICc(fitG) res[m, 6] = AIC(fitG, k = log(nseq)) calls[[m]] = fitG$call trees[[m]] = fitG$tree m = m + 1 } if (G & I) { if(trace>0)print(paste0(model, "+G+I")) fitGI = update(fitI, k = k) fitGI = optim.pml(fitGI, model = model, optGamma = TRUE, optInv = TRUE, control = control) res[m, 1] = paste0(model, "+G+I") res[m, 2] = fitGI$df res[m, 3] = fitGI$logLik res[m, 4] = AIC(fitGI) res[m, 5] = AICc(fitGI) res[m, 6] = AIC(fitGI, k = log(nseq)) calls[[m]] = fitGI$call trees[[m]] = fitGI$tree m = m + 1 } if (FREQ) { if(trace>0)print(paste0(model, "+F")) fitF = optim.pml(fittmp, model = model, optBf = TRUE, control = control) res[m, 1] = paste0(model, "+F") res[m, 2] = fitF$df res[m, 3] = fitF$logLik res[m, 4] = AIC(fitF) res[m, 5] = AICc(fitF) res[m, 6] = AIC(fitF, k = log(nseq)) calls[[m]] = fitF$call trees[[m]] = fitF$tree m = m + 1 } if (FREQ & I) { if(trace>0)print(paste0(model, "+I+F")) fitIF <- update(fitF, inv = fitI$inv) fitIF = optim.pml(fitIF, model = model, optBf = TRUE, optInv = TRUE, control = control) res[m, 1] = paste0(model, "+I+F") res[m, 2] = fitIF$df res[m, 3] = fitIF$logLik res[m, 4] = AIC(fitIF) res[m, 5] = AICc(fitIF) res[m, 6] = AIC(fitIF, k = log(nseq)) calls[[m]] = fitIF$call trees[[m]] = fitIF$tree m = m + 1 } if (FREQ & G) { if(trace>0)print(paste0(model, "+G+F")) fitGF <- update(fitF, k=k, shape=fitG$shape) fitGF = optim.pml(fitGF, model = model, optBf = TRUE, optGamma = TRUE, control = control) res[m, 1] = paste0(model, "+G+F") res[m, 2] = fitGF$df res[m, 3] = fitGF$logLik res[m, 4] = AIC(fitGF) res[m, 5] = AICc(fitGF) res[m, 6] = AIC(fitGF, k = log(nseq)) calls[[m]] = fitGF$call trees[[m]] = fitGF$tree m = m + 1 } if (FREQ & G & I) { if(trace>0)print(paste0(model, "+G+I+F")) fitGIF <- update(fitIF, k=k) fitGIF = optim.pml(fitGIF, model = model, optBf = TRUE, optInv = TRUE, optGamma = TRUE, control = control) res[m, 1] = paste0(model, "+G+I+F") res[m, 2] = fitGIF$df res[m, 3] = fitGIF$logLik res[m, 4] = AIC(fitGIF) res[m, 5] = AICc(fitGIF) res[m, 6] = AIC(fitGIF, k = log(nseq)) calls[[m]] = fitGIF$call trees[[m]] = fitGIF$tree m = m + 1 } list(res, trees, calls) } eval.success <- FALSE if (!eval.success & multicore) { # !require(parallel) || # if (.Platform$GUI != "X11") { # warning("package 'parallel' not found or GUI is used, \n analysis is performed in serial") # } # else { RES <- mclapply(model, fitPar, fit, G, I, k, FREQ, mc.cores=mc.cores) eval.success <- TRUE # } } if (!eval.success) RES <- lapply(model, fitPar, fit, G, I, k, FREQ) # res <- RES <- lapply(model, fitPar, fit, G, I, k, freq) # RESULT = matrix(NA, n * l, 6) # RESULT = as.data.frame(RESULT) # colnames(RESULT) = c("Model", "df", "logLik", "AIC", "AICc", "BIC") RESULT = matrix(NA, n * l, 8) RESULT = as.data.frame(RESULT) colnames(RESULT) = c("Model", "df", "logLik", "AIC", "AICw", "AICc", "AICcw", "BIC") for (i in 1:l) RESULT[((i - 1) * n + 1):(n * i), c(1,2,3,4,6,8)] = RES[[i]][[1]] RESULT[,5] <- aic.weights(RESULT[,4]) RESULT[,7] <- aic.weights(RESULT[,6]) for(i in 1:l){ for(j in 1:n){ mo = RES[[i]][[1]][j,1] tname = paste0("tree_", mo) tmpmod = RES[[i]][[3]][[j]] tmpmod["tree"] = call(tname) if(!is.null(tmpmod[["k"]]))tmpmod["k"] = k if(attr(data, "type")=="AA") tmpmod["model"] = RES[[i]][[1]][1,1] assign(tname, RES[[i]][[2]][[j]], envir=env) assign(mo, tmpmod, envir=env) } } attr(RESULT, "env") = env RESULT } phangorn/R/SH.R0000644000175100001440000000323612761653173012761 0ustar hornikusersSH.test <- function (..., B = 10000, data = NULL) { fits <- list(...) p = 1 if (inherits(fits[[1]],"pmlPart")) # class(fits[[1]]) == "pmlPart") { fits = fits[[1]]$fits p = length(fits) } k = length(fits) if (is.null(data)) data = fits[[1]]$data res = NULL for (h in 1:p) { if (p > 1) data = fits[[h]]$data weight = attr(data, "weight") lw = length(weight) siteLik = matrix(0, lw, k) for (i in 1:k) siteLik[, i] = update(fits[[i]], data = data)$site ntree = k Lalpha <- drop(crossprod(siteLik, weight)) Talpha <- max(Lalpha) - Lalpha M <- matrix(NA, k, B) # S <- matrix(NA, k, B) wvec <- rep(1L:lw, weight) size = length(wvec) for (i in 1:B) { boot = tabulate(sample(wvec, replace=TRUE), nbins=lw) M[, i] <- crossprod(siteLik, boot) } M <- M - rowMeans(M) # for (i in 1:B) for (j in 1:ntree) S[j, i] <- max(M[j, i] - M[, i]) S = matrix(apply(M,2,min), k, B, byrow=TRUE) S = M - S count <- numeric(ntree) for (j in 1:ntree) count[j] <- sum(S[j, ] > Talpha[j]) count <- count/B trees <- 1:k if (p == 1) res = cbind(trees, Lalpha, Talpha, count) else res = rbind(res, cbind(h, trees[-h], Lalpha[-h], Talpha[-h], count[-h])) } if (p == 1) colnames(res) <- c("Trees", "ln L", "Diff ln L", "p-value") else colnames(res) <- c("Partition", "Trees", "ln L", "Diff ln L", "p-value") res }phangorn/R/superTree.R0000644000175100001440000001134513020402733014404 0ustar hornikusers# now more memoryefficient # from phytools code by Liam Revell with a few changes my.supertree<-function(trees, trace=0, ...){ # some minor error checking if(!inherits(trees,"multiPhylo")) stop("trees must be object of class 'multiPhylo.'") labels <- lapply(trees, function(x)sort(x$tip.label)) ulabels <- unique(labels) lul <- length(ulabels) # compute matrix representation phylogenies X<-vector("list", lul) # list of bipartitions characters<-0 # number of characters weights <- NULL species<-trees[[1]]$tip.label for(i in 1:lul){ pos <- match(labels, ulabels[i]) ind <- which(!is.na(pos)) temp<-prop.part(trees[ind]) # find all bipartitions # create matrix representation of trees[[i]] in X[[i]] X[[i]]<-matrix(0,nrow=length(trees[[ind[1]]]$tip.label),ncol=length(temp)-1) for(j in 1:ncol(X[[i]])) X[[i]][c(temp[[j+1]]),j]<-1 rownames(X[[i]])<-attr(temp,"labels") # label rows # if(i==1) species<-trees[[ind[1]]]$tip.label # else species<-union(species,trees[[ind[1]]]$tip.label) # accumulate labels characters<-characters+ncol(X[[i]]) # count characters weights <- c(weights, attr(temp, "number")[-1]) } XX<-matrix(data="?",nrow=length(species),ncol=characters,dimnames=list(species)) j<-1 for(i in 1:length(X)){ # copy each of X into supermatrix XX XX[rownames(X[[i]]),c(j:((j-1)+ncol(X[[i]])))]<-X[[i]][1:nrow(X[[i]]),1:ncol(X[[i]])] j<-j+ncol(X[[i]]) } # compute contrast matrix for phangorn contrast<-matrix(data=c(1,0,0,1,1,1),3,2,dimnames=list(c("0","1","?"),c("0","1")),byrow=TRUE) # convert XX to phyDat object XX<-phyDat(XX,type="USER",contrast=contrast, compress=FALSE) attr(XX, "weight") <- weights # estimate supertree supertree<-pratchet(XX,all=TRUE, trace=trace, ...) return(supertree) } # Robinson-Foulds supertree fun.rf <- function(x, tree) sum(RF.dist(x, tree)) fun.spr <- function(x, tree) sum(SPR.dist(x, tree)) dist.superTree <- function(tree, trace=0, fun, start=NULL, multicore=FALSE, mc.cores = NULL){ if(multicore && is.null(mc.cores)){ mc.cores <- detectCores() } if(is.null(start))start <- superTree(tree, rooted=FALSE) if(inherits(start, "multiPhylo")) start <- start[[1]] best_tree <- unroot(start) best <- fun(best_tree, tree) if(trace>0)cat("best score so far:", best, "\n") eps <- TRUE while(eps){ nni_trees <- nni(best_tree) if (multicore) { tmp <- mclapply(nni_trees, fun, tree, mc.cores = mc.cores) tmp <-unlist(tmp) } else tmp <- sapply(nni_trees, fun, tree) if(min(tmp) < best){ ind <- which.min(tmp) best_tree <- nni_trees[[ind]] best <- tmp[ind] if(trace>0)cat("best score so far:", best, "\n") } else eps <- FALSE } attr(best_tree, "score") <- best best_tree } # we want a rooted supertree superTree = function(tree, method="MRP", rooted=FALSE, trace=0, ...){ fun = function(x){ x=reorder(x, "postorder") nTips = length(x$tip.label) x$edge[x$edge>nTips] = x$edge[x$edge>nTips] + 2L l=nrow(x$edge) oldroot = x$edge[l,1L] x$edge=rbind(x$edge,matrix(c(rep(nTips+2,2),oldroot,nTips+1),2L,2L)) x$edge.length=c(x$edge.length, 100, 100) x$tip.label=c(x$tip.label, "ZZZ") x$Nnode=x$Nnode+1L x } if(!is.null(attr(tree, "TipLabel")))tree = .uncompressTipLabel(tree) tree = unclass(tree) if(rooted) tree = lapply(tree, fun) class(tree)="multiPhylo" res = my.supertree(tree, trace=trace, ...) if(rooted){ if(inherits(res,"multiPhylo")){ res = lapply(res, root, "ZZZ") res = lapply(res, drop.tip, "ZZZ") class(res) = "multiPhylo" } else{ res = root(res, "ZZZ") res = drop.tip(res, "ZZZ") } } if(inherits(res,"multiPhylo")){ fun = function(x){ x$edge.length <- rep(.1, nrow(x$edge)) x } res <- lapply(res, fun) res <- lapply(res, reorder, "postorder") class(res) = "multiPhylo" } else{ res$edge.length = rep(.1, nrow(res$edge)) res <- reorder(res, "postorder") } if(method=="MRP") return(res) tree <- unroot(tree) tree <- reorder(tree, "postorder") # tree <- lapply(tree, unroot) # tree <- lapply(tree, reorder, "postorder") # class(tree) = "multiPhylo" if(method=="RF") res <- dist.superTree(tree, trace=trace, fun.rf, start=res) if(method=="SPR") res <- dist.superTree(tree, trace=trace, fun.spr, start=res) res } phangorn/R/sysdata.rda0000644000175100001440000004601512761653173014466 0ustar hornikusers‹í}w@U¹ò0M, T;*‚‚R *̱£ˆR,ØPDDi‚ Å.Hé R¤WéMz‘^”Þü€“Üý¸>ûÞ¾U÷­›?îÜ$“™Éd’ÉI9G~“â2fEf:ú‘ß‘ºáZ¦‘ˆžþÑa8…†f<´ÒŒ‚48Žá·ñgÿnùßËÿÞø¹ìX n_êôojüoÑý£ùß;?‰ïª÷·øüÓ®¯ðO{þ¹á{×÷{ùÓ—ïÏ Äæ÷½êû½üå¿Ë÷gâóû³ëû½üáåû£ñƒùý¨ñó[øÎô¿w ~0¿ÿ¶¾ÔŸý·áWk§?þ¨¿ú³øþ¬@ü >´žÔý·áŸvù9t~ýß 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Ü–æ4M‚‡³&œÝ8©~¢j9u ìðû>>øX{¥s{6g+u ?…`³ 5‘qæð6&…Ýú@qã±à«Ã†/×?ž nyµfÍM]‚¦±ûÒE£JHŽ—¥IVxI24’óˆ ;G DB,­É+ó«‚ðq—WcD'>sÙa•+_, ½åKp|*¶-ꂪÁx»YîùãN÷t–˜fhÏÍ›òÆ»üFýû$ˆX¸…Ùm‡¤²kï˜ÜeC°ÍÚe»Ûrjך¥| ª‡˜à1‡¢…ÇŽ¸jCàð,ýâÜÇq&ÌuŽøVf¯åòøå6ÍÐÿdƪ>õphangorn/R/treedist.R0000644000175100001440000006240513012724712014261 0ustar hornikusers# # tree distance functions # allKids <- function(phy){ nTips = as.integer(length(phy$tip.label)) lp=nrow(phy$edge) nNode = phy$Nnode .C("AllKids", as.integer(phy$edge[,2]), as.integer(phy$edge[,1]), as.integer(nTips), as.integer(nNode), as.integer(lp), integer(lp), integer(nNode+1L),integer(nNode)) } coph <- function(x, path=FALSE){ if (is.null(attr(x, "order")) || attr(x, "order") == "cladewise") x <- reorder(x, "postorder") nTips = as.integer(length(x$tip.label)) parents = as.integer(x$edge[,1]) kids = as.integer(x$edge[,2]) lp= as.integer(length(parents)) nNode = as.integer(x$Nnode) m = as.integer(max(x$edge)) el = double(m) if(path) el <- rep(1.0, m) else el[kids] = x$edge.length dm <- .C("C_cophenetic", kids, parents, as.double(el), lp, m, nTips, nNode, double(nTips*(nTips-1L)/2L))[[8]] attr(dm, "Size") <- nTips attr(dm, "Labels") <- x$tip.label attr(dm, "Diag") <- FALSE attr(dm, "Upper") <- FALSE class(dm) <- "dist" dm } cophenetic.splits <- function(x){ labels <- attr(x, "labels") X <- splits2design(x) dm <- as.vector(X%*%attr(x, "weight")) attr(dm, "Size") <- length(labels) attr(dm, "Labels") <- labels attr(dm, "Diag") <- FALSE attr(dm, "Upper") <- FALSE class(dm) <- "dist" dm } cophenetic.networx <- function(x){ # spl <- attr(x, "splits") spl <- x$splits cophenetic.splits(spl) } SHORTwise <- function (x, nTips, delete=FALSE) { v <- 1:nTips # l <- sapply(x, length) l <- lengths(x) lv = floor(nTips/2) for (i in 1:length(x)) { if(l[i]>lv){ y <- x[[i]] x[[i]] <- v[-y] } if(l[i]==nTips/2){ y <- x[[i]] if (y[1] != 1) x[[i]] <- v[-y] } } if(any(l==nTips) && delete){ x=x[l!=nTips] } x } oneWise <- function (x, nTips=NULL) { if(is.null(nTips))nTips <- length(x[[1L]]) v <- 1:nTips for (i in 2:length(x)) { y <- x[[i]] if (y[1] != 1) y <- v[-y] if (y[1] != 1) y <- v[-y] x[[i]] <- y } x } # leomrtns addition sprdist <- function (tree1, tree2) { tree1 = unroot(tree1) tree2 = unroot(tree2) lt1 = length(tree1$tip.label) lt2 = length(tree2$tip.label) # checking labels is obligatory for spr (user should prune one of them beforehand?) ind <- match(tree1$tip.label, tree2$tip.label) if (any(is.na(ind)) | lt1 != lt2 ) stop("trees have different labels") tree2$tip.label <- tree2$tip.label[ind] ind2 <- match(1:length(ind), tree2$edge[, 2]) tree2$edge[ind2, 2] <- order(ind) # same as in original treedist (will create list of strings with shorter side of splits) tree1 = reorder(tree1, "postorder") tree2 = reorder(tree2, "postorder") if(!is.binary.tree(tree1) | !is.binary.tree(tree2))message("Trees are not binary!") # possibly replace bip with bipart bp1 = bip(tree1); bp1 <- SHORTwise(bp1, lt1) bp2 = bip(tree2); bp2 <- SHORTwise(bp2, lt2) bp1 <- bp1[ lengths(bp1)>1 ] # only internal nodes bp2 <- bp2[ lengths(bp2)>1 ] if (length(bp1) != length(bp2)) stop ("number of bipartitions given to C_sprdist are not the same") # OBS: SPR distance works w/ incompatible splits only, but it needs common cherries (to replace by single leaf) spr <- .Call("C_sprdist", bp1, bp2, lt1); names(spr) <- c("spr", "spr_extra", "rf", "hdist") # list("spr" = spr[1], "spr_extra" = spr[2], "rf" = spr[3], "hdist"= spr[4]); spr } SPR1 <- function(trees){ trees <- .compressTipLabel(trees) trees <- .uncompressTipLabel(trees) trees <- lapply(trees, unroot) trees <- lapply(trees, reorder, "postorder") nTips <- length(trees[[1]]$tip.label) fun <- function(x, nTips){ bp <- bipart(x) bp <- SHORTwise(bp, nTips) bp <- bp[ lengths(bp)>1 ] bp } BP <- lapply(trees, fun, nTips) k <- 1 l <- length(trees) SPR <- numeric((l * (l - 1))/2) for (i in 1:(l - 1)){ bp <- BP[[i]] for (j in (i + 1):l){ SPR[k] <- .Call("C_sprdist", bp, BP[[j]], nTips)[1] k=k+1 } } attr(SPR, "Size") <- l if(!is.null(names(trees)))attr(SPR, "Labels") <- names(trees) attr(SPR, "Diag") <- FALSE attr(SPR, "Upper") <- FALSE class(SPR) <- "dist" return(SPR) } SPR2 <- function(tree, trees){ trees <- .compressTipLabel(trees) tree <- checkLabels(tree, attr(trees, "TipLabel")) trees <- .uncompressTipLabel(trees) if (any(sapply(trees, is.rooted))) { trees <- lapply(trees, unroot) } trees <- lapply(trees, reorder, "postorder") tree <- unroot(tree) nTips <- length(tree$tip.label) fun <- function(x, nTips){ bp <- bipart(x) bp <- SHORTwise(bp, nTips) bp <- bp[ lengths(bp)>1 ] bp } bp <- fun(tree, nTips) k <- 1 l <- length(trees) SPR <- numeric(l) for (i in 1:l){ SPR[i] <- .Call("C_sprdist", bp, fun(trees[[i]], nTips), nTips)[1] } if(!is.null(names(trees)))names(SPR) <- names(trees) return(SPR) } SPR.dist <- function(tree1, tree2=NULL){ if(inherits(tree1, "multiPhylo") && is.null(tree2))return(SPR1(tree1)) if(inherits(tree1, "phylo") && inherits(tree2, "phylo"))return(sprdist(tree1, tree2)[1]) if(inherits(tree1, "phylo") && inherits(tree2, "multiPhylo"))return(SPR2(tree1, tree2)) if(inherits(tree2, "phylo") && inherits(tree1, "multiPhylo"))return(SPR2(tree2, tree1)) return(NULL) } treedist <- function (tree1, tree2, check.labels=TRUE) { tree1 = unroot(tree1) tree2 = unroot(tree2) if (check.labels) { ind <- match(tree1$tip.label, tree2$tip.label) if (any(is.na(ind)) | length(tree1$tip.label) != length(tree2$tip.label)) stop("trees have different labels") tree2$tip.label <- tree2$tip.label[ind] ind2 <- match(1:length(ind), tree2$edge[, 2]) tree2$edge[ind2, 2] <- order(ind) } tree1 = reorder(tree1, "postorder") tree2 = reorder(tree2, "postorder") symmetric.difference = NULL branch.score.difference = NULL path.difference = NULL quadratic.path.difference = NULL if(!is.binary.tree(tree1) | !is.binary.tree(tree2))message("Trees are not binary!") bp1 = bip(tree1) bp2 = bip(tree2) bp1 <- SHORTwise(bp1, length(tree1$tip.label)) bp2 <- SHORTwise(bp2, length(tree2$tip.label)) bp1 <- sapply(bp1, paste, collapse = "_") bp2 <- sapply(bp2, paste, collapse = "_") l = length(tree1$tip.label) if (!is.null(tree1$edge.length) & !is.null(tree2$edge.length)) { dv1 = coph(tree1) dv2 = coph(tree2) quadratic.path.difference = sqrt(sum((dv1 - dv2)^2)) } RF = sum(match(bp1, bp2, nomatch=0L)==0L) + sum(match(bp2, bp1, nomatch=0L)==0L) symmetric.difference = RF #2 * (p - sum(r1)) if (!is.null(tree1$edge.length) & !is.null(tree2$edge.length)) { w1 = numeric(max(tree1$edge)) w2 = numeric(max(tree2$edge)) w1[tree1$edge[,2]] = tree1$edge.length w2[tree2$edge[,2]] = tree2$edge.length v1 = tree1$edge.length v2 = tree2$edge.length ind3 = match(bp1, bp2, nomatch=0L) ind4 = ind3[ind3>0] ind3 = which(ind3>0) s1 = sum((w1[ind3] - w2[ind4])^2) s2 = sum(w1[-ind3]^2) s3 = sum(w2[-ind4]^2) branch.score.difference = sqrt(s1 + s2 + s3) } tree1$edge.length = rep(1, nrow(tree1$edge)) tree2$edge.length = rep(1, nrow(tree2$edge)) dt1 = coph(tree1) dt2 = coph(tree2) path.difference = sqrt(sum((dt1 - dt2)^2)) result = c(symmetric.difference = symmetric.difference, branch.score.difference = branch.score.difference, path.difference = path.difference, quadratic.path.difference = quadratic.path.difference) result } wRF0 <- function(tree1, tree2, normalize=FALSE, check.labels=TRUE, rooted = FALSE){ r1 = is.rooted(tree1) r2 = is.rooted(tree2) if (r1 != r2) { message("one tree is unrooted, unrooted both") } if (!rooted) { if (r1) tree1 <- unroot(tree1) if (r2) tree2 <- unroot(tree2) } if (!r1 | !r2) { if (r1) tree1 = unroot(tree1) if (r2) tree2 = unroot(tree2) } if (!is.binary.tree(tree1) | !is.binary.tree(tree2)) message("Trees are not binary!") if (check.labels) { ind <- match(tree1$tip.label, tree2$tip.label) if (any(is.na(ind)) | length(tree1$tip.label) != length(tree2$tip.label)) stop("trees have different labels") tree2$tip.label <- tree2$tip.label[ind] ind2 <- match(1:length(ind), tree2$edge[, 2]) tree2$edge[ind2, 2] <- order(ind) } bp1 = bip(tree1) bp2 = bip(tree2) if (!rooted) { bp1 <- SHORTwise(bp1, length(tree1$tip.label)) bp2 <- SHORTwise(bp2, length(tree2$tip.label)) } bp1 <- sapply(bp1, paste, collapse = "_") bp2 <- sapply(bp2, paste, collapse = "_") w1 <- numeric(max(tree1$edge)) w2 <- numeric(max(tree2$edge)) w1[tree1$edge[,2]] <- tree1$edge.length w2[tree2$edge[,2]] <- tree2$edge.length ind3 <- match(bp1, bp2, nomatch=0L) ind4 <- ind3[ind3>0] ind3 <- which(ind3>0) s1 <- sum(abs(w1[ind3] - w2[ind4])) s2 <- sum(w1[-ind3]) s3 <- sum(w2[-ind4]) wRF <- s1 + s2 + s3 if(normalize) wRF <- wRF / (sum(tree1$edge.length) + sum(tree2$edge.length)) return(wRF) } wRF2 <- function(tree, trees, normalize=FALSE, check.labels = TRUE){ if(check.labels){ trees <- .compressTipLabel(trees) tree <- checkLabels(tree, attr(trees, "TipLabel")) } trees <- .uncompressTipLabel(trees) unclass(trees) if (any(sapply(trees, is.rooted))) { trees <- lapply(trees, unroot) } nTips <- length(tree$tip.label) fun1 <- function(x){ w <- numeric(max(x$edge)) w[x$edge[,2]] <- x$edge.length w } W <- lapply(trees, fun1) fun2 <- function(x, nTips){ bp <- bip(x) bp <- SHORTwise(bp, nTips) bp <- sapply(bp, paste, collapse = "_") bp } BP <- lapply(trees, fun2, nTips) if(is.rooted(tree)) tree <- unroot(tree) bp = bip(tree) bp <- SHORTwise(bp, nTips) bp <- sapply(bp, paste, collapse = "_") w <- numeric(max(tree$edge)) w[tree$edge[,2]] <- tree$edge.length l <- length(trees) wRF <- numeric(l) for(i in 1:l){ ind3 = fmatch(BP[[i]], bp, nomatch=0L) ind4 = ind3[ind3>0] ind3 = which(ind3>0) s1 = sum(abs(W[[i]][ind3] - w[ind4])) s2 = sum(W[[i]][-ind3]) s3 = sum(w[-ind4]) wRF[i] = (s1 + s2 + s3) } if(normalize){ sc <- sapply(trees, function(x)sum(x$edge.length)) + sum(tree$edge.length) wRF <- wRF / sc } wRF } wRF1 <- function(trees, normalize=FALSE, check.labels = TRUE){ if(check.labels) trees <- .compressTipLabel(trees) trees <- .uncompressTipLabel(trees) unclass(trees) nTips <- length(trees[[1]]$tip.label) if (any(sapply(trees, is.rooted))) { trees <- lapply(trees, unroot) } fun1 <- function(x){ w <- numeric(max(x$edge)) w[x$edge[,2]] <- x$edge.length w } W <- lapply(trees, fun1) fun2 <- function(x, nTips){ bp <- bip(x) bp <- SHORTwise(bp, nTips) bp <- sapply(bp, paste, collapse = "_") bp } if(normalize) sc <- sapply(trees, function(x)sum(x$edge.length)) BP <- lapply(trees, fun2, nTips) k <- 1 l <- length(trees) wRF <- numeric((l * (l - 1))/2) for (i in 1:(l - 1)){ bp <- BP[[i]] w <- W[[i]] for (j in (i + 1):l){ ind3 = fmatch(BP[[j]], bp, nomatch=0L) ind4 = ind3[ind3>0] ind3 = which(ind3>0) s1 = sum(abs(W[[j]][ind3] - w[ind4])) s2 = sum(W[[j]][-ind3]) s3 = sum(w[-ind4]) wRF[k] <- (s1 + s2 + s3) if(normalize) wRF[k] <- wRF[k] / (sc[i] + sc[j]) k=k+1 } } attr(wRF, "Size") <- l if(!is.null(names(trees)))attr(wRF, "Labels") <- names(trees) attr(wRF, "Diag") <- FALSE attr(wRF, "Upper") <- FALSE class(wRF) <- "dist" return(wRF) } mRF2 <- function(tree, trees, normalize=FALSE, check.labels = TRUE){ if (!inherits(trees,"multiPhylo")) stop("trees should be an object of class \"multiPhylo\"") if (!inherits(tree,"phylo")) stop("trees should be an object of class \"phylo\"") trees <- .compressTipLabel(trees) tipLabel <- attr(trees, "TipLabel") if (check.labels) { ind <- match(tipLabel, tree$tip.label) if (any(is.na(ind)) | length(tipLabel) != length(tree$tip.label)) stop("trees have different labels") tree$tip.label <- tree$tip.label[ind] ind2 <- match(1:length(ind), tree$edge[, 2]) tree$edge[ind2, 2] <- order(ind) } nTips <- length(tipLabel) l <- length(trees) RF <- numeric(l) trees <- .uncompressTipLabel(trees) # n <- length(attr(trees, "TipLabel")) trees <- unclass(trees) if (any(sapply(trees, is.rooted))) { message("Some trees are rooted. Unrooting all trees.\n") trees <- lapply(trees, unroot) } if(is.rooted(tree)) tree <- unroot(tree) if (any(sapply(trees, function(x) !is.binary.tree(x)))) { message("Some trees are not binary. Result may not what you expect!") } tree <- reorder(tree, "postorder") trees <- lapply(trees, reorder, "postorder") xx <- lapply(trees, bipart) xx <- lapply(xx, SHORTwise, nTips) xx <- lapply(xx,function(x)sapply(x, paste, collapse="_")) yy <- bipart(tree) yy <- SHORTwise(yy, nTips) yy <- sapply(yy, paste, collapse="_") NnodeT <- Nnode(tree) Nnodes <- sapply(trees, Nnode) for (i in 1:l){ RF[i] <- Nnodes[i] + NnodeT - 2 * sum(fmatch(xx[[i]], yy, nomatch=0L)>0L) # RF[i] <- sum(match(xx[[i]], yy, nomatch=0L)==0L) + sum(match(yy, xx[[i]], nomatch=0L)==0L) } if(!is.null(names(trees)))names(RF) <- names(trees) if(!normalize)return(RF) else{ sc <- sapply(trees, Nnode) + Nnode(tree) - 2 return(RF/sc) } } mRF<-function(trees, normalize=FALSE){ if (!inherits(trees,"multiPhylo")) stop("trees should be an object of class \"multiPhylo\"") trees <- .compressTipLabel(trees) tipLabel <- attr(trees, "TipLabel") nTips <- length(tipLabel) l <- length(trees) RF <- numeric((l * (l - 1))/2) trees <- .uncompressTipLabel(trees) # n <- length(attr(trees, "TipLabel")) trees <- unclass(trees) if (any(sapply(trees, is.rooted))) { message("Some trees are rooted. Unrooting all trees.\n") trees <- lapply(trees, unroot) } if (any(sapply(trees, function(x) !is.binary.tree(x)))) { message("Some trees are not binary. Result may not what you expect!") } trees <- lapply(trees, reorder, "postorder") xx <- lapply(trees, bipart) xx <- lapply(xx, SHORTwise, nTips) xx <- lapply(xx,function(x)sapply(x, paste, collapse="_")) # returns list of character vectors Nnodes <- sapply(trees, Nnode) k=1 for (i in 1:(l - 1)){ tmp = xx[[i]] for (j in (i + 1):l){ RF[k] <- Nnodes[i] + Nnodes[j] - 2 * sum(fmatch(xx[[j]], tmp, nomatch=0L)>0L) # RF[k] <- sum(match(xx[[j]], tmp, nomatch=0L)==0L) + sum(match(tmp, xx[[j]], nomatch=0L)==0L) if(normalize) RF[k] <- RF[k] / ( Nnodes[i] + Nnodes[j] - 2) k=k+1 } } attr(RF, "Size") <- l if(!is.null(names(trees)))attr(RF, "Labels") <- names(trees) attr(RF, "Diag") <- FALSE attr(RF, "Upper") <- FALSE class(RF) <- "dist" return(RF) } RF0 <- function(tree1, tree2=NULL, normalize=FALSE, check.labels = TRUE, rooted=FALSE){ r1 = is.rooted(tree1) r2 = is.rooted(tree2) if(!rooted){ if(r1) { tree1<-unroot(tree1) r1 <- FALSE } if(r2) { tree2<-unroot(tree2) r2 <- FALSE } } else{ if(r1 != r2) { message("one tree is unrooted, unrooted both") tree1<-unroot(tree1) tree2<-unroot(tree2) r1 <- r2 <- FALSE } } if (check.labels) { ind <- match(tree1$tip.label, tree2$tip.label) if (any(is.na(ind)) | length(tree1$tip.label) != length(tree2$tip.label)) stop("trees have different labels") tree2$tip.label <- tree2$tip.label[ind] # tree2$edge[match(ind, tree2$edge[, 2]), 2] <- 1:length(ind) ind2 <- match(1:length(ind), tree2$edge[, 2]) tree2$edge[ind2, 2] <- order(ind) } if(!is.binary.tree(tree1) | !is.binary.tree(tree2))message("Trees are not binary!") bp1 = bipart(tree1) bp2 = bipart(tree2) nTips <- length(tree1$tip.label) if(!rooted){ bp1 <- SHORTwise(bp1, nTips) bp2 <- SHORTwise(bp2, nTips) } RF = sum(match(bp1, bp2, nomatch=0L)==0L) + sum(match(bp2, bp1, nomatch=0L)==0L) if(normalize) RF <- RF / (Nnode(tree1) + Nnode(tree2) - 2) RF } RF.dist <- function(tree1, tree2=NULL, normalize=FALSE, check.labels = TRUE, rooted=FALSE) { if(class(tree1)=="phylo" && class(tree2)=="phylo")return(RF0(tree1, tree2, normalize, check.labels, rooted)) if(class(tree1)=="multiPhylo" && is.null(tree2))return(mRF(tree1, normalize)) if(class(tree1)=="phylo" && class(tree2)=="multiPhylo")return(mRF2(tree1, tree2, normalize, check.labels)) if(class(tree2)=="phylo" && class(tree1)=="multiPhylo")return(mRF2(tree2, tree1, normalize, check.labels)) else return(NULL) } wRF.dist <- function(tree1, tree2=NULL, normalize=FALSE, check.labels = TRUE, rooted=FALSE){ if(class(tree1)=="phylo" && class(tree2)=="phylo")return(wRF0(tree1, tree2, normalize, check.labels, rooted)) if(class(tree1)=="multiPhylo" && is.null(tree2))return(wRF1(tree1, normalize, check.labels)) if(class(tree1)=="phylo" && class(tree2)=="multiPhylo")return(wRF2(tree1, tree2, normalize, check.labels)) if(class(tree2)=="phylo" && class(tree1)=="multiPhylo")return(wRF2(tree2, tree1, normalize, check.labels)) else return(NULL) } kf0 <- function(tree1, tree2, check.labels = TRUE){ if(check.labels)tree2 <- checkLabels(tree2, tree1$tip.label) if(is.rooted(tree1)) tree1 <- unroot(tree1) if(is.rooted(tree2)) tree2 <- unroot(tree2) bp1 = bip(tree1) bp2 = bip(tree2) bp1 <- SHORTwise(bp1, length(tree1$tip.label)) bp2 <- SHORTwise(bp2, length(tree2$tip.label)) bp1 <- sapply(bp1, paste, collapse = "_") bp2 <- sapply(bp2, paste, collapse = "_") w1 <- numeric(max(tree1$edge)) w2 <- numeric(max(tree2$edge)) w1[tree1$edge[,2]] <- tree1$edge.length w2[tree2$edge[,2]] <- tree2$edge.length ind3 = match(bp1, bp2, nomatch=0L) ind4 = ind3[ind3>0] ind3 = which(ind3>0) s1 = sum((w1[ind3] - w2[ind4])^2) s2 = sum(w1[-ind3]^2) s3 = sum(w2[-ind4]^2) branch.score.difference = sqrt(s1 + s2 + s3) branch.score.difference } kf1 <- function(tree, trees, check.labels = TRUE){ if(check.labels){ trees <- .compressTipLabel(trees) tree <- checkLabels(tree, attr(trees, "TipLabel")) } trees <- .uncompressTipLabel(trees) unclass(trees) if (any(sapply(trees, is.rooted))) { trees <- lapply(trees, unroot) } nTips <- length(tree$tip.label) fun1 <- function(x){ w <- numeric(max(x$edge)) w[x$edge[,2]] <- x$edge.length w } W <- lapply(trees, fun1) fun2 <- function(x, nTips){ bp <- bip(x) bp <- SHORTwise(bp, nTips) bp <- sapply(bp, paste, collapse = "_") bp } BP <- lapply(trees, fun2, nTips) if(is.rooted(tree)) tree <- unroot(tree) bp = bip(tree) bp <- SHORTwise(bp, nTips) bp <- sapply(bp, paste, collapse = "_") w <- numeric(max(tree$edge)) w[tree$edge[,2]] <- tree$edge.length l <- length(trees) branch.score.difference <- numeric(l) for(i in 1:l){ ind3 = fmatch(BP[[i]], bp, nomatch=0L) ind4 = ind3[ind3>0] ind3 = which(ind3>0) s1 = sum((W[[i]][ind3] - w[ind4])^2) s2 = sum(W[[i]][-ind3]^2) s3 = sum(w[-ind4]^2) branch.score.difference[i] = sqrt(s1 + s2 + s3) } branch.score.difference } kf2 <- function(trees, check.labels = TRUE){ if(check.labels) trees <- .compressTipLabel(trees) trees <- .uncompressTipLabel(trees) unclass(trees) nTips <- length(trees[[1]]$tip.label) if (any(sapply(trees, is.rooted))) { trees <- lapply(trees, unroot) } fun1 <- function(x){ w <- numeric(max(x$edge)) w[x$edge[,2]] <- x$edge.length w } W <- lapply(trees, fun1) fun2 <- function(x, nTips){ bp <- bip(x) bp <- SHORTwise(bp, nTips) bp <- sapply(bp, paste, collapse = "_") bp } BP <- lapply(trees, fun2, nTips) k <- 1 l <- length(trees) KF <- numeric((l * (l - 1))/2) for (i in 1:(l - 1)){ bp <- BP[[i]] w <- W[[i]] for (j in (i + 1):l){ ind3 = fmatch(BP[[j]], bp, nomatch=0L) ind4 = ind3[ind3>0] ind3 = which(ind3>0) s1 = sum((W[[j]][ind3] - w[ind4])^2) s2 = sum(W[[j]][-ind3]^2) s3 = sum(w[-ind4]^2) KF[k] <- sqrt(s1 + s2 + s3) k=k+1 } } attr(KF, "Size") <- l if(!is.null(names(trees)))attr(KF, "Labels") <- names(trees) attr(KF, "Diag") <- FALSE attr(KF, "Upper") <- FALSE class(KF) <- "dist" return(KF) } # TODO distance matrices KF.dist <- function(tree1, tree2=NULL, check.labels = TRUE, rooted=FALSE){ if(inherits(tree1, "multiPhylo") && is.null(tree2))return(kf2(tree1)) if(inherits(tree1, "phylo") && inherits(tree2, "phylo"))return(kf0(tree1, tree2, check.labels)) if(inherits(tree1, "phylo") && inherits(tree2, "multiPhylo"))return(kf1(tree1, tree2, check.labels)) if(inherits(tree2, "phylo") && inherits(tree1, "multiPhylo"))return(kf1(tree2, tree1,check.labels)) return(NULL) } path.dist <- function(tree1, tree2=NULL, check.labels = TRUE, use.weight=FALSE){ if(inherits(tree1, "phylo") && inherits(tree2, "phylo")) return(pd0(tree1, tree2, check.labels, !use.weight)) if(inherits(tree1, "phylo") && inherits(tree2, "multiPhylo")) return(pd1(tree1, tree2, check.labels, !use.weight)) if(inherits(tree2, "phylo") && inherits(tree1, "multiPhylo")) return(pd1(tree2, tree1, check.labels, !use.weight)) if(inherits(tree1, "multiPhylo") && is.null(tree2)) return(pd2(tree1, check.labels, !use.weight)) else return(NULL) } #wPath.dist <- function(tree1, tree2=NULL, check.labels = TRUE){ # if(inherits(tree1, "phylo") && inherits(tree2, "phylo")) # return(pd0(tree1, tree2, check.labels, FALSE)) # if(inherits(tree1, "phylo") && inherits(tree2, "multiPhylo")) # return(pd1(tree1, tree2, check.labels, FALSE)) # if(inherits(tree2, "phylo") && inherits(tree1, "multiPhylo")) # return(pd1(tree2, tree1, check.labels, FALSE)) # if(inherits(tree1, "multiPhylo") && is.null(tree2)) # return(pd2(tree1, check.labels, FALSE)) # else return(NULL) #} pd0 <- function(tree1, tree2, check.labels=TRUE, path=TRUE){ if(check.labels)tree2 <- checkLabels(tree2, tree1$tip.label) if(path){ tree1 <- unroot(tree1) tree2 <- unroot(tree2) } # n <- nrow(tree1$edge) # tree1$edge.length <- tree2$edge.length <- rep(1, n) dt1 = coph(tree1, path) dt2 = coph(tree2, path) sqrt(sum((dt1 - dt2)^2)) } pd1 <- function(tree, trees, check.labels=TRUE, path=TRUE){ if(check.labels){ trees <- .compressTipLabel(trees) tree <- checkLabels(tree, attr(trees, "TipLabel")) } trees <- .uncompressTipLabel(trees) unclass(trees) if(path){ trees <- lapply(trees, unroot) tree <- unroot(tree) } trees <- lapply(trees, reorder, "postorder") l <- length(trees) dt <- coph(tree, path) res <- numeric(l) for(i in 1:l){ dt2 <- coph(trees[[i]], path) res[i] <- sqrt(sum((dt - dt2)^2)) } res } pd2 <- function(trees, check.labels=TRUE, path=TRUE){ if(check.labels) trees <- .compressTipLabel(trees) trees <- .uncompressTipLabel(trees) unclass(trees) if(path)trees <- lapply(trees, unroot) trees <- lapply(trees, reorder, "postorder") l <- length(trees) CM <- lapply(trees, coph, path) res <- numeric(l) k <- 1 PD <- numeric((l * (l - 1))/2) for (i in 1:(l - 1)){ for (j in (i + 1):l){ PD[k] <- sqrt(sum((CM[[i]] - CM[[j]])^2)) k=k+1 } } attr(PD, "Size") <- l if(!is.null(names(trees)))attr(PD, "Labels") <- names(trees) attr(PD, "Diag") <- FALSE attr(PD, "Upper") <- FALSE class(PD) <- "dist" return(PD) } phangorn/R/distTree.R0000644000175100001440000004162613013127651014223 0ustar hornikusers# # UPGMA, NJ, UNJ, nnls # "upgma" <- function(D,method="average",...){ DD=as.dist(D) hc = hclust(DD,method=method,...) result = as.phylo(hc) result = reorder(result, "postorder") result } "wpgma" <- function(D,method="mcquitty",...){ DD=as.dist(D) hc = hclust(DD,method=method,...) result = as.phylo(hc) result = reorder(result, "postorder") result } upgma.edge.length <- function(x, dm, method="average"){ METHODS <- c("average", "single", "complete") # , "mcquitty" i.meth <- match.arg(method, METHODS) X <- designTree(x, "rooted", TRUE) labels <- x$tip if(is.matrix(dm) || inherits(dm, "dist")){ dm <- as.matrix(dm)[labels,labels] dm <- dm[lower.tri(dm)] } ind <- X@i+1 node <- X@p X@x[] <- 1 nh <- numeric(max(x$edge)) Y = X*dm for(i in 1:(length(node)-1)){ pos = ind[(node[i]+1):node[i+1]] tmp <- switch(i.meth, average = mean(Y[pos, i]), single = min(Y[pos, i]), complete = max(Y[pos, i])) nh[X@nodes[i]] = tmp } x$edge.length <- (nh[x$edge[,1]] - nh[x$edge[,2]]) /2 x } upgma_nni <- function(d, method="average", opt="min", trace=0, mc.cores=2L) { METHODS <- c("average", "single", "complete") # , "mcquitty" method <- match.arg(method, METHODS) OPT <- c("min", "ls") opt <- match.arg(opt, OPT) tree <- upgma(d, method=method) labels <- tree$tip.label nTips <- length(labels) y <- as.matrix(d)[labels,labels] y <- y[lower.tri(y)] best.tree <- tree bestLS <- sum((coph(best.tree)-y)^2) bestME <- sum(best.tree$edge.length) run.nni = TRUE count_nni = 0 print(count_nni) while (run.nni) { trees <- nni(best.tree) trees <- .uncompressTipLabel(trees) trees <- unclass(trees) nni.trees <- lapply(trees, upgma.edge.length, y, method=method) ind <- which(sapply(nni.trees, function(x)!any(x$edge.length<0))) if (length(ind)==0)return(best.tree) nni.trees <- nni.trees[ind] if(opt == "min"){ ME <- sapply(nni.trees, function(x)sum(x$edge.length)) if (any(ME < bestME)){ bestME <- min(ME) count_nni = count_nni + 1 best.tree <- nni.trees[[which.min(ME)]] print(bestME) } else run.nni = FALSE } else{ LS <- sapply(nni.trees, function(x) sum((coph(x)-y)^2)) if (any(LS < bestLS)){ bestLS <- min(LS) count_nni = count_nni + 1 best.tree <- nni.trees[[which.min(LS)]] print(bestLS) } else run.nni = FALSE } } best.tree } NJ_old <- function(x) { x = as.matrix(x) labels <- attr(x, "Labels")[[1]] edge.length = NULL edge = NULL d = as.matrix(x) if (is.null(labels)) labels = colnames(d) l = dim(d)[1] m = l - 2 nam = 1L:l k = 2L * l - 2L while (l > 2) { r = rowSums(d)/(l - 2) i = 0 j = 0 tmp <- .C("out", as.double(d), as.double(r), as.integer(l), as.integer(i), as.integer(j)) e2 = tmp[[5]] e1 = tmp[[4]] l1 = d[e1, e2]/2 + (r[e1] - r[e2])/(2) l2 = d[e1, e2] - l1 edge.length = c(l1, l2, edge.length) edge = rbind(c(k, nam[e2]), edge) edge = rbind(c(k, nam[e1]), edge) nam = c(nam[c(-e1, -e2)], k) dnew = (d[e1, ] + d[e2, ] - d[e1, e2])/2 d = cbind(d, dnew) d = rbind(d, c(dnew, 0)) d = d[-c(e1, e2), -c(e1, e2)] k = k - 1L l = l - 1L } edge.length = c(d[2, 1], edge.length) attr(edge.length,"names") = NULL result = list(edge = rbind(c(nam[2], nam[1]), edge), edge.length = edge.length, tip.label = labels, Nnode = m) class(result) <- "phylo" reorder(result, "postorder") } NJ <- function(x) reorder(nj(x), "postorder") UNJ <- function(x) { x = as.matrix(x) labels <- attr(x, "Labels")[[1]] edge.length = NULL edge = NULL d = as.matrix(x) if (is.null(labels)) labels = colnames(d) l = dim(d)[1] n = l nam = as.character(1:l) m=l-2 nam = 1:l k = 2*l-2 w = rep(1,l) while (l > 2) { r = rowSums(d)/(l - 2) i = 0 j = 0 tmp <- .C("out", as.double(d), as.double(r), as.integer(l), as.integer(i), as.integer(j)) e2 = tmp[[5]] e1 = tmp[[4]] l1 = d[e1, e2]/2 + sum((d[e1,-c(e1,e2)] - d[e2,-c(e1,e2)])*w[-c(e1,e2)])/(2*(n-w[e1]-w[e2])) l2 = d[e1, e2]/2 + sum((d[e2,-c(e1,e2)] - d[e1,-c(e1,e2)])*w[-c(e1,e2)])/(2*(n-w[e1]-w[e2])) edge.length = c(l1, l2, edge.length) edge = rbind(c(k, nam[e2]), edge) edge = rbind(c(k, nam[e1]), edge) nam = c(nam[c(-e1, -e2)], k) dnew = (w[e1]*d[e1, ] + w[e2]*d[e2, ] - w[e1]*l1 - w[e2]*l2)/(w[e1] + w[e2]) d = cbind(d, dnew) d = rbind(d, c(dnew, 0)) d = d[-c(e1, e2), -c(e1, e2)] w = c(w, w[e1] + w[e2]) w = w[-c(e1, e2)] k = k - 1 l = l - 1 } edge.length=c(d[2,1],edge.length) result = list(edge = rbind(c(nam[2], nam[1]), edge), edge.length=edge.length, tip.label = labels, Nnode=m) class(result) <- "phylo" reorder(result, "postorder") } PNJ <- function (data) { q <- l <- r <- length(data) weight <- attr(data,"weight") height = NULL parentNodes <- NULL childNodes <- NULL nam <- names(data) tip.label <- nam edge = 1:q z = 0 D = matrix(0, q, q) for (i in 1:(l - 1)) { for (j in (i + 1):l) { w = (data[[i]] * data[[j]]) %*% c(1, 1, 1, 1) D[i, j] = sum(weight[w==0]) } } while (l > 1) { l = l - 1 z = z + 1 d = D + t(D) if(l>1) r = rowSums(d)/(l-1) if(l==1) r = rowSums(d) M = d - outer(r,r,"+") diag(M) = Inf e=which.min(M) e0=e%%length(r) e1 = ifelse(e0==0, length(r), e0) e2= ifelse(e0==0, e%/%length(r), e%/%length(r) + 1) ind = c(e1,e2) len = d[e]/2 nam = c(nam[-ind], as.character(-l)) parentNodes = c(parentNodes,-l,-l) childNodes = c(childNodes,edge[e1],edge[e2]) height = c(height, len, len) edge = c(edge[-ind], -l) w = (data[[e1]] * data[[e2]]) %*% c(1, 1, 1, 1) w = which(w == 0) newDat = data[[e1]] * data[[e2]] newDat[w, ] = data[[e1]][w, ] + data[[e2]][w, ] data = data[-c(e1,e2)] data[[l]] = newDat if (l > 1) { D = as.matrix(D[, -ind]) D = D[-ind, ] dv = numeric(l - 1) for (i in 1:(l - 1)) { w = (data[[i]] * data[[l]]) %*% c(1, 1, 1, 1) dv[i] = sum(weight[w==0]) } D = cbind(D, dv) D = rbind(D, 0) } } tree <- list(edge = cbind(as.character(parentNodes),as.character(childNodes)),tip.label=tip.label) class(tree) <- "phylo" tree <- old2new.phylo(tree) reorder(tree) } # # Distance Matrix methods # # as.Matrix, sparse = TRUE, designTree <- function(tree, method="unrooted", sparse=FALSE, ...){ if (!is.na(pmatch(method, "all"))) method <- "unrooted" METHOD <- c("unrooted", "rooted") method <- pmatch(method, METHOD) if (is.na(method)) stop("invalid method") if (method == -1) stop("ambiguous method") if(!is.rooted(tree) & method==2) stop("tree has to be rooted") if(method==1){ X <- designUnrooted(tree,...) if(sparse) X = Matrix(X) } if(method==2) X <- designUltra(tree, sparse=sparse,...) X } # splits now work designUnrooted = function (tree, order = NULL) { if(inherits(tree, "phylo")){ if (is.rooted(tree)) tree = unroot(tree) p = bipartition(tree) } if(inherits(tree, "splits")) p <- as.matrix(tree) if (!is.null(order)) p = p[, order] m = dim(p)[2] ind = rowSums(p) p=p[ind!=m,] n = dim(p)[1] res = matrix(0, (m - 1) * m/2, n) k = 1 for (i in 1:(m - 1)) { for (j in (i + 1):m) { res[k, ] = p[, i] != p[, j] k = k + 1 } } if(inherits(tree, "phylo"))colnames(res) = paste(tree$edge[, 1], tree$edge[, 2], sep = "<->") res } designUltra <- function (tree, sparse=TRUE) { if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree = reorder(tree, "postorder") leri = allChildren(tree) bp = bip(tree) n = length(tree$tip.label) l = tree$Nnode nodes = integer(l) k = 1L u=numeric( n * (n - 1)/2) v=numeric( n * (n - 1)/2) m = 1L for (i in 1:length(leri)) { if (length(leri[[i]])>1) { if(length(leri[[i]])==2)ind = getIndex(bp[[leri[[i]][1] ]], bp[[leri[[i]][2] ]], n) else { ind=NULL le=leri[[i]] nl = length(le) for(j in 1:(nl-1)) ind =c(ind, getIndex(bp[[le[j] ]], unlist(bp[ le[(j+1):nl] ]), n)) } li = length(ind) v[m: (m+li-1)]=k u[m: (m+li-1)]=ind nodes[k]=i m = m+li k = k + 1L } } if(sparse) X = sparseMatrix(i=u,j=v, x=2L) else{ X = matrix(0L, n * (n - 1)/2, l) X[cbind(u,v)]=2L } colnames(X) = nodes attr(X, "nodes") = nodes X } designUnrooted2 <- function (tree, sparse=TRUE) { if (is.null(attr(tree, "order")) || attr(tree, "order") != "postorder") tree = reorder(tree, "postorder") leri = allChildren(tree) bp = bip(tree) n = length(tree$tip.label) l = tree$Nnode nodes = integer(l) nTips = as.integer(length(tree$tip.label)) k = nTips u=numeric( n * (n - 1)/2) v=numeric( n * (n - 1)/2) z=numeric( n * (n - 1)/2) y=numeric( n * (n - 1)/2) p=1L m = 1L for (i in 1:length(leri)) { if (length(leri[[i]]) > 1) { if(length(leri[[i]])==2){ ind = getIndex(bp[[leri[[i]][1] ]], bp[[leri[[i]][2] ]], n) ytmp = rep(bp[[leri[[i]][1] ]], each = length(bp[[leri[[i]][2] ]])) ztmp = rep(bp[[leri[[i]][2] ]], length(bp[[leri[[i]][1] ]])) } else { # browser() ind=NULL le=leri[[i]] nl = length(le) ytmp=NULL ztmp=NULL for(j in 1:(nl-1)){ bp1 = bp[[le[j] ]] bp2 = unlist(bp[le[(j+1):nl] ]) ind =c(ind, getIndex(bp1, unlist(bp2), n)) ytmp = c(ytmp, rep(bp1, each = length(bp2))) ztmp = c(ztmp, rep(bp2, length(bp1))) } } li = length(ind) v[m: (m+li-1)]=k u[m: (m+li-1)]=ind y[m: (m+li-1)]=ytmp z[m: (m+li-1)]=ztmp nodes[p]=i m = m+li k = k + 1L p = p + 1L } } jj = c(y,z) #[ind],v) ii = c(u,u) #[ind],u) ind = (jj < nTips) jj = c(jj[ind], v) ii = c(ii[ind], u) l1 = length(u) l2 = sum(ind) x= rep(c(-1L,2L), c(l2, l1)) X = sparseMatrix(i=ii,j=jj, x=x) if(!sparse){ X = as.matrix(X) } nodes = c(1:(nTips-1L), nodes) colnames(X) = nodes attr(X, "nodes") = nodes X } nnls.tree <- function(dm, tree, rooted=FALSE, trace=1){ if(is.rooted(tree) & rooted==FALSE){ tree = unroot(tree) warning("tree was rooted, I unrooted the tree!") } tree = reorder(tree, "postorder") dm = as.matrix(dm) k = dim(dm)[1] labels = tree$tip.label dm = dm[labels,labels] y = dm[lower.tri(dm)] #computing the design matrix from the tree if(rooted) X = designUltra(tree) else X = designUnrooted2(tree) lab = attr(X, "nodes") # na.action if(any(is.na(y))){ ind = which(is.na(y)) X = X[-ind,,drop=FALSE] y= y[-ind] } # LS solution Dmat <- crossprod(X) # cross-product computations dvec <- crossprod(X, y) betahat <- as.vector(solve(Dmat, dvec)) betahattmp = betahat bhat = numeric(max(tree$edge)) bhat[as.integer(lab)] = betahat betahat = bhat[tree$edge[,1]] - bhat[tree$edge[,2]] if(!any(betahat<0)){ # if(!rooted){ RSS = sum((y-(X%*%betahattmp))^2) if(trace)print(paste("RSS:", RSS)) attr(tree, "RSS") = RSS # } tree$edge.length = betahat return(tree) } # non-negative LS n = dim(X)[2] l = nrow(tree$edge) lab = attr(X, "nodes") # vielleicht solve.QP.compact ind1 = match(tree$edge[,1], lab) ind2 = match(tree$edge[,2], lab) # Amat = matrix(0, n, l) # Amat[cbind(ind1, 1:l)] = 1 # Amat[cbind(ind2, 1:l)] = -1 # betahat <- quadprog::solve.QP(as.matrix(Dmat),as.vector(dvec),Amat)$sol Amat <- matrix(0, 2, l) Amat[1,] <- 1 Amat[2,] <- -1 Aind <- matrix(0L, 3, l) Aind[1,] <- 2L Aind[2,] <- as.integer(ind1) Aind[3,] <- as.integer(ind2) if(any(is.na(Aind))){ na_ind <- which(is.na(Aind), arr.ind = TRUE) Aind[is.na(Aind)] <- 0L for(i in 1:nrow(na_ind)) Aind[1, na_ind[i, 2]] <- Aind[1, na_ind[i, 2]] - 1L } betahat <- quadprog::solve.QP.compact(as.matrix(Dmat),as.vector(dvec),Amat, Aind)$sol # quadratic programing solving # if(!rooted){ RSS = sum((y-(X%*%betahat))^2) if(trace)print(paste("RSS:", RSS)) attr(tree, "RSS") = RSS # } bhat = numeric(max(tree$edge)) bhat[as.integer(lab)] = betahat betahat = bhat[tree$edge[,1]] - bhat[tree$edge[,2]] tree$edge.length = betahat tree } nnls.phylo <- function(x, dm, rooted=FALSE, trace=0){ nnls.tree(dm, x, rooted, trace=trace) } nnls.splits <- function(x, dm, trace=0){ labels=attr(x, "labels") dm = as.matrix(dm) k = dim(dm)[1] dm = dm[labels,labels] y = dm[lower.tri(dm)] x = SHORTwise(x, k) l <- lengths(x) if(any(l==0)) x = x[-which(l==0)] X = splits2design(x) if(any(is.na(y))){ ind = which(is.na(y)) X = X[-ind,,drop=FALSE] y= y[-ind] } Dmat <- crossprod(X) # cross-product computations dvec <- crossprod(X, y) betahat <- as.vector(solve(Dmat, dvec)) if(!any(betahat<0)){ RSS = sum((y-(X%*%betahat))^2) if(trace)print(paste("RSS:", RSS)) attr(x, "RSS") = RSS attr(x, "weights") = betahat return(x) } n = dim(X)[2] int <- lengths(x) # Amat = diag(n) # (int) # betahat <- quadprog::solve.QP(as.matrix(Dmat),as.vector(dvec),Amat)$sol # quadratic programing solving # faster version Amat <- matrix(1, 1, n) Aind <- matrix(0L, 2L, n) Aind[1,] <- 1L Aind[2,] <- as.integer(1L:n) betahat <- quadprog::solve.QP.compact(as.matrix(Dmat),as.vector(dvec),Amat, Aind)$sol RSS = sum((y-(X%*%betahat))^2) ind = (betahat > 1e-8) | int==1 x = x[ind] attr(x, "weights") = betahat[ind] if(trace)print(paste("RSS:", RSS)) attr(x, "RSS") = RSS x } nnls.networx <- function(x, dm){ # spl <- attr(x, "splits") spl <- x$splits spl2 <- nnls.splits(spl, dm) weight <- attr(spl, "weight") weight[] <- 0 weight[match(spl2, spl)] = attr(spl2, "weight") # attr(attr(x, "splits"), "weight") <- weight attr(x$splits, "weight") <- weight x$edge.length = weight[x$splitIndex] x } designSplits <- function (x, splits = "all", ...) { if (!is.na(pmatch(splits, "all"))) splits <- "all" if(inherits(x, "splits")) return(designUnrooted(x)) SPLITS <- c("all", "star") #,"caterpillar") splits <- pmatch(splits, SPLITS) if (is.na(splits)) stop("invalid splits method") if (splits == -1) stop("ambiguous splits method") if(splits==1) X <- designAll(x) if(splits==2) X <- designStar(x, ...) return(X) } # add return splits=FALSE designAll <- function(n, add.split=FALSE){ Y = matrix(0L, n*(n-1)/2, n) k = 1 for(i in 1:(n-1)){ for(j in (i+1):n){ Y[k,c(i,j)]=1L k=k+1L } } m <- n-1L X <- matrix(0L, m+1, 2^m) for(i in 1:m) X[i, ] <- rep(rep(c(0L,1L), each=2^(i-1)),2^(m-i)) X <- X[,-1] if(!add.split) return((Y%*%X)%%2) list(X=(Y%*%X)%%2,Splits=t(X)) } # faster sparse version designStar = function(n, sparse=TRUE){ # res=NULL # for(i in 1:(n-1)) res = rbind(res,cbind(matrix(0,(n-i),i-1),1,diag(n-i))) res <- stree(n) %>% as.splits %>% splits2design if(!sparse) return(as.matrix(res)) res } phangorn/R/SOWH.R0000644000175100001440000000602612761653173013227 0ustar hornikusersSOWH.test <- function(x, n=100, restricted=list(optNni=FALSE), optNni=TRUE, trace = 1, ...){ res = matrix(NA, n, 2) extras <- match.call(expand.dots = FALSE)$... optU = list (optNni = optNni, optBf = FALSE, optQ = FALSE, optInv = FALSE, optGamma = FALSE, optEdge = TRUE, optRate = FALSE, optRooted = FALSE, model = NULL) if(!is.null(extras)){ namAll = names(extras) for(i in 1: length(extras))optU[[namAll[i]]] = extras[[i]] } optR = optU namR = names(restricted) for(i in 1: length(namR))optR[[namR[i]]] = restricted[[i]] restr <- optim.pml(x, optNni = optR$optNni, optBf = optR$optBf, optQ = optR$optQ, optInv = optR$optInv, optGamma = optR$optGamma, optEdge = optR$optEdge, optRate = optR$optRate, optRooted = optR$optRooted, model = optR$model, pml.control(trace = trace-1L)) unrestr <- optim.pml(restr, optNni = optU$optNni, optBf = optU$optBf, optQ = optU$optQ, optInv = optU$optInv, optGamma = optU$optGamma, optEdge = optU$optEdge, optRate = optU$optRate, optRooted = optU$optRooted, model = optU$model, pml.control(trace = trace-1L)) for(i in 1:n){ if(trace>0) cat("iteration: ", i, "\n") newData <- simSeq(restr) restrTmp <- update(restr, data=newData) unrestrTmp <- restrTmp # update(unrestr, data=newData) restrTmp <- optim.pml(restrTmp, optNni = optR$optNni, optBf = optR$optBf, optQ = optR$optQ, optInv = optR$optInv, optGamma = optR$optGamma, optEdge = optR$optEdge, optRate = optR$optRate, optRooted = optR$optRooted, model = optR$model, pml.control(trace = trace-1L)) unrestrTmp <- optim.pml(unrestrTmp, optNni = optU$optNni, optBf = optU$optBf, optQ = optU$optQ, optInv = optU$optInv, optGamma = optU$optGamma, optEdge = optU$optEdge, optRate = optU$optRate, optRooted = optU$optRooted, model = optU$model, pml.control(trace = trace-1L)) res[i, 1] <- logLik(restrTmp) res[i, 2] <- logLik(unrestrTmp) } result = list("LL"=res, "restr" = restr, "unrestr" = unrestr) class(result) = "SOWH" result } print.SOWH <- function(x, digits = 4L, ...){ resLL = logLik(x$restr) unresLL = logLik(x$unrestr) diffLL = unresLL - resLL pval <- sum( (x$LL[,2] - x$LL[,1]) > diffLL) / nrow(x$LL) res = c(resLL, unresLL, diffLL, pval) names(res) = c("ln L restr", "ln L unrestr", "Diff ln L", "p-value") print(res, digits=digits) invisible(x) } summary.SOWH <- function(object, digits = 4L, plot=TRUE, ...){ resLL = logLik(object$restr) unresLL = logLik(object$unrestr) diffLL = unresLL - resLL pval <- sum( (object$LL[,2] - object$LL[,1]) > diffLL) / nrow(object$LL) res = c(resLL, unresLL, diffLL, pval) names(res) = c("ln L restr", "ln L unrestr", "Diff ln L", "p-value") print(res, digits=digits) if(plot){ d = object$LL[,2] - object$LL[,1] hist( d, freq=FALSE, xlim=c(0, 1.2 * max(d, diffLL))) abline(v=diffLL, col="red") } invisible(object) } phangorn/R/phylo.R0000644000175100001440000043556113020064142013571 0ustar hornikusers edgeLengthIndex <- function(child, parent, nTips){ fun = function(child, parent, nTips){ if(child <= nTips) return(child) else{ if(child < parent) return(parent) return(child) } } if (length(child)==1) return(fun(child, parent, nTips)) else { res = integer(length(child)) for(i in 1:length(child))res[i]=fun(child[i], parent[i], nTips) return(res) } } # # Maximum likelihood estimation # discrete.gamma <- function (alpha, k) { if (k == 1) return(1) quants <- qgamma((1:(k - 1))/k, shape = alpha, rate = alpha) diff( c(0, pgamma(quants * alpha, alpha + 1),1)) * k } # allow transition probs of zero (added o) optimQ <- function (tree, data, Q=rep(1,6), subs=rep(1,length(Q)), trace = 0, ...) { m = length(Q) n = max(subs) o = min(subs) ab = numeric(n) # ab = log(Q[match(1:n, subs)]) for(i in 1:n) ab[i]=log(Q[which(subs==i)[1]]) fn = function(ab, tree, data, m, n, o, subs,...) { Q = numeric(m) for(i in 1:n)Q[subs==i] = ab[i] if(o < 0)Q[subs<0] = -Inf pml.fit(tree, data, Q = exp(Q),...)# Q^2, ...) } res = optim(par = ab, fn = fn, gr = NULL, method = "L-BFGS-B", lower = -Inf, upper = 10, control = list(fnscale = -1, maxit = 25, trace = trace), tree = tree, data = data, m=m, n=n, o=o, subs=subs,...) Q = rep(1, m) for(i in 1:n) Q[subs==i] = exp(res[[1]][i]) if(o < 0)Q[subs<0] = 0 res[[1]] = Q res } optimCodon <- function (tree, data, Q=rep(1,1830), subs=rep(1,length(Q)), syn = rep(0, length(Q)), trace = 0L, ab = c(0,0), optK=TRUE, optW=TRUE, ...) { m = length(Q) n = 1L # max(subs) fn = function(ab, tree, data, m, n, subs, syn, optK, optW, ...) { Q = numeric(m) Q[subs==1] = 0 # transversion if(optK) Q[subs==2] = ab[1] # transition else Q[subs==2] = 0 if(optW) Q[syn==1] = Q[syn==1] + ab[2] # ab[n+1] dnds Q[syn<0] = -Inf pml.fit(tree, data, Q = exp(Q),...)# Q^2, ...) } res = optim(par = ab, fn = fn, gr = NULL, method = "L-BFGS-B", lower = -Inf, upper = Inf, control = list(fnscale = -1, maxit = 25, trace = trace), tree = tree, data = data, m=m, n=n, subs=subs, syn=syn, optK=optK, optW=optW, ...) ab = exp(res[[1]]) Q[subs==1] = 1 # transversion if(optK) Q[subs==2] = ab[1] # transition else{ Q[subs==2] = 1 ab[1] = 1 } if(optW) Q[syn==1] = Q[syn==1] * ab[2] # dnds else ab[2] = 1 Q[syn<0] = 0 res[[5]] = ab res[[1]] = Q res } subsChoice <- function(type=c("JC", "F81", "K80", "HKY", "TrNe", "TrN", "TPM1", "K81", "TPM1u", "TPM2", "TPM2u", "TPM3", "TPM3u", "TIM1e", "TIM1", "TIM2e", "TIM2", "TIM3e", "TIM3", "TVMe", "TVM", "SYM", "GTR")){ type = match.arg(type) switch(type, JC = list(optQ=FALSE, optBf=FALSE, subs=c(0, 0, 0, 0, 0, 0)), F81 = list(optQ=FALSE, optBf=TRUE, subs=c(0, 0, 0, 0, 0, 0)), K80 = list(optQ=TRUE, optBf=FALSE, subs=c(0, 1, 0, 0, 1, 0)), HKY = list(optQ=TRUE, optBf=TRUE, subs=c(0, 1, 0, 0, 1, 0)), TrNe = list(optQ=TRUE, optBf=FALSE, subs=c(0, 1, 0, 0, 2, 0)), TrN = list(optQ=TRUE, optBf=TRUE, subs=c(0, 1, 0, 0, 2, 0)), TPM1 = list(optQ=TRUE, optBf=FALSE, subs=c(0, 1, 2, 2, 1, 0)), K81 = list(optQ=TRUE, optBf=FALSE, subs=c(0, 1, 2, 2, 1, 0)), TPM1u = list(optQ=TRUE, optBf=TRUE, subs=c(0, 1, 2, 2, 1, 0)), TPM2 = list(optQ=TRUE, optBf=FALSE, subs=c(1, 2, 1, 0, 2, 0)), TPM2u = list(optQ=TRUE, optBf=TRUE, subs=c(1, 2, 1, 0, 2, 0)), TPM3 = list(optQ=TRUE, optBf=FALSE, subs=c(1, 2, 0, 1, 2, 0)), TPM3u = list(optQ=TRUE, optBf=TRUE, subs=c(1, 2, 0, 1, 2, 0)), TIM1e = list(optQ=TRUE, optBf=FALSE, subs=c(0, 1, 2, 2, 3, 0)), TIM1 = list(optQ=TRUE, optBf=TRUE, subs=c(0, 1, 2, 2, 3, 0)), TIM2e = list(optQ=TRUE, optBf=FALSE, subs=c(1, 2, 1, 0, 3, 0)), TIM2 = list(optQ=TRUE, optBf=TRUE, subs=c(1, 2, 1, 0, 3, 0)), TIM3e = list(optQ=TRUE, optBf=FALSE, subs=c(1, 2, 0, 1, 3, 0)), TIM3 = list(optQ=TRUE, optBf=TRUE, subs=c(1, 2, 0, 1, 3, 0)), TVMe = list(optQ=TRUE, optBf=FALSE, subs=c(1, 2, 3, 4, 2, 0)), TVM = list(optQ=TRUE, optBf=TRUE, subs=c(1, 2, 3, 4, 2, 0)), SYM = list(optQ=TRUE, optBf=FALSE, subs=c(1, 2, 3, 4, 5, 0)), GTR = list(optQ=TRUE, optBf=TRUE, subs=c(1, 2, 3, 4, 5, 0)) ) } optimGamma = function(tree, data, shape=1, k=4,...){ fn = function(shape, tree, data, k,...)pml.fit(tree, data, shape=shape, k=k,...) res = optimize(f=fn, interval = c(0.1, 500), lower = 0.1, upper = 500, maximum = TRUE, tol = .01, tree=tree, data=data, k=k,...) res } optimInv = function(tree, data, inv=0.01, INV=NULL, ll.0=NULL,...){ fn = function(inv, tree, data,...)pml.fit(tree, data, inv=inv, INV=INV, ll.0=NULL,...) res = optimize(f=fn, interval = c(0,1), lower = 0, upper = 1, maximum = TRUE, tol = .0001, tree=tree, data=data,...) res } # changed to c(-10,10) from c(-5,5) optimRate <- function(tree, data, rate=1, ...){ fn <- function(rate, tree, data, ...) pml.fit(tree, data, rate=exp(rate), ...) res <- optimize(f = fn, interval = c(-10, 10), tree = tree, data = data, ..., maximum = TRUE) res[[1]] <- exp(res[[1]]) res } optimBf = function(tree, data, bf=c(.25,.25,.25,.25), trace=0,...){ l=length(bf) nenner = 1/bf[l] lbf = log(bf * nenner) lbf = lbf[-l] fn = function(lbf, tree, data,...){ bf = exp(c(lbf,0)) bf = bf/sum(bf) pml.fit(tree, data, bf=bf, ...) } res = optim(par=lbf, fn=fn, gr=NULL, method="Nelder-Mead", control=list(fnscale=-1, maxit=500, trace=trace),tree=tree, data=data,...) bf = exp(c(res[[1]],0)) bf = bf/sum(bf) result = list(bf=bf, loglik = res[[2]]) result } optimW = function(fit,...){ w = fit$w g = fit$g siteLik = fit$siteLik k = length(w) l = dim(siteLik[[1]])[1] x=matrix(0,l,k) for(i in 1:k)x[,i] = rowSums(siteLik[[i]]) weight = fit$weight nenner = 1/w[k] eta = log(w * nenner) eta = eta[-k] fn = function(eta,x,g,weight){ eta = c(eta,0) p = exp(eta)/sum(exp(eta)) res = x%*%p res = sum(weight*log(res)) * (1 + abs(sum(p*g) - 1)) res } res = optim(eta, fn = fn, method = "Nelder-Mead", control=list(fnscale=-1, reltol = 1e-12),gr=NULL, x=x,g=g, weight=weight) p = exp(c(res$par,0)) p = p/sum(p) result = list(par = p, value = res$value) result } #predict.pml <- function(object, newdata,...) sum(object$site * newdata) logLik.pml <- function(object,...){ res <- object$logLik attr(res,"df") <- object$df class(res) <- "logLik" res } AICc <- function (object, ...) UseMethod("AICc") AICc.pml <- function(object, ...){ n = sum(object$weight) k = object$df if(k >= (n-1)) return(NaN) res = AIC(object) res + (2*k*(k+1))/(n-k-1) } BIC.pml <- function(object, ...){ res = AIC(object, k=log(sum(object$weight))) res } anova.pml <- function (object, ...) { X <- c(list(object), list(...)) df <- sapply(X, "[[", "df") ll <- sapply(X, "[[", "logLik") dev <- c(NA, 2 * diff(ll)) ddf <- c(NA, diff(df)) table <- data.frame(ll, df, ddf, dev, pchisq(dev, ddf, lower.tail = FALSE)) dimnames(table) <- list(1:length(X), c("Log lik.", "Df", "Df change", "Diff log lik.", "Pr(>|Chi|)")) structure(table, heading = "Likelihood Ratio Test Table", class = c("anova", "data.frame")) } #vcov.pml <- function(object, obs=FALSE,...){ # if(obs) FI = score4(object)[[2]] # else FI = score(object,FALSE)[[2]] # l = dim(FI)[1] # res = try(solve(FI)) # if(class(res) == "try-error"){ # cat("Covariance is ill-conditioned !! \n") # res = solve(FI + diag(l)* 1e-8) # } # res #} vcov.pml <- function(object, ...){ FI = score(object,FALSE)[[2]] l = dim(FI)[1] res = try(solve(FI)) if(class(res) == "try-error"){ cat("Covariance is ill-conditioned !! \n") res = solve(FI + diag(l)* 1e-8) } res } getd2P <- function(el, eig=edQt(), g=1.0){ n <- length(eig$values) res <- .Call("getd2PM",eig,as.integer(n),as.double(el),as.double(g)) attr(res,"dim") <- c(length(g),length(el)) res } getdP <- function(el, eig=edQt(), g=1.0){ n <- length(eig$values) res <- .Call("getdPM",eig,as.integer(n),as.double(el),as.double(g)) attr(res,"dim") <- c(length(g),length(el)) res } # version without transformation (used for vcov) getdP2 <- function(el, eig=edQt(), g=1.0){ n <- length(eig$values) res <- .Call("getdPM2",eig,as.integer(n),as.double(el),as.double(g)) attr(res,"dim") <- c(length(g),length(el)) res } # version without transformation getd2P2 <- function(el, eig=edQt(), g=1.0){ n <- length(eig$values) res <- .Call("getd2PM2",eig,as.integer(n),as.double(el),as.double(g)) attr(res,"dim") <- c(length(g),length(el)) res } getP <- function(el, eig=edQt(), g=1.0){ n <- length(eig$values) res <- .Call("getPM", eig, as.integer(n), as.double(el), as.double(g)) attr(res, "dim") <- c(length(g), length(el)) res } lli <- function (data, tree=NULL, ...) { contrast <- attr(data, "contrast") nr <- attr(data, "nr") nc <- attr(data, "nc") nco <- as.integer(dim(contrast)[1]) if(!is.null(tree)) data <- subset(data, tree$tip.label) .Call("invSites", data, as.integer(nr), as.integer(nc), contrast, as.integer(nco)) } edQt <- function (Q = c(1, 1, 1, 1, 1, 1), bf = c(0.25, 0.25, 0.25, 0.25)) { l = length(bf) res = matrix(0, l, l) res[lower.tri(res)] = Q res = res + t(res) res = res * bf res2 = res * rep(bf, each = l) diag(res) = -colSums(res) res = res/sum(res2) e = eigen(res, FALSE) e$inv = solve.default(e$vec) e } edQ <- function(Q=c(1,1,1,1,1,1), bf=c(0.25,.25,.25,.25)){ l=length(bf) res = matrix(0, l, l) res[lower.tri(res)] = Q res = res+t(res) res = res * rep(bf,each=l) diag(res) = -rowSums(res) res2 = res * rep(bf,l) diag(res2)=0 res = res/sum(res2) e = eigen(res, FALSE) e$inv = solve.default(e$vec) e } edQ2 <- function(Q){ res = Q l=dim(Q)[1] diag(res) = 0 diag(res) = -rowSums(res) e = eigen(res, FALSE) e$inv = solve.default(e$vec) e } pml.free <- function(){ .C("ll_free") # rm(.INV, .iind, envir = parent.frame()) } pml.init <- function(data, k=1L){ nTips <- length(data) nr <- attr(data, "nr") nc <- attr(data, "nc") .C("ll_init", as.integer(nr), as.integer(nTips), as.integer(nc), as.integer(k)) # INV <- lli(data) #, tree # .iind <<- which((INV %*% rep(1, nc)) > 0) # .INV <<- Matrix(INV, sparse=TRUE) # assign(".iind", which((INV %*% rep(1, nc)) > 0), envir=parent.frame()) # assign(".INV", Matrix(INV, sparse=TRUE), envir=parent.frame()) } pml.free2 <- function(){.C("ll_free2")} pml.init2 <- function(data, k=1L){ nTips <- length(data) nr <- attr(data, "nr") nc <- attr(data, "nc") weight <- attr(data, "weight") .C("ll_init2", as.integer(unlist(data, use.names=FALSE)), as.double(weight), as.integer(nr), as.integer(nTips), as.integer(nc), as.integer(k)) } fn.quartet <- function(old.el, eig, bf, dat, g=1, w=1, weight, ll.0) { l= length(dat[,1]) ll = ll.0 res = vector("list", 2*l) tmp1 = NULL tmp2 = NULL attr(res,"dim") = c(l,2) for(j in 1:l){ P = getP(old.el, eig, g[j]) tmp1 = (dat[[j,1]] %*% P[[1]]) *(dat[[j,2]] %*% P[[2]]) tmp2 = (dat[[j,3]] %*% P[[3]]) * (dat[[j,4]] %*% P[[4]]) res[[j,1]] = tmp1 * (tmp2 %*% P[[5]]) res[[j,2]] = tmp2 ll = ll + res[[j,1]] %*% (w[j]*bf) } l0 = sum(weight * log(ll)) list(ll=l0,res=res) } fn.quartet2 <- function (old.el, eig, bf, dat1, dat2, dat3, dat4, g = 1, w = 1, weight, ll.0, contrast, ext) { l = length(w) ll = ll.0 res = vector("list", 2 * l) tmp1 = NULL tmp2 = NULL attr(res, "dim") = c(l, 2) for (j in 1:l) { P = getP(old.el, eig, g[j]) if (ext[1] == FALSE && ext[2] == FALSE) tmp1 = (dat1[[j]] %*% P[[1]]) * (dat2[[j]] %*% P[[2]]) if (ext[1] == FALSE && ext[2] == TRUE) tmp1 = (dat1[[j]] %*% P[[1]]) * (contrast %*% P[[2]])[dat2, ] if (ext[1] == TRUE && ext[2] == FALSE) tmp1 = (contrast %*% P[[1]])[dat1, ] * (dat2[[j]] %*% P[[2]]) if (ext[1] == TRUE && ext[2] == TRUE) tmp1 = (contrast %*% P[[1]])[dat1, ] * (contrast %*% P[[2]])[dat2, ] if (ext[3] == FALSE && ext[4] == FALSE) tmp2 = (dat3[[j]] %*% P[[3]]) * (dat4[[j]] %*% P[[4]]) if (ext[3] == FALSE && ext[4] == TRUE) tmp2 = (dat3[[j]] %*% P[[3]]) * (contrast %*% P[[4]])[dat4, ] if (ext[3] == TRUE && ext[4] == FALSE) tmp2 = (contrast %*% P[[3]])[dat3, ] * (dat4[[j]] %*% P[[4]]) if (ext[3] == TRUE && ext[4] == TRUE) tmp2 = (contrast %*% P[[3]])[dat3, ] * (contrast %*% P[[4]])[dat4, ] res[[j, 1]] = tmp1 * (tmp2 %*% P[[5]]) res[[j, 2]] = tmp2 ll = ll + res[[j, 1]] %*% (w[j] * bf) } l0 = sum(weight * log(ll)) list(ll = l0, res = res) } optim.quartet2 <- function (old.el, eig, bf, dat1, dat2, dat3, dat4, g = 1, w = 1, weight, ll.0 = weight * 0, control = list(eps = 1e-08, maxit = 5, trace = 0), llcomp = -Inf, evi, contrast, contrast2, ext = c(FALSE, FALSE, FALSE, FALSE)) { eps = 1 iter = 0 while (eps > control$eps && iter < control$maxit) { tmp <- fn.quartet2(old.el = old.el, eig = eig, bf = bf, dat1 = dat1, dat2 = dat2, dat3 = dat3, dat4 = dat4, g = g, w = w, weight = weight, ll.0 = ll.0, contrast=contrast, ext = ext) old.ll = tmp$ll el1 <- fs3(old.el[1], eig, tmp$res[, 1], dat1, weight, g = g, w = w, bf = bf, ll.0 = ll.0, contrast=contrast, contrast2=contrast2, evi=evi, ext = ext[1], getA=TRUE, getB=FALSE) el2 <- fs3(old.el[2], eig, el1[[2]], dat2, weight, g = g, w = w, bf = bf, ll.0 = ll.0, contrast=contrast, contrast2=contrast2, evi=evi, ext = ext[2], getA=TRUE, getB=FALSE) el5 <- fs3(old.el[5], eig, el2[[2]], tmp$res[, 2], weight, g = g, w = w, bf = bf, ll.0 = ll.0, contrast=contrast, contrast2=contrast2, evi=evi, ext = 0L, getA=FALSE, getB=TRUE) el3 <- fs3(old.el[3], eig, el5[[3]], dat3, weight, g = g, w = w, bf = bf, ll.0 = ll.0, contrast=contrast, contrast2=contrast2, evi=evi, ext = ext[3], getA=TRUE, getB=FALSE) el4 <- fs3(old.el[4], eig, el3[[2]], dat4, weight, g = g, w = w, bf = bf, ll.0 = ll.0, contrast=contrast, contrast2=contrast2, evi=evi, ext = ext[4], getA=FALSE, getB=FALSE) old.el[1] = el1[[1]] old.el[2] = el2[[1]] old.el[3] = el3[[1]] old.el[4] = el4[[1]] old.el[5] = el5[[1]] iter = iter + 1 ll = el4[[4]] eps = (old.ll - ll)/ll if (ll < llcomp) return(list(old.el, ll)) old.ll = ll } list(old.el, ll) } pml.nni.old <- function (tree, data, w, g, eig, bf, ll.0, ll, ...) { k = length(w) INDEX <- indexNNI(tree) rootEdges <- attr(INDEX,"root") .dat <- NULL data = getCols(data, tree$tip.label) parent = tree$edge[,1] child = tree$edge[,2] weight = attr(data, "weight") datp = rnodes(tree, data, w, g, eig, bf) contrast <- attr(data, "contrast") contrast2 <- contrast %*% eig[[2]] evi = (t(eig[[3]]) * bf) nTips = length(tree$tip.label) evector <- numeric(max(parent)) evector[child] <- tree$edge.length m <- dim(INDEX)[1] loglik = numeric(2*m) edgeMatrix <- matrix(0, 2*m, 5) l = length(datp[, 1]) for(i in 1:m){ ei = INDEX[i,] el0 = evector[INDEX[i,]] ext = ei[1:4] < nTips+1L if (!(ei[5] %in% rootEdges)) dat1 = datp[, ei[1], drop = FALSE] else{ if(ext[1]) dat1 = data[[ ei[1] ]] else dat1 = .dat[, ei[1], drop=FALSE] } if(ext[2]) dat2 = data[[ ei[2] ]] else dat2 = .dat[, ei[2], drop=FALSE] if(ext[3]) dat3 = data[[ ei[3] ]] else dat3 = .dat[, ei[3], drop=FALSE] if(ext[4]) dat4 = data[[ ei[4] ]] else dat4 = .dat[, ei[4], drop=FALSE] new1 <- optim.quartet2(el0[c(1, 3, 2, 4, 5)], eig, bf, dat1, dat3, dat2, dat4, g, w, weight, ll.0, llcomp=ll, evi=evi, contrast=contrast, contrast2=contrast2, ext=ext[c(1, 3, 2, 4)]) new2 <- optim.quartet2(el0[c(1, 4, 3, 2, 5)], eig, bf, dat1, dat4, dat3, dat2, g, w, weight, ll.0, llcomp=ll, evi=evi, contrast=contrast, contrast2=contrast2, ext=ext[c(1, 4, 3, 2)]) loglik[(2*i)-1]=new1[[2]] loglik[(2*i)]=new2[[2]] edgeMatrix[(2*i)-1,]=new1[[1]] edgeMatrix[(2*i),]=new2[[1]] } swap <- 0 eps0 <- 1e-6 candidates <- loglik > ll + eps0 nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) nTips <- as.integer(length(tree$tip.label)) # on.exit(.C("ll_free")) # .C("ll_init", nr, nTips, nc, as.integer(k)) # cat("candidates", sum(candidates), "\n") while(any(candidates)){ ind = which.max(loglik) loglik[ind]=-Inf if( ind %% 2 ) swap.edge = c(2,3) else swap.edge = c(2,4) tree2 <- changeEdge(tree, INDEX[(ind+1)%/%2,swap.edge], INDEX[(ind+1)%/%2,], edgeMatrix[ind,]) test <- pml.fit(tree2, data, bf = bf, k=k, g=g, w=w, eig=eig, ll.0=ll.0, ...) if(test <= ll + eps0) candidates[ind] = FALSE if(test > ll + eps0) { ll = test swap=swap+1 tree <- tree2 indi <- which(rep(colSums(apply(INDEX,1,match,INDEX[(ind+1)%/%2,],nomatch=0))>0,each=2)) candidates[indi] <- FALSE loglik[indi] <- -Inf } } list(tree=tree, ll=ll, swap=swap) } rnodes <- function (tree, data, w, g, eig, bf) { if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") data = getCols(data, tree$tip.label) q = length(tree$tip.label) node <- tree$edge[, 1] edge <- tree$edge[, 2] m = length(edge) + 1 # max(edge) l = length(w) dat = vector(mode = "list", length = m*l) dim(dat) <- c(l,m) tmp = length(data) # for(i in 1:length(w))dat[i,1:tmp]=new2old.phyDat(data) # # dat[1,1:tmp] <- data vielleicht gebraucht el <- tree$edge.length P <- getP(el, eig, g) nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) node = as.integer(node - min(node)) edge = as.integer(edge - 1) nTips = as.integer(length(tree$tip.label)) mNodes = as.integer(max(node) + 1) contrast = attr(data, "contrast") nco = as.integer(dim(contrast)[1]) for(i in 1:l)dat[i,(q + 1):m] <- .Call("LogLik2", data, P[i,], nr, nc, node, edge, nTips, mNodes, contrast, nco) parent <- tree$edge[, 1] child <- tree$edge[, 2] nTips = min(parent) - 1 datp = vector("list", m) dat2 = vector("list", m * l) dim(dat2) <- c(l,m) for(i in 1:l){ datp[(nTips + 1)] = dat[i,(nTips + 1)] for (j in (m - 1):1) { if (child[j] > nTips){ tmp2 = (datp[[parent[j]]]/(dat[[i,child[j]]] %*% P[[i,j]])) datp[[child[j]]] = (tmp2 %*% P[[i,j]]) * dat[[i,child[j]]] dat2[[i, child[j]]] = tmp2 } } } assign(".dat", dat, envir = parent.frame(n = 1)) dat2 } score <- function (fit, transform=TRUE) { tree = fit$tree child <- tree$edge[, 2] l = length(child) sc = numeric(l) weight = as.numeric(fit$weight) f <- drop(exp(fit$site)) dl = dl(fit, transform) dl = dl/f sc = colSums(weight * dl) F = crossprod(dl*weight,dl) names(sc) = child dimnames(F) = list(child, child) result = list(sc = sc, F = F) result } # wird noch in partition models verwendet optim.quartet <- function (old.el, eig, bf, dat, g = 1, w = 1, weight, ll.0 = weight * 0, control = list(eps = 1e-08, maxit = 5, trace = 0), llcomp=-Inf) { eps = 1 iter = 0 evi = (t(eig[[3]]) * bf) while (eps > control$eps && iter < control$maxit) { tmp <- fn.quartet(old.el = old.el, eig = eig, bf = bf, dat = dat, g = g, w = w, weight = weight, ll.0 = ll.0) old.ll = tmp$ll el1 <- fs(old.el[1], eig, tmp$res[, 1], dat[, 1], weight, g = g, w = w, bf = bf, ll.0 = ll.0, evi, getA=TRUE, getB=FALSE) el2 <- fs(old.el[2], eig, el1[[2]], dat[, 2], weight, g = g, w = w, bf = bf, ll.0 = ll.0, evi, getA=TRUE, getB=FALSE) el5 <- fs(old.el[5], eig, el2[[2]], tmp$res[, 2], weight, g = g, w = w, bf = bf, ll.0 = ll.0, evi, getA=FALSE, getB=TRUE) el3 <- fs(old.el[3], eig, el5[[3]], dat[, 3], weight, g = g, w = w, bf = bf, ll.0 = ll.0, evi, getA=TRUE, getB=FALSE) el4 <- fs(old.el[4], eig, el3[[2]], dat[, 4], weight, g = g, w = w, bf = bf, ll.0 = ll.0, evi, getA=FALSE, getB=FALSE) old.el[1] = el1[[1]] old.el[2] = el2[[1]] old.el[3] = el3[[1]] old.el[4] = el4[[1]] old.el[5] = el5[[1]] iter = iter + 1 ll = el4[[4]] eps = (old.ll - ll) / ll if(ll 0") if (!is.numeric(maxit) || maxit <= 0) stop("maximum number of iterations must be > 0") list(epsilon = epsilon, maxit = maxit, trace = trace) } # improves trees similar to likelihoodRatchet <- function(obj, maxit=100, k=10, control=pml.control(epsilon = 1e-08, maxit = 10, trace = 1L)){ tree = obj$tree nTips <- length(tree$tip.label) trace = control$trace control$trace = trace-1L kmax=1 for(i in 1:maxit){ tree<- rNNI(obj$tree, moves=nTips/3, n=1) #tree <- rSPR(tree, moves=10, k=3, n=1) obj2 = update(obj, tree=tree) obj2 <- optim.pml(obj2, TRUE, control = control) if(logLik(obj2) > logLik(obj)){ obj <- obj2 kmax=1 } else kmax = kmax+1 if(trace > 0) print(paste("Iteration ", i,", best pscore so far:",logLik(obj))) if(kmax == k) break() } obj } fs <- function (old.el, eig, parent.dat, child.dat, weight, g=g, w=w, bf=bf, ll.0=ll.0, evi, getA=TRUE, getB=TRUE) { if (old.el < 1e-8) old.el <- 1e-8 lg = length(parent.dat) P <- getP(old.el, eig, g) nr = as.integer(length(weight)) nc = as.integer(length(bf)) eve = eig[[2]] dad <- .Call("getDAD", parent.dat, child.dat, P, nr, nc) X <- .Call("getPrep", dad, child.dat, eig[[2]], evi, nr, nc) .Call("FS4", eig, as.integer(length(bf)), as.double(old.el), as.double(w), as.double(g), X, child.dat, dad, as.integer(length(w)), as.integer(length(weight)), as.double(weight), as.double(ll.0), as.integer(getA), as.integer(getB)) # as.double(bf), } fs3 <- function (old.el, eig, parent.dat, child, weight, g=g, w=w, bf=bf, ll.0=ll.0, contrast, contrast2, evi, ext=TRUE, getA=TRUE, getB=TRUE) # child.dat { if (old.el < 1e-8) old.el <- 1e-8 lg = length(parent.dat) P <- getP(old.el, eig, g) nr = as.integer(length(weight)) nc = as.integer(length(bf)) if(ext==FALSE){ child.dat <- child eve = eig[[2]] dad <- .Call("getDAD", parent.dat, child.dat, P, nr, nc) X <- .Call("getPrep", dad, child.dat, eig[[2]], evi, nr, nc) } else { nco = as.integer(nrow(contrast)) dad <- .Call("getDAD2", parent.dat, child, contrast, P, nr, nc, nco) child.dat <- vector("list", lg) for (i in 1:lg)child.dat[[i]] <- contrast[child, , drop=FALSE] X <- .Call("getPrep2", dad, child, contrast2, evi, nr, nc, nco) } .Call("FS4", eig, as.integer(length(bf)), as.double(old.el), as.double(w), as.double(g), X, child.dat, dad, as.integer(length(w)), as.integer(length(weight)), as.double(weight), as.double(ll.0), as.integer(getA), as.integer(getB)) # as.double(bf), } optimEdge <- function (tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, control = pml.control(epsilon = 1e-08, maxit = 10, trace=0), ...) { if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") nTips <- length(tree$tip.label) el <- tree$edge.length tree$edge.length[el < 1e-08] <- 1e-08 oldtree = tree k = length(w) data = subset(data, tree$tip.label) loglik = pml.fit4(tree, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k) start.ll <- old.ll <- loglik contrast <- attr(data, "contrast") contrast2 <- contrast %*% eig[[2]] evi = (t(eig[[3]]) * bf) weight <- attr(data, "weight") eps = 1 iter = 0 treeP = tree tree = reorder(tree) child = tree$edge[, 2] parent = tree$edge[, 1] m <- max(tree$edge) pvec <- integer(m) pvec[child] <- parent EL = numeric(m) EL[child] = tree$edge.length n = length(tree$edge.length) nr = as.integer(length(weight)) nc = as.integer(length(bf)) nco = as.integer(nrow(contrast)) eve = eig[[2]] lg = k rootNode = getRoot(tree) # raus ScaleEPS = 1.0/4294967296.0 anc = Ancestors(tree, 1:m, "parent") anc0 = as.integer(c(0L, anc)) while (eps > control$eps && iter < control$maxit) { EL <- .Call("optE", as.integer(parent), as.integer(child), as.integer(anc0), eig, evi, EL, w, g, as.integer(nr), as.integer(nc), as.integer(nTips), as.double(contrast), as.double(contrast2), nco, data, as.double(weight), as.double(ll.0)) iter = iter + 1 treeP$edge.length = EL[treeP$edge[,2]] newll <- pml.fit4(treeP, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k) eps = ( old.ll - newll ) / newll if( eps <0 ) return(list(oldtree, old.ll)) oldtree = treeP if(control$trace>1) cat(old.ll, " -> ", newll, "\n") old.ll = newll } if(control$trace>0) cat(start.ll, " -> ", newll, "\n") list(tree=treeP, logLik=newll, c(eps, iter)) } pml.move <- function(EDGE, el, data, g, w, eig, k, nTips, bf){ node <- EDGE[, 1] edge <- EDGE[, 2] nr = as.integer(attr(data, "nr")) nc = as.integer(attr(data, "nc")) node = as.integer(node - nTips - 1L) edge = as.integer(edge - 1L) contrast = attr(data, "contrast") nco = as.integer(dim(contrast)[1]) tmp <- .Call("PML3", dlist=data, as.double(el), as.double(g), nr, nc, k, eig, as.double(bf), node, edge, nTips, nco, contrast, N=as.integer(length(edge))) # as.double(w), return(NULL) } # # pmlPart + pmlCluster # optimPartQ <- function (object, Q = c(1, 1, 1, 1, 1, 1), ...) { l = length(Q) Q = Q[-l] Q = sqrt(Q) fn = function(Q, object, ...) { result <- 0 Q = c(Q^2, 1) n <- length(object) for (i in 1:n) result <- result + update(object[[i]], Q = Q, ...)$logLik result } res = optim(par = Q, fn = fn, gr = NULL, method = "L-BFGS-B", lower = 0, upper = Inf, control = list(fnscale = -1, maxit = 25), object = object, ...) res[[1]] = c(res[[1]]^2, 1) res } optimPartQGeneral <- function (object, Q = c(1, 1, 1, 1, 1, 1), subs=rep(1,length(Q)), ...) { m = length(Q) n = max(subs) ab = numeric(n) for(i in 1:n) ab[i]=log(Q[which(subs==i)[1]]) fn = function(ab, object, m, n, subs, ...) { Q = numeric(m) for(i in 1:n)Q[subs==i] = ab[i] Q = exp(Q) result = 0 for (i in 1:length(object)) result <- result + update(object[[i]], Q = Q, ...)$logLik result } res = optim(par = ab, fn = fn, gr = NULL, method = "L-BFGS-B", lower = -Inf, upper = Inf, control = list(fnscale = -1, maxit = 25), object = object, m=m, n=n, subs=subs, ...) Q = rep(1, m) for(i in 1:n) Q[subs==i] = exp(res[[1]][i]) res[[1]] = Q res } optimPartBf <- function (object, bf = c(0.25, 0.25, 0.25, 0.25), ...) { l = length(bf) nenner = 1/bf[l] lbf = log(bf * nenner) lbf = lbf[-l] fn = function(lbf, object, ...) { result <- 0 bf = exp(c(lbf, 0)) bf = bf/sum(bf) n <- length(object) for (i in 1:n) result <- result + update(object[[i]], bf = bf, ...)$logLik result } res = optim(par = lbf, fn = fn, gr = NULL, method = "Nelder-Mead", control = list(fnscale = -1, maxit = 500), object, ...) print(res[[2]]) bf = exp(c(res[[1]], 0)) bf = bf/sum(bf) } optimPartInv <- function (object, inv = 0.01, ...) { fn = function(inv, object, ...) { result <- 0 n <- length(object) for (i in 1:n) result <- result + update(object[[i]], inv = inv, ...)$logLik result } res = optimize(f = fn, interval = c(0, 1), lower = 0, upper = 1, maximum = TRUE, tol = 1e-04, object, ...) # print(res[[2]]) res[[1]] } optimPartGamma <- function (object, shape = 1, ...) { fn = function(shape, object, ...) { result <- 0 n <- length(object) for (i in 1:n) result <- result + update(object[[i]], shape = shape, ...)$logLik result } res = optimize(f = fn, interval = c(0, 100), lower = 0, upper = 100, maximum = TRUE, tol = 0.01, object, ...) res } dltmp <- function (fit, i=1, transform=transform) # i = weights { tree = fit$tree data = getCols(fit$data, tree$tip.label) if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") q = length(tree$tip.label) node <- tree$edge[, 1] edge <- tree$edge[, 2] m = length(edge) + 1 # max(edge) dat = vector(mode = "list", length = m) eig = fit$eig w = fit$w[i] g = fit$g[i] bf = fit$bf el <- tree$edge.length P <- getP(el, eig, g) nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) node = as.integer(node - min(node)) edge = as.integer(edge - 1) nTips = as.integer(length(tree$tip.label)) mNodes = as.integer(max(node) + 1) contrast = attr(data, "contrast") nco = as.integer(dim(contrast)[1]) dat[(q + 1):m] <- .Call("LogLik2", data, P, nr, nc, node, edge, nTips, mNodes, contrast, nco) result = dat[[q+1]] %*% (bf * w) parent <- tree$edge[, 1] child <- tree$edge[, 2] nTips = min(parent) - 1 datp = vector("list", m) el = tree$edge.length if (transform) dP = getdP(tree$edge.length, eig, g) else dP = getdP2(tree$edge.length, eig, g) datp[(nTips + 1)] = dat[(nTips + 1)] l = length(child) dl = matrix(0, nr, l) for (j in (m - 1):1) { # tips have factor format, internal edges are matrices if (child[j] > nTips){ tmp2 = (datp[[parent[j]]]/(dat[[child[j]]] %*% P[[j]])) dl[, j] = (tmp2 * (dat[[child[j]]] %*% dP[[j]])) %*% (w * bf) datp[[child[j]]] = (tmp2 %*% P[[j]]) * dat[[child[j]]] } else{ tmp2 = (datp[[parent[j]]]/((contrast %*% P[[j]])[data[[child[j]]],] )) dl[, j] = (tmp2 * ((contrast %*% dP[[j]])[data[[child[j]]],]) ) %*% (w * bf) } } dl } dl <- function(x, transform = TRUE){ w = x$w l=length(x$w) dl = dltmp(x, 1, transform) i=2 while(i < (l+1)){ dl = dl + dltmp(x, i, transform) i = i + 1 } dl } # add control and change edge optimPartEdge <- function (object, ...) { tree <- object[[1]]$tree theta <- object[[1]]$tree$edge.length n <- length(object) l <- length(theta) nrv <- numeric(n) for (i in 1:n) nrv[i] = attr(object[[i]]$data, "nr") cnr <- cumsum(c(0, nrv)) weight = numeric(sum(nrv)) dl <- matrix(NA, sum(nrv), l) for (i in 1:n) weight[(cnr[i] + 1):cnr[i + 1]] = attr(object[[i]]$data, "weight") ll0 = 0 for (i in 1:n) ll0 = ll0 + object[[i]]$logLik eps = 1 scalep =1 k = 1 while (eps > 0.001 & k<50) { if(scalep==1){ for (i in 1:n) { lv = drop(exp(object[[i]]$site)) dl[(cnr[i] + 1):cnr[i + 1], ] = dl(object[[i]], TRUE)/lv } sc = colSums(weight * dl) F = crossprod(dl * weight, dl) + diag(l)*1e-10 # add small ridge penalty for numerical stability } thetaNew = log(theta) + scalep * solve(F, sc) tree$edge.length = as.numeric(exp(thetaNew)) for (i in 1:n) object[[i]] <- update(object[[i]], tree = tree) ll1 = 0 for (i in 1:n) ll1 = ll1 + object[[i]]$logLik eps <- ll1 - ll0 if (eps < 0 || is.nan(eps)) { scalep = scalep/2 eps = 1 thetaNew = log(theta) ll1 = ll0 } else scalep = 1 theta = exp(thetaNew) ll0 <- ll1 k=k+1 } object } makePart <- function(fit, rooted, weight=~index+genes){ if(inherits(fit,"phyDat")){ x <- fit dm <- dist.ml(x) if(!rooted) tree <- NJ(dm) else tree <- upgma(dm) fit <- pml(tree, x, k=4) } dat <- fit$data if(class(weight)[1]=="formula") weight <- xtabs(weight, data=attr(dat, "index")) fits <- NULL for(i in 1:dim(weight)[2]){ ind <- which(weight[,i] > 0) dat2 <- getRows(dat, ind) attr(dat2, "weight") <- weight[ind,i] fits[[i]] <- update(fit, data = dat2) } names(fits) = colnames(fits) fits } multiphyDat2pmlPart <- function(x, rooted=FALSE, ...){ fun <- function(x, ...){ dm <- dist.ml(x) if(!rooted) tree <- NJ(dm) else tree <- upgma(dm) fit <- pml(tree, x, ...) } fits <- lapply(x@seq, fun, ...) fits } pmlPart2multiPhylo <- function(x){ res <- lapply(x$fits, FUN=function(x)x$tree) class(res) <- "multiPhylo" res } plot.pmlPart<- function(x, ...){ plot(pmlPart2multiPhylo(x), ...) } pmlPart <- function (formula, object, control=pml.control(epsilon=1e-8, maxit=10, trace=1), model=NULL, rooted=FALSE, ...) { call <- match.call() form <- phangornParseFormula(formula) opt <- c("nni", "bf", "Q", "inv", "shape", "edge", "rate") optAll <- match(opt, form$left) optPart <- match(opt, form$right) AllNNI <- !is.na(optAll[1]) AllBf <- !is.na(optAll[2]) AllQ <- !is.na(optAll[3]) AllInv <- !is.na(optAll[4]) AllGamma <- !is.na(optAll[5]) AllEdge <- !is.na(optAll[6]) PartNni <- !is.na(optPart[1]) PartBf <- !is.na(optPart[2]) PartQ <- !is.na(optPart[3]) PartInv <- !is.na(optPart[4]) PartGamma <- !is.na(optPart[5]) PartEdge <- !is.na(optPart[6]) PartRate <- !is.na(optPart[7]) if(PartNni) PartEdge <- TRUE if(inherits(object,"multiphyDat")){ if(AllNNI || AllEdge) object <- do.call(cbind.phyDat, object@seq) else fits <- multiphyDat2pmlPart(object, rooted=rooted, ...) } if(inherits(object,"pml")) fits <- makePart(object, rooted=rooted, ...) if(inherits(object,"phyDat")) fits <- makePart(object, rooted=rooted, ...) if(inherits(object,"pmlPart")) fits <- object$fits if(inherits(object,"list")) fits <- object trace = control$trace epsilon = control$epsilon maxit = control$maxit p <- length(fits) # if(length(model) epsilon & m < maxit) { loli = 0 if(any(c(PartNni, PartBf, PartInv, PartQ, PartGamma, PartEdge, PartRate))){ for (i in 1:p) { fits[[i]] = optim.pml(fits[[i]], optNni=PartNni, optBf=PartBf, optQ=PartQ, optInv=PartInv, optGamma=PartGamma, optEdge=PartEdge, optRate=PartRate, optRooted=rooted, control = pml.control(maxit = 3, epsilon = 1e-8, trace-1), model=model[i]) } } if (AllQ) { Q = fits[[1]]$Q subs = c(1:(length(Q)-1), 0) newQ <- optimPartQGeneral(fits, Q=Q, subs=subs) for (i in 1:p) fits[[i]] <- update(fits[[i]], Q = newQ[[1]]) } if (AllBf) { bf = fits[[1]]$bf newBf <- optimPartBf(fits, bf=bf) for (i in 1:p) fits[[i]] <- update(fits[[i]], bf = newBf) } if (AllInv) { inv = fits[[1]]$inv newInv <- optimPartInv(fits, inv=inv) for (i in 1:p) fits[[i]] <- update(fits[[i]], inv = newInv) } if (AllGamma) { shape = fits[[1]]$shape newGamma <- optimPartGamma(fits, shape=shape)[[1]] for (i in 1:p) fits[[i]] <- update(fits[[i]], shape = newGamma) } if (AllNNI){ fits <- optimPartNNI(fits,AllEdge) if(trace>0) cat(attr(fits,"swap"), " NNI operations performed") } if (AllEdge) fits <- optimPartEdge(fits) if (PartRate){ tree = fits[[1]]$tree rate=numeric(p) wp =numeric(p) for(i in 1:p){ wp[i]=sum(fits[[i]]$weight) rate[i] <- fits[[i]]$rate } ratemult = sum(wp) / sum(wp*rate) tree$edge.length = tree$edge.length/ratemult for(i in 1:p)fits[[i]] = update(fits[[i]], tree=tree, rate=rate[i]*ratemult) } loli <- 0 for (i in 1:p) loli <- loli + fits[[i]]$log eps = (logLik - loli)/loli if(trace>0) cat("loglik:", logLik, "-->", loli, "\n") logLik <- loli m = m + 1 } df <- matrix(1, 6 ,2) colnames(df) <- c("#df", "group") rownames(df) <- c("Edge", "Shape", "Inv", "Bf", "Q", "Rate") df[1,1] <- length(fits[[1]]$tree$edge.length) df[2,1] <- fits[[1]]$k > 1 df[3,1] <- fits[[1]]$inv > 0 df[4,1] <- length(unique(fits[[1]]$bf)) - 1 df[5,1] <- length(unique(fits[[1]]$Q)) - 1 df[6,1] <- 0 # rates if(PartEdge) df[1,2] = p if(PartGamma) df[2,2] = p if(PartInv) df[3,2] = p if(PartBf) df[4,2] = p if(PartQ) df[5,2] = p if(PartRate) df[6,1] = p-1 attr(logLik, "df") = sum(df[,1]*df[,2]) object <- list(logLik = logLik, fits = fits, call = call, df=df) class(object) <- "pmlPart" object } bip <- function (obj) { if (is.null(attr(obj, "order")) || attr(obj, "order") == "cladewise") obj <- reorder(obj, "postorder") maxP = max(obj$edge) nTips = length(obj$tip.label) res <- .Call("C_bip", as.integer(obj$edge[, 1]), as.integer(obj$edge[, 2]), as.integer(nTips), as.integer(maxP)) res } bipart <- function(obj){ if (is.null(attr(obj, "order")) || attr(obj, "order") == "cladewise") obj <- reorder(obj, "postorder") maxP = max(obj$edge) nTips = length(obj$tip.label) res <- .Call("C_bipart", as.integer(obj$edge[,1]) , as.integer(obj$edge[,2]), as.integer(nTips), as.integer(maxP)) #, as.integer(obj$Nnode)) # attr(res, "nodes") = unique(obj$edge[,1]) res } bipartition <- function (tree) { if(is.rooted(tree))tree <- unroot(tree) if(is.null(attr(tree,"order")) || attr(tree, "order")=="cladewise") tree <- reorder(tree, "postorder") bp <- bipart(tree) nTips = length(tree$tip.label) l = length(bp) m = length(bp[[l]]) k = length(tree$edge[, 1]) result = matrix(0L, l, m) res = matrix(0L, k, m) for (i in 1:l) result[i, bp[[i]]] = 1L result = result[-l, ,drop=FALSE] for (i in 1:nTips) res[(tree$edge[, 2] == i), i] = 1L # res[tree$edge[, 2] > nTips, ] = result res[ match(unique(tree$edge[,1]),tree$edge[,2])[-l], ] = result colnames(res) = tree$tip.label rownames(res) = tree$edge[,2] res[res[, 1] == 1, ] = 1L - res[res[, 1] == 1, ] res } pmlCluster.fit <- function (formula, fit, weight, p = 4, part = NULL, control=pml.control(epsilon=1e-8, maxit=10, trace=1), ...) { call <- match.call() form <- phangornParseFormula(formula) opt <- c("nni", "bf", "Q", "inv", "shape", "edge", "rate") optAll <- match(opt, form$left) optPart <- match(opt, form$right) AllNNI <- !is.na(optAll[1]) AllBf <- !is.na(optAll[2]) AllQ <- !is.na(optAll[3]) AllInv <- !is.na(optAll[4]) AllGamma <- !is.na(optAll[5]) AllEdge <- !is.na(optAll[6]) PartNni <- !is.na(optPart[1]) PartBf <- !is.na(optPart[2]) PartQ <- !is.na(optPart[3]) PartInv <- !is.na(optPart[4]) PartGamma <- !is.na(optPart[5]) PartEdge <- !is.na(optPart[6]) PartRate <- !is.na(optPart[7]) if(PartNni) PartEdge <- TRUE nrw <- dim(weight)[1] ncw <- dim(weight)[2] if (is.null(part)){ part = rep(1:p, length=ncw) part = sample(part) } Part = part Gtrees = vector("list", p) dat <- fit$data attr(fit$orig.data, "index") <- attr(dat, "index") <- NULL for (i in 1:p) Gtrees[[i]] = fit$tree fits = vector("list", p) for (i in 1:p) fits[[i]] = fit trace = control$trace eps = 0 m = 1 logLik = fit$log trees = list() weights = matrix(0, nrw, p) lls = matrix(0, nrw, p) loli = fit$log oldpart = part eps2 = 1 iter = 0 swap = 1 while (eps < ncw || abs(eps2) > control$eps) { df2 = 0 if(any(c(PartNni, PartBf, PartInv, PartQ, PartGamma, PartEdge, PartRate))){ for (i in 1:p) { weights[, i] = rowSums(weight[, which(part == i), drop = FALSE]) ind <- which(weights[, i] > 0) dat2 <- getRows(dat, ind) attr(dat2, "weight") <- weights[ind, i] fits[[i]] <- update(fits[[i]], data = dat2) fits[[i]] = optim.pml(fits[[i]], PartNni, PartBf, PartQ, PartInv, PartGamma, PartEdge, PartRate, control = pml.control(epsilon = 1e-8, maxit = 3, trace-1)) lls[, i] = update(fits[[i]], data = dat)$site Gtrees[[i]] = fits[[i]]$tree } } if (AllQ) { Q = fits[[1]]$Q subs = c(1:(length(Q)-1), 0) newQ <- optimPartQGeneral(fits, Q=Q, subs=subs)[[1]] for (i in 1:p) fits[[i]] <- update(fits[[i]], Q = newQ) df2 = df2 + length(unique(newQ)) - 1 } if (AllBf) { bf = fits[[1]]$bf newBf <- optimPartBf(fits, bf=bf) for (i in 1:p) fits[[i]] <- update(fits[[i]], bf = newBf) df2 = df2 + length(unique(newBf)) - 1 } if (AllInv) { inv = fits[[1]]$inv newInv <- optimPartInv(fits, inv=inv) for (i in 1:p) fits[[i]] <- update(fits[[i]], inv = newInv) #there was an Error df2 = df2 + 1 } if (AllGamma) { shape = fits[[1]]$shape newGamma <- optimPartGamma(fits, shape=shape)[[1]] for (i in 1:p) fits[[i]] <- update(fits[[i]], shape = newGamma) df2 = df2 + 1 } if (AllNNI) { fits <- optimPartNNI(fits, AllEdge) if(trace>0)cat(attr(fits, "swap"), " NNI operations performed") swap <- attr(fits, "swap") } if (AllEdge) { fits <- optimPartEdge(fits) df2 = df2 + length(fits[[1]]$tree$edge.length) } if (PartRate) { tree = fits[[1]]$tree rate = numeric(p) wp = numeric(p) for (i in 1:p) { wp[i] = sum(fits[[i]]$weight) rate[i] <- fits[[i]]$rate } ratemult = sum(wp)/sum(wp * rate) tree$edge.length = tree$edge.length/ratemult for (i in 1:p) fits[[i]] = update(fits[[i]], tree = tree, rate = rate[i] * ratemult) } for (i in 1:p) lls[, i] = update(fits[[i]], data = dat)$site trees[[m]] = Gtrees LL = t(weight) %*% lls # choose partitions which change tmp =(LL[cbind(1:ncw,part)] - apply(LL, 1, max))/colSums(weight) fixi = numeric(p) for(i in 1:p){ tmpi = which(part == i) fixi[i] = tmpi[which.max(tmp[tmpi])] } oldpart = part # restrict the number of elements changing groups # If more than 25% would change, only the 25% with the highest increase per site change if( sum(tmp==0)/length(tmp) < .75){ medtmp = quantile(tmp, .25) medind = which(tmp<=medtmp) part[medind] = apply(LL[medind,], 1, which.max) } else part = apply(LL, 1, which.max) # force groups to have at least one member part[fixi] = 1:p Part = cbind(Part, part) eps = sum(diag(table(part, oldpart))) eps2 = loli loli = sum(apply(LL, 1, max)) eps2 = (eps2 - loli)/loli logLik = c(logLik, loli) if(trace>0) print(loli) Part = cbind(Part, part) df2 = df2 + df2 if (eps == ncw & swap == 0) AllNNI = FALSE m = m + 1 if (eps == ncw) iter = iter + 1 if (iter == 3) break } df <- matrix(1, 6, 2) colnames(df) <- c("#df", "group") rownames(df) <- c("Edge", "Shape", "Inv", "Bf", "Q", "Rate") df[1, 1] <- length(fits[[1]]$tree$edge.length) df[2, 1] <- fits[[1]]$k - 1 df[3, 1] <- fits[[1]]$inv > 0 df[4, 1] <- length(unique(fits[[1]]$bf)) - 1 df[5, 1] <- length(unique(fits[[1]]$Q)) - 1 df[6, 1] <- 0 if (PartEdge) df[1, 2] = p if (PartGamma) df[2, 2] = p if (PartInv) df[3, 2] = p if (PartBf) df[4, 2] = p if (PartQ) df[5, 2] = p if (PartRate) df[6, 1] = p - 1 attr(logLik, "df") = sum(df[, 1] * df[, 2]) res = list(logLik = logLik, Partition = Part, trees = trees) # intermediate results result <- list(logLik = loli, fits = fits, Partition = part, df = df, res = res, call = call) class(result) <- c("pmlPart") result } pmlCluster <- function (formula, fit, weight, p = 1:5, part = NULL, nrep = 10, control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1), ...) { call <- match.call() form <- phangornParseFormula(formula) if(any(p==1)){ opt2 <- c("nni", "bf", "Q", "inv", "shape", "edge") tmp1 <- opt2 %in% form$left tmp1 <- tmp1 | (opt2 %in% form$right) fit <- optim.pml(fit, tmp1[1], tmp1[2], tmp1[3], tmp1[4], tmp1[5], tmp1[6]) } p=p[p!=1] if(length(p)==0)return(fit) n = sum(weight) k=2 BIC = matrix(0, length(p)+1, nrep) BIC[1,] = AIC(fit, k = log(n)) LL = matrix(NA, length(p)+1, nrep) LL[1,] = logLik(fit) P = array(dim=c(length(p)+1, nrep, dim(weight)[2])) tmpBIC = Inf choice = c(1,1) for(j in p){ tmp=NULL for(i in 1:nrep){ tmp = pmlCluster.fit(formula, fit, weight, p=j, part=part, control=control,...) P[k,i,] = tmp$Partition BIC[k,i] = AIC(tmp, k = log(n)) LL[k,i] = logLik(tmp) if(BIC[k,i]0] type <- attr(x$data, "type") levels <- attr(x$data, "levels") nc <- attr(x$data, "nc") ll0 = sum(w*log(w/sum(w))) cat("\nunconstrained loglikelihood:", ll0, "\n") if(x$inv > 0)cat("Proportion of invariant sites:",x$inv,"\n") if(x$k >1){ cat("Discrete gamma model\n") cat("Number of rate categories:",x$k,"\n") cat("Shape parameter:",x$shape,"\n") } if(type=="AA") cat("Rate matrix:",x$model, "\n") if(type=="DNA"){ cat("\nRate matrix:\n") QM = matrix(0, nc, nc, dimnames = list(levels,levels)) QM[lower.tri(QM)] = x$Q QM = QM+t(QM) print(QM) cat("\nBase frequencies: \n") bf = x$bf names(bf) = levels cat(bf, "\n") } if(type=="CODON") { cat("dn/ds:",x$dnds, "\n") cat("ts/tv:",x$tstv, "\n") } if(type=="USER" & length(x$bf)<11){ cat("\nRate matrix:\n") QM = matrix(0, nc, nc, dimnames = list(levels,levels)) QM[lower.tri(QM)] = x$Q QM = QM+t(QM) print(QM) cat("\nBase frequencies: \n") bf = x$bf names(bf) = levels cat(bf, "\n") } } optEdgeMulti <- function (object, control = pml.control(epsilon = 1e-8, maxit = 10, trace=1), ...) { tree <- object$tree theta <- object$tree$edge.length weight <- attr(object$data, "weight") ll0 = object$logLik eps = 1 iter = 0 iter2 = 0 scale = 1 # l = length(theta) while (abs(eps) > control$eps && iter < control$maxit) { dl = score(object) thetaNew = log(theta) + scale * solve(dl[[2]], dl[[1]]) #+ diag(l)*1e-10 newtheta = exp(thetaNew) tree$edge.length = as.numeric(newtheta) object <- update(object, tree = tree) ll1 = object$logLik eps <- ( ll0 - ll1 ) / ll1 if(eps < 0){ newtheta = theta scale = scale / 2 tree$edge.length = as.numeric(theta) ll1 = ll0 iter2 <- iter2+1 } else{ scale=1 iter2 = 0 } theta = newtheta if(iter2==0 && control$trace>0) cat("loglik: ",ll1,"\n") ll0 <- ll1 if(iter2==10)iter2=0 if(iter2==0)iter <- iter+1 } object <- update(object, tree = tree) object } # add data for internal use parent.frame(n) for higher nestings update.pmlNew <- function (object, ..., evaluate = TRUE){ call <- object$call if (is.null(call)) stop("need an object with call component") extras <- match.call(expand.dots = FALSE)$... if (length(extras)) { existing <- !is.na(match(names(extras), names(call))) for (a in names(extras)[existing]) call[[a]] <- extras[[a]] if (any(!existing)) { call <- c(as.list(call), extras[!existing]) call <- as.call(call) } } if (evaluate) eval(call, object, parent.frame()) else call } # add dnds tstv update.pml <- function (object, ...) { extras <- match.call(expand.dots = FALSE)$... pmla <- c("tree", "data", "bf", "Q", "inv", "k", "shape", "rate", "model", "wMix", "llMix", "...") names(extras) <- pmla[pmatch(names(extras), pmla[-length(pmla)])] call = object$call if (length(extras)) { existing <- !is.na(match(names(extras), names(call))) for (a in names(extras)[existing]) call[[a]] <- extras[[a]] if (any(!existing)) { call <- c(as.list(call), extras[!existing]) call <- as.call(call) } } existing <- match(pmla, names(extras)) updateEig <- FALSE updateRates <- FALSE if (is.na(existing[1])) tree <- object$tree else tree <- eval(extras[[existing[1]]], parent.frame()) if(is.null(attr(tree,"order")) || attr(tree,"order")=="cladewise")tree <- reorder(tree, "postorder") if (is.na(existing[2])){ data <- object$data INV <- object$INV } else{ data <- eval(extras[[existing[2]]], parent.frame()) ll.0 <- numeric(attr(data,"nr")) INV <- Matrix(lli(data, tree), sparse=TRUE) } nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) if (is.na(existing[3])) bf <- object$bf else { bf <- eval(extras[[existing[3]]], parent.frame()) updateEig <- TRUE } if (is.na(existing[4])) Q <- object$Q else { Q <- eval(extras[[existing[4]]], parent.frame()) updateEig <- TRUE } # model <- object$model type <- attr(object$data, "type") model<-NULL if (type == "AA") { if(!is.na(existing[9]) ){ # model <- match.arg(eval(extras[[existing[9]]], parent.frame()), c("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24")) model <- match.arg(eval(extras[[existing[9]]], parent.frame()), .aamodels) getModelAA(model, bf = is.na(existing[3]), Q = is.na(existing[4])) updateEig <- TRUE } # else model <- object$model } if(is.na(existing[5])) inv <- object$inv else{ inv <- eval(extras[[existing[5]]], parent.frame()) updateRates <- TRUE } if(is.na(existing[6])) k <- object$k else{ k <- eval(extras[[existing[6]]], parent.frame()) updateRates <- TRUE } if(is.na(existing[7])) shape <- object$shape else{ shape <- eval(extras[[existing[7]]], parent.frame()) updateRates <- TRUE } rate <- ifelse(is.na(existing[8]), object$rate, eval(extras[[existing[8]]], parent.frame())) wMix <- ifelse(is.na(existing[10]), object$wMix, eval(extras[[existing[10]]], parent.frame())) if(is.na(existing[11])) llMix <- object$llMix else llMix <- eval(extras[[existing[11]]], parent.frame()) levels <- attr(data, "levels") weight <- attr(data, "weight") if(updateEig)eig <- edQt(bf = bf, Q = Q) else{ eig <- object$eig model <- object$model } g <- discrete.gamma(shape, k) g <- rate * g if (inv > 0) g <- g/(1 - inv) ll.0 <- as.matrix(INV %*% (bf * inv)) if(wMix>0) ll.0 <- ll.0 + llMix w = rep(1/k, k) if (inv > 0) w <- (1 - inv) * w if (wMix > 0) w <- wMix * w m <- 1 resll <- matrix(0, nr, k) nTips = as.integer(length(tree$tip.label)) data <- subset(data, tree$tip.label) on.exit(.C("ll_free")) .C("ll_init", nr, nTips, nc, as.integer(k)) tmp <- pml.fit(tree, data, bf, shape = shape, k = k, Q = Q, levels = attr(data, "levels"), inv = inv, rate = rate, g = g, w = w, eig = eig, INV = INV, ll.0 = ll.0, llMix = llMix, wMix = wMix, site = TRUE) df <- ifelse(is.ultrametric(tree), tree$Nnode, length(tree$edge.length)) # ifelse() df <- switch(type, DNA = df + (k>1) + (inv > 0) + length(unique(bf)) - 1 + length(unique(Q)) - 1, AA = df + (k>1) + (inv > 0) , CODON = df + (k>1) + (inv > 0) + length(unique(bf)) - 1 , #+ (dnds != 1) + (tstv != 1), USER = df + (k>1) + (inv > 0) + length(unique(bf)) - 1 + length(unique(Q)) - 1) # if (type == "CODON") { # df <- df + (k > 1) + (inv > 0) + length(unique(bf)) - 1 # } # else df = df + (k > 1) + (inv > 0) + length(unique(bf)) - 1 + length(unique(Q)) - 1 result = list(logLik = tmp$loglik, inv = inv, k = k, shape = shape, Q = Q, bf = bf, rate = rate, siteLik = tmp$siteLik, weight = weight, g = g, w = w, eig = eig, data = data, model = model, INV = INV, ll.0 = ll.0, tree = tree, lv = tmp$resll, call = call, df = df, wMix = wMix, llMix = llMix) if (type == "CODON") { result$dnds <- 1 result$tstv <- 1 } class(result) = "pml" result } optimMixQ <- function(object, Q=c(1, 1, 1, 1, 1, 1), omega,...){ l = length(Q) Q = Q[-l] Q = sqrt(Q) fn = function(Q, object, omega,...) { Q = c(Q^2, 1) weight <- object[[1]]$weight n <- length(omega) p <- length(weight) result <- numeric(p) for(i in 1:n)result <- result + as.numeric(update(object[[i]], Q=Q, ...)$lv) * omega[i] result <- sum(weight %*% log(result)) result } res = optim(par=Q, fn=fn, gr=NULL, method="L-BFGS-B", lower=0, upper=Inf, control=list(fnscale = -1, maxit=25), object=object, omega=omega,...) res[[1]] = c(res[[1]]^2, 1) res } optimMixBf <- function(object, bf=c(.25,.25,.25,.25), omega,...){ l = length(bf) nenner = 1/bf[l] lbf = log(bf * nenner) lbf = lbf[-l] fn = function(lbf, object, omega,...) { bf = exp(c(lbf,0)) bf = bf/sum(bf) weight <- object[[1]]$weight n <- length(omega) p <- length(weight) result <- numeric(p) for(i in 1:n)result <- result + as.numeric(update(object[[i]], bf=bf, ...)$lv) * omega[i] result <- sum(weight %*% log(result)) result } res = optim(par=lbf, fn=fn, gr=NULL, method="Nelder-Mead", control=list(fnscale=-1, maxit=500), object, omega=omega,...) # print(res[[2]]) bf = exp(c(res[[1]],0)) bf = bf/sum(bf) } optimMixInv <- function(object, inv=0.01, omega,...){ fn = function(inv, object, omega,...) { n <- length(omega) weight <- object[[1]]$weight p <- length(weight) result <- numeric(p) for(i in 1:n)result <- result + as.numeric(update(object, inv=inv, ...)$lv) * omega[i] result <- sum(weight %*% log(result)) result } res = optimize(f=fn, interval = c(0,1), lower = 0, upper = 1, maximum = TRUE, tol = .0001, object, omega=omega,...) # print(res[[2]]) res[[1]] } optimMixRate <- function (fits, ll, weight, omega, rate=rep(1,length(fits))) { r <- length(fits) rate0 <- rate[-r] fn<-function(rate, fits, ll, weight, omega){ r <- length(fits) rate <- c(rate, (1- sum(rate *omega[-r]))/omega[r]) for (i in 1:r) fits[[i]]<-update(fits[[i]], rate = rate[i]) for (i in 1:r) ll[, i] <- fits[[i]]$lv sum(weight*log(ll%*%omega)) } ui=diag(r-1) ui <- rbind(-omega[-r], ui) ci <- c(-1, rep(0, r-1)) res <- constrOptim(rate0, fn, grad=NULL, ui=ui, ci=ci, mu = 1e-04, control = list(fnscale=-1), method = "Nelder-Mead", outer.iterations = 50, outer.eps = 1e-05, fits=fits, ll=ll, weight=weight, omega=omega) rate <- res[[1]] res[[1]] <- c(rate, (1- sum(rate *omega[-r]))/omega[r]) res } optW <- function (ll, weight, omega,...) { k = length(omega) nenner = 1/omega[1] eta = log(omega * nenner) eta = eta[-1] fn = function(eta, ll, weight) { eta = c(0,eta) p = exp(eta)/sum(exp(eta)) res = sum(weight * log(ll %*% p)) res } if(k==2)res = optimize(f =fn , interval =c(-3,3) , lower = -3, upper = 3, maximum = TRUE, tol = .Machine$double.eps^0.25, ll = ll, weight = weight) else res = optim(eta, fn = fn, method = "L-BFGS-B", lower=-5, upper=5,control = list(fnscale = -1, maxit=25), gr = NULL, ll = ll, weight = weight) p = exp(c(0,res[[1]])) p = p/sum(p) result = list(par = p, value = res[[2]]) result } optimMixEdge <- function(object, omega, trace=1,...){ tree <- object[[1]]$tree theta <- object[[1]]$tree$edge.length weight = as.numeric(attr(object[[1]]$data,"weight")) n <- length(omega) p <- length(weight) q <- length(theta) lv1 = numeric(p) for(i in 1:n) lv1 = lv1 + as.numeric(object[[i]]$lv) * omega[i] ll0 <- sum(weight * log(lv1)) eps=1 iter <- 0 scalep <- 1 if(trace>0) cat(ll0) while(abs(eps)>.001 & iter<10){ dl <- matrix(0,p,q) for(i in 1:n)dl <- dl + dl(object[[i]],TRUE) * omega[i] dl <- dl/lv1 sc = colSums(weight * dl) F = crossprod(dl * weight, dl)+diag(q)*1e-6 blub <- TRUE iter2 <- 0 while(blub & iter2<10){ thetaNew = log(theta) + scalep * solve(F, sc) tree$edge.length = as.numeric(exp(thetaNew)) for(i in 1:n)object[[i]] <- update(object[[i]],tree=tree) lv1 = numeric(p) for(i in 1:n) lv1 = lv1 + as.numeric(object[[i]]$lv) * omega[i] ll1 <- sum(weight * log(lv1)) eps <- ll1 - ll0 if (eps < 0 || is.nan(eps)) { scalep = scalep/2 eps = 1 thetaNew = log(theta) ll1 = ll0 iter2 <- iter2+1 } else{ scalep = 1; theta = exp(thetaNew) blub=FALSE } } iter <- iter+1 ll0 <- ll1 } tree$edge.length <- theta for(i in 1:n)object[[i]] <- update(object[[i]],tree=tree) if(trace>0) cat("->", ll1, "\n") object } pmlMix <- function (formula, fit, m = 2, omega = rep(1/m, m), control=pml.control(epsilon=1e-8, maxit=20, trace=1), ...) { call <- match.call() form <- phangornParseFormula(formula) opt <- c("nni", "bf", "Q", "inv", "shape", "edge", "rate") optAll <- match(opt, form$left) optPart <- match(opt, form$right) AllBf <- !is.na(optAll[2]) AllQ <- !is.na(optAll[3]) AllInv <- !is.na(optAll[4]) AllGamma <- !is.na(optAll[5]) AllEdge <- !is.na(optAll[6]) MixNni <- !is.na(optPart[1]) MixBf <- !is.na(optPart[2]) MixQ <- !is.na(optPart[3]) MixInv <- !is.na(optPart[4]) MixGamma <- !is.na(optPart[5]) MixEdge <- !is.na(optPart[6]) MixRate <- !is.na(optPart[7]) if (inherits(fit,"list")) fits <- fit else { fits <- vector("list", m) for (i in 1:m) fits[[i]] <- fit } dat <- fits[[1]]$data p = attr(dat, "nr") weight = attr(dat, "weight") r = m ll = matrix(0, p, r) for (i in 1:r) ll[, i] = fits[[i]]$lv for (i in 1:r){ pl0 <- ll[, -i, drop = FALSE] %*% omega[-i] fits[[i]] <- update(fits[[i]], llMix = pl0, wMix = omega[i]) } if(MixRate) rate <- rep(1,r) llstart = sum(weight * log(ll %*% omega)) llold <- llstart ll0 <- llstart ll3 <- llstart eps0 <- 1 iter0 <- 0 trace = control$trace while (eps0 > control$eps & iter0 < control$maxit) { #while (eps0 > 1e-6 & iter0 < 20) { eps1 <- 100 iter1 <- 0 if (AllQ) { newQ <- optimMixQ(fits, Q = fits[[1]]$Q, omega = omega)[[1]] for (i in 1:m) fits[[i]] <- update(fits[[i]], Q = newQ) } if (AllBf) { newBf <- optimMixBf(fits, bf = fits[[1]]$bf, omega = omega) for (i in 1:m) fits[[i]] <- update(fits[[i]], bf = newBf) } if (AllInv) { newInv <- optimMixInv(fits, inv = fits[[1]]$inv, omega = omega) for (i in 1:m) fits[[i]] <- update(fits[[i]], Inv = newInv) } if (AllEdge) fits <- optimMixEdge(fits, omega, trace=trace-1) for (i in 1:r) ll[, i] <- fits[[i]]$lv while ( abs(eps1) > 0.001 & iter1 < 3) { if(MixRate){ rate <- optimMixRate(fits, ll, weight, omega, rate)[[1]] for (i in 1:r) fits[[i]] <- update(fits[[i]], rate=rate[i]) for (i in 1:r) ll[, i] <- fits[[i]]$lv } for (i in 1:r){ pl0 <- ll[, -i, drop = FALSE] %*% omega[-i] fits[[i]] <- update(fits[[i]], llMix = pl0, wMix = omega[i]) } for (i in 1:r) { pl0 <- ll[, -i, drop = FALSE] %*% omega[-i] fits[[i]] <- optim.pml(fits[[i]], MixNni, MixBf, MixQ, MixInv, MixGamma, MixEdge, optRate=FALSE, control = pml.control(epsilon = 1e-8, maxit = 3, trace-1), llMix = pl0, wMix = omega[i]) ll[, i] <- fits[[i]]$lv res = optW(ll, weight, omega) omega = res$p if(MixRate){ blub <- sum(rate*omega) rate <- rate / blub tree <- fits[[1]]$tree tree$edge.length <- tree$edge.length*blub for (i in 1:r) fits[[i]]<-update(fits[[i]], tree=tree, rate = rate[i]) for (i in 1:r) ll[, i] <- fits[[i]]$lv } for (i in 1:r){ pl0 <- ll[, -i, drop = FALSE] %*% omega[-i] fits[[i]] <- update(fits[[i]], llMix = pl0, wMix = omega[i]) } } ll1 = sum(weight * log(ll %*% omega)) res = optW(ll, weight, omega) omega = res$p if(MixRate){ blub <- sum(rate*omega) rate <- rate / blub tree <- fits[[1]]$tree tree$edge.length <- tree$edge.length*blub for (i in 1:r) fits[[i]]<-update(fits[[i]], tree=tree, rate = rate[i]) if(trace>0) print(rate) for (i in 1:r) ll[, i] <- fits[[i]]$lv } for (i in 1:r){ pl0 <- ll[, -i, drop = FALSE] %*% omega[-i] fits[[i]] <- update(fits[[i]], llMix = pl0, wMix = omega[i]) } ll2 = sum(weight * log(ll %*% omega)) eps1 = llold - ll2 iter1 <- iter1 + 1 llold = ll2 } ll1 <- sum(weight * log(ll %*% omega)) eps0 <- (ll3 - ll1) / ll1 ll3 <- ll1 iter0 <- iter0 + 1 if(trace>0) print(iter0) } parameter <- c(AllBf=AllBf, AllQ=AllQ, AllInv=AllInv, AllGamma=AllGamma, AllEdge=AllEdge, MixNni=MixNni, MixBf=MixBf, MixQ=MixQ, MixInv=MixInv, MixGamma=MixGamma, MixEdge=MixEdge, MixRate=MixRate) df <- matrix(1, 6 ,2) colnames(df) <- c("#df", "group") rownames(df) <- c("Edge", "Shape", "Inv", "Bf", "Q", "Rate") df[1,1] <- length(fits[[1]]$tree$edge.length) # df[2,1] <- fits[[1]]$k - 1 df[2,1] <- fits[[1]]$k > 1 df[3,1] <- fits[[1]]$inv > 0 df[4,1] <- length(unique(fits[[1]]$bf)) - 1 df[5,1] <- length(unique(fits[[1]]$Q)) - 1 df[6,1] <- 0 if(MixEdge) df[1,2] = r if(MixGamma) df[2,2] = r if(MixInv) df[3,2] = r if(MixBf) df[4,2] = r if(MixQ) df[5,2] = r if(MixRate) df[6,1] = r-1 attr(logLik, "df") = sum(df[,1]*df[,2]) converge <- c(iter=iter0, eps=eps0) result <- list(logLik = ll1, omega = omega, fits = fits, call = call, converge=converge, parameter=parameter, df=df) class(result) <- "pmlMix" result } print.pmlMix <- function(x,...){ nc <- attr(x$fits[[1]]$data, "nc") nr <- attr(x$fits[[1]]$data, "nr") levels <- attr(x$fits[[1]]$data, "levels") r <- length(x$fits) w <- x$fits[[1]]$weight w <- w[w>0] type <- attr(x$fits[[1]]$data, "type") nc <- attr(x$fits[[1]]$data, "nc") ll0 = sum(w*log(w/sum(w))) bf <- matrix(0,r,nc) dimnames(bf) <- list(1:r, levels) Q <- matrix(0, r, nc*(nc-1)/2) dimnames(Q) <- list(1:r, NULL) rate <- numeric(r) inv <- x$fits[[1]]$inv shape <- numeric(r) for(i in 1:r){ bf[i, ] <- x$fits[[i]]$bf Q[i, ] <- x$fits[[i]]$Q rate[i] <- x$fits[[i]]$rate shape[i] <- x$fits[[i]]$shape } cat("\nloglikelihood:", x$logLik, "\n") cat("\nunconstrained loglikelihood:", ll0, "\n") cat("AIC: ", AIC(x), " BIC: ", AIC(x, k=log(nr)), "\n\n") cat("\nposterior:", x$omega ,"\n") if(inv > 0)cat("Proportion of invariant sites:",inv,"\n") cat("\nRates:\n") cat(rate,"\n") cat("\nBase frequencies: \n") print(bf) cat("\nRate matrix:\n") print(Q) } logLik.pmlMix <- function (object, ...) { res <- object$logLik attr(res, "df") <- sum(object$df[,1] * object$df[,2]) class(res) <- "logLik" res } print.pmlPart <- function(x,...){ df <- x$df nc <- attr(x$fits[[1]]$data, "nc") levels <- attr(x$fits[[1]]$data, "levels") r <- length(x$fits) nc <- attr(x$fits[[1]]$data, "nc") nr <- attr(x$fits[[1]]$data, "nr") k <- x$fits[[1]]$k lbf=x$df["Bf",2] bf <- matrix(0, lbf, nc) if(lbf>1)dimnames(bf) <- list(1:r, levels) lQ = x$df["Q",2] Q <- matrix(0, lQ, nc*(nc-1)/2) if(lQ>1)dimnames(Q) <- list(1:r, NULL) type <- attr(x$fits[[1]]$data, "type") loli <- numeric(r) rate <- numeric(r) shape <- numeric(r) sizes <- numeric(r) inv <- numeric(r) for(i in 1:r){ loli[i] <- x$fits[[i]]$logLik if(i <= lbf)bf[i, ] <- x$fits[[i]]$bf if(i <= lQ)Q[i, ] <- x$fits[[i]]$Q rate[i] <- x$fits[[i]]$rate shape[i] <- x$fits[[i]]$shape inv[i] <- x$fits[[i]]$inv sizes[i] <- sum(attr(x$fits[[i]]$data,"weight")) } cat("\nloglikelihood:", x$logLik, "\n") cat("\nloglikelihood of partitions:\n ", loli, "\n") cat("AIC: ", AIC(x), " BIC: ", AIC(x, k=log(sum(sizes))), "\n\n") cat("Proportion of invariant sites:",inv,"\n") cat("\nRates:\n") cat(rate,"\n") if(k>1){ cat("\nShape parameter:\n") cat(shape,"\n") } if(type=="AA") cat("Rate matrix:",x$fits[[1]]$model, "\n") else{ cat("\nBase frequencies: \n") print(bf) cat("\nRate matrix:\n") print(Q) } } logLik.pmlPart <- function (object, ...) { res <- object$logLik attr(res, "df") <- sum(object$df[,1] * object$df[,2]) class(res) <- "logLik" res } pmlPen <- function(object, lambda, ...){ if(inherits(object,"pmlPart")) return(pmlPartPen(object, lambda,...)) if(inherits(object,"pmlMix")) return(pmlMixPen(object, lambda,...)) else stop("object has to be of class pmlPart or pmlMix") } pmlPartPen <- function(object, lambda, control=pml.control(epsilon=1e-8, maxit=20, trace=1),...){ fits <- object$fits m <- length(fits) K = -diag(length(fits[[1]]$tree$edge.length)) Ktmp=K for(i in 1:(m-1))Ktmp = cbind(Ktmp,K) KM = Ktmp for(i in 1:(m-1))KM = rbind(KM,Ktmp) diag(KM) = m-1 theta=NULL l = length(fits[[1]]$tree$edge.length) loglik = 0 for(i in 1:m){ theta = c(theta,fits[[i]]$tree$edge.length) loglik = loglik + fits[[i]]$logLik } print(loglik) pen = - 0.5 * lambda * t(theta)%*%KM%*%theta loglik = loglik - 0.5 * lambda * t(theta)%*%KM%*%theta eps=1 H = matrix(0, m * l, m * l) iter=0 trace = control$trace while( abs(eps)>control$eps & iter0)print(loglik1) loglik0 = loglik1 pen = - 0.5 * lambda * t(theta)%*%KM%*%theta loglik1 = loglik1 - 0.5 * lambda * t(thetanew)%*%KM%*%thetanew eps = (loglik - loglik1) / loglik1 loglik = loglik1 theta = exp(thetanew) iter = iter+1 if(trace>0)print(iter) } df = sum( diag(solve(H + lambda* KM, H))) object$df[1,1] = df object$df[1,2] = 1 object$fits = fits object$logLik = loglik0 attr(object$logLik, "df") = sum(object$df[,1]*object$df[,2]) object$logLik.pen = loglik attr(object$logLik.pen, "df") = sum(object$df[,1]*object$df[,2]) object } pmlMixPen = function (object, lambda, optOmega=TRUE, control=pml.control(epsilon=1e-8, maxit=20, trace=1), ...) { fits <- object$fits m <- length(fits) K = -diag(length(fits[[1]]$tree$edge.length)) tree <- fits[[1]]$tree Ktmp = K for (i in 1:(m - 1)) Ktmp = cbind(Ktmp, K) KM = Ktmp for (i in 1:(m - 1)) KM = rbind(KM, Ktmp) diag(KM) = m - 1 theta = NULL l = length(fits[[1]]$tree$edge.length) omega <- object$omega dat <- fits[[1]]$data nr = attr(dat, "nr") weight = drop(attr(dat, "weight")) ll = matrix(0, nr, m) for (i in 1:m) ll[, i] = fits[[i]]$lv lv = drop(ll %*% omega) loglik = sum(weight * log(lv)) for (i in 1:m) theta = c(theta, fits[[i]]$tree$edge.length) pen = - 0.5 * lambda * t(theta) %*% KM %*% theta loglik = loglik + pen print(loglik) eps0 = 1 dl <- matrix(0, nr, m * l) iter0 = 0 trace = control$trace while (abs(eps0) > control$eps & iter0 < control$maxit) { eps = 1 iter = 0 while (abs(eps) > 0.01 & iter < 5) { for (i in 1:m) { dl[, (1:l) + l * (i - 1)] <- dl(fits[[i]], TRUE) * omega[i] } dl <- dl/lv sc = colSums(weight * dl) - lambda * KM %*% log(theta) H = crossprod(dl * weight, dl) thetanew = log(theta) + solve(H + lambda * KM, sc) for (i in 1:m) fits[[i]]$tree$edge.length = exp(thetanew[(1:l) + (i - 1) * l]) for (i in 1:m) { tree$edge.length = exp(thetanew[(1:l) + (i - 1) * l]) fits[[i]] = update.pml(fits[[i]], tree = tree) ll[, i] = fits[[i]]$lv } lv = drop(ll %*% omega) loglik1 = sum(weight * log(lv)) pen = - 0.5 * lambda * t(thetanew) %*% KM %*% thetanew loglik1 = loglik1 + pen eps = abs(loglik1 - loglik) theta = exp(thetanew) loglik <- loglik1 iter = iter + 1 } if(optOmega){ res = optWPen(ll, weight, omega, pen) omega = res$p for (i in 1:m) { pl0 <- ll[, -i, drop = FALSE] %*% omega[-i] fits[[i]] <- update(fits[[i]], llMix = pl0, wMix = omega[i]) } } lv = drop(ll %*% omega) loglik1 = sum(weight * log(lv)) loglik0 =loglik1 loglik1 = loglik1 - 0.5 * lambda * t(thetanew) %*% KM %*% thetanew eps0 = (loglik - loglik1) / loglik1 theta = exp(thetanew) loglik <- loglik1 iter0 = iter0 + 1 if(trace>0) print(loglik) } for (i in 1:m) { pl0 <- ll[, -i, drop = FALSE] %*% omega[-i] fits[[i]] <- update(fits[[i]], llMix = pl0, wMix = omega[i]) } df = sum(diag(solve(H + lambda * KM, H))) penalty <- list(lambda=lambda, K=KM, thetanew=thetanew, ploglik=loglik) object$omega = omega object$df[1, 1] = df object$df[1, 2] = 1 object$fits = fits object$logLik = loglik0 object$penalty = penalty object } optWPen = function (ll, weight, omega, pen, ...) { k = length(omega) nenner = 1/omega[1] eta = log(omega * nenner) eta = eta[-1] fn = function(eta, ll, weight, pen) { eta = c(0, eta) p = exp(eta)/sum(exp(eta)) res = sum(weight * log(ll %*% p)) + pen res } if (k == 2) res = optimize(f = fn, interval = c(-3, 3), lower = -3, upper = 3, maximum = TRUE, tol = .Machine$double.eps^0.25, ll = ll, weight = weight, pen = pen) else res = optim(eta, fn = fn, method = "L-BFGS-B", lower = -5, upper = 5, control = list(fnscale = -1, maxit = 25), gr = NULL, ll = ll, weight = weight, pen=pen) p = exp(c(0, res[[1]])) p = p/sum(p) result = list(par = p, value = res[[2]]) result } optNNI <- function(fit, INDEX){ tree = fit$tree ll.0 <- fit$ll.0 loli <- fit$logLik bf = fit$bf eig = fit$eig k = fit$k w = fit$w g = fit$g rootEdges <- attr(INDEX, "root") .dat <- NULL parent = tree$edge[, 1] child = tree$edge[, 2] data = getCols(fit$data, tree$tip.label) datp <- rnodes(tree, data, w, g, eig, bf) # nicht elegant, spaeter auch raus tmp = length(tree$tip.label) for(i in 1:length(w)).dat[i,1:tmp]=new2old.phyDat(data) evector <- numeric(max(parent)) evector[child] <- tree$edge.length m <- dim(INDEX)[1] k = min(parent) loglik = numeric(2 * m) edgeMatrix <- matrix(0, 2 * m, 5) for (i in 1:m) { ei = INDEX[i, ] el0 = evector[INDEX[i, ]] l = length(datp[, 1]) weight = fit$weight datn = vector("list", 4 * l) attr(datn, "dim") = c(l, 4) datn <- .dat[, ei[1:4], drop = FALSE] if (!(ei[5] %in% rootEdges)) datn[, 1] = datp[, ei[1], drop = FALSE] new1 <- optim.quartet(el0[c(1, 3, 2, 4, 5)], eig, bf, datn[, c(1, 3, 2, 4), drop = FALSE], g, w, weight, ll.0, llcomp = fit$log) new2 <- optim.quartet(el0[c(1, 4, 3, 2, 5)], eig, bf, datn[, c(1, 4, 3, 2), drop = FALSE], g, w, weight, ll.0, llcomp = fit$log) loglik[(2 * i) - 1] = new1[[2]] loglik[(2 * i)] = new2[[2]] edgeMatrix[(2 * i) - 1, ] = new1[[1]] edgeMatrix[(2 * i), ] = new2[[1]] } list(loglik=loglik, edges = edgeMatrix) } optimPartNNI <- function (object, AllEdge=TRUE,...) { tree <- object[[1]]$tree INDEX <- indexNNI(tree) l = length(object) loglik0 = 0 for(i in 1:l)loglik0 = loglik0 + logLik(object[[i]]) l = length(object) TMP=vector("list", l) for(i in 1:l){ TMP[[i]] = optNNI(object[[i]], INDEX) } loglik=TMP[[1]][[1]] for(i in 2:l)loglik=loglik+TMP[[i]][[1]] swap <- 0 candidates <- loglik > loglik0 while (any(candidates)) { ind = which.max(loglik) loglik[ind] = -Inf if (ind%%2) swap.edge = c(2, 3) else swap.edge = c(2, 4) tree2 <- changeEdge(tree, INDEX[(ind + 1)%/%2, swap.edge], INDEX[(ind + 1)%/%2, ], TMP[[1]][[2]][ind, ]) tmpll = 0 for(i in 1:l){ if(!AllEdge)tree2 <- changeEdge(object[[i]]$tree, INDEX[(ind + 1)%/%2, swap.edge], INDEX[(ind + 1)%/%2, ], TMP[[i]][[2]][ind, ]) tmpll <- tmpll + update(object[[i]], tree = tree2)$logLik } if (tmpll < loglik0) candidates[ind] = FALSE if (tmpll > loglik0) { swap = swap + 1 tree <- tree2 indi <- which(rep(colSums(apply(INDEX, 1, match, INDEX[(ind + 1)%/%2, ], nomatch = 0)) > 0, each = 2)) candidates[indi] <- FALSE loglik[indi] <- -Inf for(i in 1:l){ if(!AllEdge)tree2 <- changeEdge(object[[i]]$tree, INDEX[(ind + 1)%/%2, swap.edge], INDEX[(ind + 1)%/%2, ], TMP[[i]][[2]][ind, ]) object[[i]] <- update(object[[i]], tree = tree2) } loglik0 = 0 for(i in 1:l)loglik0 = loglik0 + logLik(object[[i]]) cat(loglik0, "\n") } } if(AllEdge)object <- optimPartEdge(object) attr(object,"swap") = swap object } ### this is the version we want to optimise pml.fit4 <- function (tree, data, bf = rep(1/length(levels), length(levels)), shape = 1, k = 1, Q = rep(1, length(levels) * (length(levels) - 1)/2), levels = attr(data, "levels"), inv = 0, rate = 1, g = NULL, w = NULL, eig = NULL, INV = NULL, ll.0 = NULL, llMix = NULL, wMix = 0, ..., site=FALSE) { weight <- as.double(attr(data, "weight")) nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) nTips <- as.integer(length(tree$tip.label)) k <- as.integer(k) m = 1 if (is.null(eig)) eig = edQt(bf = bf, Q = Q) if (is.null(w)) { w = rep(1/k, k) if (inv > 0) w <- (1 - inv) * w if (wMix > 0) w <- (1 - wMix) * w } if (is.null(g)) { g = discrete.gamma(shape, k) if (inv > 0) g <- g/(1 - inv) g <- g * rate } # inv0 <- inv if(any(g<.gEps)){ for(i in 1:length(g)){ if(g[i]<.gEps){ inv <- inv+w[i] } } w <- w[g>.gEps] g <- g[g>.gEps] # kmax <- k k <- length(w) } # .iind <- get(".iind", parent.frame()) # .INV <- get(".INV", parent.frame()) # if(is.null(ll.0)) if (is.null(ll.0)){ ll.0 <- numeric(attr(data,"nr")) } if(inv>0) ll.0 <- as.matrix(INV %*% (bf * inv)) # if(inv>0) # ll.0 <- as.matrix(.INV %*% (bf * inv)) node <- tree$edge[, 1] edge <- tree$edge[, 2] # root <- as.integer(node[length(node)]) el <- as.double(tree$edge.length) node = as.integer(node - nTips - 1L) # min(node)) edge = as.integer(edge - 1L) contrast = attr(data, "contrast") nco = as.integer(dim(contrast)[1]) siteLik <- .Call("PML4", dlist=data, el, as.double(w), as.double(g), nr, nc, k, eig, as.double(bf), node, edge, nTips, nco, contrast, N=as.integer(length(edge))) # if(inv>0) siteLik[.iind] = log(exp(siteLik[.iind]) + ll.0[.iind]) ind = which(ll.0>0) # if(!is.null(ll.0)) siteLik[.iind] = log(exp(siteLik[.iind]) + ll.0[.iind]) if(!is.null(ll.0)) siteLik[ind] = log(exp(siteLik[ind]) + ll.0[ind]) # diese 2 zeilen darueber in C, weight, nr, nc, nco, contrast in C struct definieren if(wMix >0) siteLik <- log(exp(siteLik) * (1-wMix) + llMix ) loglik <- sum(weight * siteLik) if(!site) return(loglik) return(list(loglik=loglik, siteLik=siteLik)) #, resll=resll } pml.fit <- function (tree, data, bf = rep(1/length(levels), length(levels)), shape = 1, k = 1, Q = rep(1, length(levels) * (length(levels) - 1)/2), levels = attr(data, "levels"), inv = 0, rate = 1, g = NULL, w = NULL, eig = NULL, INV = NULL, ll.0 = NULL, llMix = NULL, wMix = 0, ..., site=FALSE) { Mkv=FALSE weight <- as.double(attr(data, "weight")) nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) nTips <- as.integer(length(tree$tip.label)) k <- as.integer(k) m = 1 if (is.null(eig)) eig = edQt(bf = bf, Q = Q) if (is.null(w)) { w = rep(1/k, k) if (inv > 0) w <- (1 - inv) * w if (wMix > 0) w <- (1 - wMix) * w } if (is.null(g)) { g = discrete.gamma(shape, k) if (inv > 0) g <- g/(1 - inv) g <- g * rate } # inv0 <- inv if(any(g<.gEps)){ for(i in 1:length(g)){ if(g[i]<.gEps){ inv <- inv+w[i] } } w <- w[g>.gEps] g <- g[g>.gEps] # kmax <- k k <- length(w) } if (is.null(INV)) INV <- Matrix(lli(data, tree), sparse=TRUE) if (is.null(ll.0)){ ll.0 <- numeric(attr(data,"nr")) } if(inv>0) ll.0 <- as.matrix(INV %*% (bf * inv)) if(Mkv) ll.0 <- as.matrix(INV %*% bf) if (wMix > 0) ll.0 <- ll.0 + llMix node <- tree$edge[, 1] edge <- tree$edge[, 2] # root <- as.integer(node[length(node)]) el <- as.double(tree$edge.length) node = as.integer(node - nTips - 1L) # min(node)) edge = as.integer(edge - 1L) contrast = attr(data, "contrast") nco = as.integer(dim(contrast)[1]) # dlist=data, nr, nc, weight, k ausserhalb definieren # pmlPart einbeziehen # as.double(w), resll <- .Call("PML0", dlist=data, el, as.double(g), nr, nc, k, eig, as.double(bf), node, edge, nTips, nco, contrast, N=as.integer(length(edge))) # sort(INV@i)+1L ind = which(ll.0>0) # automatic in INV gespeichert sca = .Call("rowMax", resll, length(weight), as.integer(k)) + 1 # nr statt length(weight) lll = resll - sca lll <- exp(lll) lll <- (lll%*%w) if(Mkv) p0 <- sum(exp(log(lll[ind]) + sca[ind])) lll[ind] = lll[ind] + exp(log(ll.0[ind])-sca[ind]) siteLik <- lll siteLik <- log(siteLik) + sca # needs to change if(wMix >0) siteLik <- log(exp(siteLik) * (1-wMix) + llMix ) if(Mkv)siteLik <- siteLik + log(1-p0) loglik <- sum(weight * siteLik) if(!site) return(loglik) resll = exp(resll) return(list(loglik=loglik, siteLik=siteLik, resll=resll)) } pml <- function (tree, data, bf = NULL, Q = NULL, inv = 0, k = 1, shape = 1, rate = 1, model=NULL, ...) { Mkv = FALSE if(!is.null(model) && model=="Mkv")Mkv = TRUE call <- match.call() extras <- match.call(expand.dots = FALSE)$... pmla <- c("wMix", "llMix") existing <- match(pmla, names(extras)) wMix <- ifelse(is.na(existing[1]), 0, eval(extras[[existing[1]]], parent.frame()) ) llMix <- ifelse(is.na(existing[2]), 0, eval(extras[[existing[2]]], parent.frame()) ) if(!inherits(tree,"phylo")) stop("tree must be of class phylo") # if(is.null(tree$edge.length)){ # warning("tree has no edge length, used nnls.phylo to assign them") # tree <- nnls.phylo(tree, dist.ml(data)) # } if(any(duplicated(tree$tip.label))) stop("tree must have unique labels!") if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") if(any(tree$edge.length < 0)) { tree$edge.length[tree$edge.length < 0] <- 1e-08 message("negative edges length changed to 0!") } if (class(data)[1] != "phyDat") stop("data must be of class phyDat") if(is.null(tree$edge.length)) stop("tree must have edge weights") if(any(is.na(match(tree$tip.label, attr(data, "names"))))) stop("tip labels are not in data") data <- subset(data, tree$tip.label) # needed levels <- attr(data, "levels") if(Mkv){ data <- cbind(constSitePattern(length(tree$tip.label),levels, tree$tip.label), data) attr(data, "weight")[1:attr(data, "nc")] <- 0.0 } weight <- attr(data, "weight") nr <- attr(data, "nr") type <- attr(data,"type") if(type=="AA" & !is.null(model)){ model <- match.arg(model, .aamodels) getModelAA(model, bf=is.null(bf), Q=is.null(Q)) } if(type=="CODON"){ Q <- as.numeric(.syn > 0) } if (is.null(bf)) bf <- rep(1/length(levels), length(levels)) if (is.null(Q)) Q <- rep(1, length(levels) * (length(levels) - 1)/2) m <- 1 eig <- edQt(bf = bf, Q = Q) w <- rep(1/k, k) if (inv > 0) w <- (1 - inv) * w if (wMix > 0) w <- wMix * w g <- discrete.gamma(shape, k) if (inv > 0) g <- g/(1 - inv) g <- rate * g inv0 <- inv kmax <- k if(any(g<.gEps)){ for(i in 1:length(g)){ if(g[i]<.gEps){ inv <- inv+w[i] } } w <- w[g>.gEps] g <- g[g>.gEps] k <- length(w) } INV <- Matrix(lli(data, tree), sparse=TRUE) ll.0 <- as.matrix(INV %*% (bf * inv)) # if(Mkv) ll.0 <- as.matrix(INV %*% rep(1, length(bf))) if(wMix>0) ll.0 <- ll.0 + llMix nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) nTips <- as.integer(length(tree$tip.label)) on.exit(.C("ll_free")) .C("ll_init", nr, nTips, nc, as.integer(k)) tmp <- pml.fit(tree, data, bf, shape = shape, k = k, Q = Q, levels = attr(data, "levels"), inv = inv, rate = rate, g = g, w = w, eig = eig, INV = INV, ll.0 = ll.0, llMix = llMix, wMix = wMix, site=TRUE) #, Mkv=Mkv df <- ifelse(is.ultrametric(tree), tree$Nnode, length(tree$edge.length)) df <- switch(type, DNA = df + (kmax>1) + (inv0 > 0) + length(unique(bf)) - 1 + length(unique(Q)) - 1, AA = df + (kmax>1) + (inv0 > 0) , CODON = df + (kmax>1) + (inv0 > 0) + length(unique(bf)) - 1, USER = df + (kmax>1) + (inv0 > 0) + length(unique(bf)) - 1 + length(unique(Q)) - 1) # if(type=="AA" & !is.null(model)){ # df <- df + (kmax>1) + (inv0 > 0) #+ length(unique(bf)) - 1 # } # if(type=="CODON"){ # df <- df + (kmax>1) + (inv0 > 0) + length(unique(bf)) - 1 # } # else df = df + (kmax>1) + (inv0 > 0) + length(unique(bf)) - 1 + length(unique(Q)) - 1 result = list(logLik = tmp$loglik, inv = inv, k = kmax, shape = shape, Q = Q, bf = bf, rate = rate, siteLik = tmp$siteLik, weight = weight, g = g, w = w, eig = eig, data = data, model=model, INV = INV, ll.0 = ll.0, tree = tree, lv = tmp$resll, call = call, df=df, wMix=wMix, llMix=llMix) #, Mkv=Mkv if(type=="CODON"){ result$dnds <- 1 result$tstv <- 1 } class(result) = "pml" result } optimRooted <- function(tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, INV=INV, control = pml.control(epsilon = 1e-08, maxit = 25, trace=0), ...){ tree$edge.length[tree$edge.length < 1e-08] <- 1e-08 # nicht richtig nTips = as.integer(length(tree$tip.label)) k = length(w) # optimising rooted triplets optRoot0 <- function(t, tree, data, g, w, eig, bf, ll.0, k){ l = length(tree$edge.length) tree$edge.length[1:(l-1)] = tree$edge.length[1:(l-1)] + t tree$edge.length[l] = tree$edge.length[l] - t loglik = pml.fit4(tree, data, bf=bf, g=g, w=w, eig=eig, INV=INV, ll.0=ll.0, k=k) # loglik } # optim edges leading to the root optRoot2 <- function(t, tree, data, g, w, eig, bf, ll.0, k){ tree$edge.length = tree$edge.length + t #c(el1+t, el2-t) loglik = pml.fit4(tree, data, bf=bf, g=g, w=w, eig=eig, INV=INV, ll.0=ll.0, k=k) #, INV=INV loglik } # scale whole tree scaleEdges = function(t=1, trace=0, tree, data, ...){ fn = function(t, tree, data,...){ tree$edge.length = tree$edge.length*t pml.fit4(tree, data, ...) } optimize(f=fn, interval=c(0.25,4), tree=tree, data=data, ..., maximum = TRUE, tol = .00001) } parent = tree$edge[, 1] child = tree$edge[, 2] anc <- Ancestors(tree, 1:max(tree$edge), "parent") sibs <- Siblings(tree, 1:max(tree$edge)) allKids <- cvector <- allChildren(tree) rootNode = getRoot(tree) child2 = orderNNI(tree, nTips) #(cvector, rootNode, nTips, TRUE) lengthList <- edgeList <- vector("list", max(tree$edge)) for(i in tree$edge[,2]){ pa <- anc[i] kids <- sibs[[i]] if(pa!=rootNode){ edgeList[[i]] <- cbind(pa, c(anc[pa], kids)) lengthList[[i]] <- c(pa, kids) } else{ edgeList[[i]] <- cbind(pa, kids) lengthList[[i]] <- kids } } treeList <- vector("list", max(child2)) for(i in child2){ pa <- anc[i] kids <- allKids[[i]] treeList[[i]] <- cbind(i, c(kids, pa)) } ll <- pml.fit4(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) #, INV=INV # if(control$trace>2)cat("ll", ll, "\n") eps=10 iter = 1 EL = numeric(max(tree$edge)) EL[tree$edge[, 2]] = tree$edge.length eps0 =1e-8 tmp <- scaleEdges(t, trace=0, tree, data, bf = bf, k=k, ll.0=ll.0, eig = eig, w=w, g=g) # if(control$trace>2)cat("scale", tmp[[2]], "\n") t = tmp[[1]] tree$edge.length = tree$edge.length*t el = tree$edge.length EL[tree$edge[, 2]] = tree$edge.length ll2 <- pml.fit4(tree, data, bf=bf, k=k, eig=eig, INV=INV, ll.0=ll.0, w=w, g=g) tmptree = tree while(eps>control$eps && iter < control$maxit){ ll2 <- pml.fit4(tree, data, bf=bf, k=k, eig=eig, INV=INV, ll.0=ll.0, w=w, g=g) loli <- rootNode children <- allKids[[rootNode]] kidsEl <- EL[children] minEl = min(kidsEl) kidsEl = kidsEl - minEl tmptree$edge = cbind(rootNode, children) tmptree$edge.length = kidsEl t <- optimize(f=optRoot2,interval=c(1e-8,3), tmptree, data=data, k=k, g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, maximum=TRUE) optRoot2(t[[1]], tmptree, data=data, k=k, g=g, w=w, eig=eig, bf=bf, ll.0=ll.0) # if(control$trace>2)cat("optRoot", t[[2]], "\n") ll3 = t[[2]] EL[children] = kidsEl + t[[1]] tree$edge.length = EL[tree$edge[, 2]] ll2 <- pml.fit4(tree, data, bf=bf, k=k, eig=eig, INV=INV, ll.0=ll.0, w=w, g=g) for(i in 1:length(child2)){ dad = child2[i] # if(dad>nTips ){ # kann raus pa = anc[dad] while(loli != pa){ tmpKids= cvector[[loli]] tmpEdge = cbind(loli, tmpKids) pml.move(tmpEdge, EL[tmpKids], data, g, w, eig, k, nTips, bf) loli=anc[loli] } pml.move(edgeList[[dad]], EL[lengthList[[dad]]], data, g, w, eig, k, nTips, bf) children <- allKids[[dad]] kidsEl <- EL[children] minEl = min(kidsEl) maxEl = EL[dad] EDGE = treeList[[dad]] tmptree$edge = EDGE tmptree$edge.length = c(kidsEl, maxEl) t0 = optRoot0(0, tmptree, data, g, w, eig, bf, ll.0, k) t = optimize(f=optRoot0, interval=c(-minEl+eps0,maxEl-eps0), tmptree, data=data, g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, k=k, maximum=TRUE) # if(control$trace>2) cat("edge", t[[2]], "\n") if(!is.nan(t[[2]]) & t[[2]] > ll3){ optRoot0(t[[1]], tmptree, data=data, g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, k=k) EL[children] = kidsEl+t[[1]] EL[dad] = maxEl-t[[1]] ll3 = t[[2]] } else optRoot0(0, tmptree, data, g, w, eig, bf, ll.0, k) loli = dad # } } tree$edge.length = EL[tree$edge[, 2]] ll2 <- pml.fit(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) eps = (ll - ll2) / ll2 if(control$trace>1) cat("optimRooted: ", ll, " -> ", ll2, "\n") ll=ll2 iter = iter+1 } list(tree=tree, logLik=ll, c(eps=eps, iter=iter)) } index.nni <- function (ch, cvector, pvector, root) { p1 = pvector[ch] k12 = cvector[[ch]] k3 = cvector[[p1]] k3 = k3[k3 != ch] kids = c(k12, k3, ch) parents = c(ch, ch, p1, p1) if (p1 != root){ k4 = pvector[p1] kids = c(kids, k4) parents = c(parents, p1) } cbind(parents, kids) } orderNNI <- function (tree, nTips){ res = reorder(tree)$edge[,2] res = res[res>nTips] res } rooted.nni <- function(tree, data, eig, w, g, bf, rate, ll.0, INV, control = pml.control(epsilon = 1e-08, maxit = 25, trace=0), ...){ ind0 = which(ll.0>0) contrast = attr(data, "contrast") tree$edge.length[tree$edge.length < 1e-08] <- 1e-08 nTips = as.integer(length(tree$tip.label)) k = length(w) if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder.phylo(tree, "postorder") if(!is.rooted(tree))stop("tree must be rooted") attr(tree, "order") = NULL weight = attr(data , "weight") nr = as.integer(attr(data , "nr")) nc = as.integer(attr(data , "nc")) getEL1 <- function(t, nh){ el = numeric(4) if(nh[1] > nh[2]){ el[2] = nh[1] -nh[2] tnh = nh[1] + t[1] } else{ el[1] = nh[2] -nh[1] tnh = nh[2] + t[1] } el[1:2] = el[1:2] + t[1] if(tnh > nh[3]) el[3] = el[3] + tnh - nh[3] else el[4] = el[4] - tnh + nh[3] el[3:4] = el[3:4] + t[2] el } optRootU <- function(t, tree, data, bf, g, w, eig, ll.0, k, INV, nh){ tree$edge.length = getEL1(t, nh) pml.fit4(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) # pml.fit(tree, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, INV=INV) } getEL2 = function(t, nh){ el = numeric(5) eps= 1e-6 nh12.min = max(nh[1:2]) + eps nh123.min = max(nh12.min, nh[3]) + eps l1 = nh[5] - nh123.min - eps el[5] = l1 * t[1] + eps nh123 = nh[5] - el[5] l2 = nh123 - nh12.min - eps nh12 = nh12.min + l2 * t[2] el[1] = nh12 - nh[1] el[2] = nh12 - nh[2] el[3] = nh123 - nh[3] el[4] = nh123 - nh12 el } optEdgeU <- function(t, tree, data, bf, g, w, eig, ll.0, k, INV, nh){ tree$edge.length = getEL2(t, nh) # pml.fit(tree, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, INV=INV) pml.fit4(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) } child = tree$edge[, 2] parent = tree$edge[, 1] # ll <- pml.fit(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) ll <- pml.fit4(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) llstart <- ll eps=.00001 iter = 1 EL = numeric(max(tree$edge)) EL[tree$edge[,2]] = tree$edge.length change = numeric(length(parent)) + 1 rootNode = getRoot(tree) anc = Ancestors(tree, 1:max(tree$edge), "parent") cvector = allChildren(tree) sibs <- Siblings(tree, 1:max(tree$edge)) child2 = orderNNI(tree, nTips) while(iter < 2){ ll2 <- pml.fit(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) nh=nodeHeight(tree) loli <- rootNode pa <-rootNode nchanges = 0 ind=1 i <- 1 tree1 <- tree2 <- tree3 <- tree for(i in 1:length(child2)){ ch <- child2[i] dad <- anc[ch] if(ch>nTips){ EL[tree$edge[,2]] = tree$edge.length pa <- ifelse(dad==rootNode, rootNode ,anc[dad]) # should avoid unnecessary movements while(loli != dad && loli!=rootNode){ if(loli==pa){ tmpKids <- sibs[[dad]] tmpEdge <- cbind(pa, c(anc[pa], tmpKids)) pml.move(tmpEdge, EL[c(pa, tmpKids)], data, g, w, eig, k, nTips, bf) # cat("move from pa to dad \n") loli = dad } else{ # cat("move loli up", loli, "dad", dad, "pa", pa, "ch", ch, "\n") tmpKids = cvector[[loli]] tmpEdge = cbind(loli, tmpKids) pml.move(tmpEdge, EL[tmpKids], data, g, w, eig, k, nTips, bf) loli=anc[loli] } } if(loli == rootNode && dad!= loli){ # update all nodes pml.fit(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) # cat("move down loli", loli, "dad", dad, "pa", pa, "ch", ch, "\n") gd <- rev(Ancestors(tree, ch, "all")) tmpKids <- sibs[[gd[2]]] tmpEdge <- cbind(rootNode, tmpKids) pml.move(tmpEdge, EL[tmpKids], data, g, w, eig, k, nTips, bf) gd = gd[-1] while(length(gd)>1){ tmpKids <- sibs[[gd[2]]] tmpEdge = cbind(gd[1], c(anc[gd[1]],tmpKids)) pml.move(tmpEdge, EL[c(gd[1],tmpKids)], data, g, w, eig, k, nTips, bf) gd = gd[-1] } loli=dad } X1 <- index.nni(ch, cvector, anc, rootNode) # if(loli!=rootNode){ # tmpKids <- c(ch, sibs[[ch]]) # tmpEdge <- cbind(dad, c(pa, tmpKids)) # tree1$edge <- tmpEdge # tree1$edge.length = EL[c(dad, tmpKids)] # ll0 = pml.fit(tree1, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, INV=INV) # cat("triplet", ll0, "\n") # } if(loli!=rootNode){ tree1$edge <- X1 tree1$edge.length = abs(nh[X1[,1]] - nh[X1[,2]]) # ll0 = pml.fit(tree1, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, INV=INV) ll0 <- pml.fit4(tree1, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) # cat("quartet", ll0, ch, dad, "\n") } if(dad == rootNode){ ll0 = pml.fit(tree, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, INV=INV) # cat("at root", ll0, ch, dad, "\n") ind2 = c(1,3,2,4) ind3 = c(3,2,1,4) X2 = X3 = X1 X2[,2] = X1[ind2, 2] X3[,2] = X1[ind3, 2] tree1$edge = X1 tree2$edge = X2 tree3$edge = X3 edge1 <- X1[,2] edge1[4] = dad res1 =optim(par = c(.1,.1), optRootU, gr=NULL, tree=tree1, data=data, nh=nh[X1[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k, method = "L-BFGS-B", lower = 1e-8, upper = 5, control = list(fnscale=-1)) res2 =optim(par = c(.1,.1), optRootU, gr=NULL, tree=tree2, data=data, nh=nh[X2[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k, method = "L-BFGS-B", lower = 1e-8, upper = 5, control = list(fnscale=-1)) res3 =optim(par = c(.1,.1), optRootU, gr=NULL, tree=tree3, data=data, nh=nh[X3[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k, method = "L-BFGS-B", lower = 1e-8, upper = 5, control = list(fnscale=-1)) ind = which.max(c(res1[[2]], res2[[2]], res3[[2]])) if(control$trace>2) cat("root", c(res1[[2]], res2[[2]], res3[[2]]), "\n") if(ind==1){ ll2 = res1[[2]] optRootU(t=res1[[1]], tree=tree1, data=data, nh=nh[X1[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k) tmpEL = getEL1(res1[[1]], nh[X1[,2]]) tree = changeEdgeLength(tree, X1[,2], tmpEL) } if(ind==2){ ll2 = res2[[2]] optRootU(t=res2[[1]], tree=tree2, data=data, nh=nh[X2[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k) tmpEL = getEL1(res2[[1]], nh[X2[,2]]) tree <- changeEdge(tree, X1[c(2,3),2]) tree = changeEdgeLength(tree, X2[,2], tmpEL) } if(ind==2){ ll2 = res3[[2]] optRootU(t=res3[[1]], tree=tree3, data=data, nh=nh[X3[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k) tmpEL = getEL1(res3[[1]], nh[X3[,2]]) tree <- changeEdge(tree, X1[c(1,3),2]) tree = changeEdgeLength(tree, X3[,2], tmpEL) } } else{ loli = dad ind2 = c(1,3,2,4,5) ind3 = c(3,2,1,4,5) X2 = X3 = X1 X2[,2] = X1[ind2, 2] X3[,2] = X1[ind3, 2] tree1$edge = X1 tree2$edge = X2 tree3$edge = X3 tt = c(.3,.5) res1 =optim(par = tt, optEdgeU, gr=NULL, tree=tree1, data, nh=nh[X1[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k, method = "L-BFGS-B", lower = 1e-4, upper = 1-1e-4, control = list(fnscale=-1)) res2 =optim(par = tt, optEdgeU, gr=NULL, tree=tree2, data, nh=nh[X2[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k, method = "L-BFGS-B", lower = 1e-4, upper = 1-1e-4, control = list(fnscale=-1)) res3 =optim(par = tt, optEdgeU, gr=NULL, tree=tree3, data, nh=nh[X3[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k, method = "L-BFGS-B", lower = 1e-4, upper = 1-1e-4, control = list(fnscale=-1)) ind = which.max(c(res1[[2]], res2[[2]], res3[[2]])) if(control$trace>2) cat("edge", ch, ":", c(res1[[2]], res2[[2]], res3[[2]]), "\n") ll3 = max(c(res1[[2]], res2[[2]], res3[[2]])) if( (ll3 - 1e-5*ll3) < ll2){ loli = rootNode # ll2 <- pml.fit(tree, data, bf=bf, eig=eig, ll.0=ll.0, w=w, g=g) ll2 <- pml.fit4(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) nh=nodeHeight(tree) EL[tree$edge[,2]] = tree$edge.length ind=0 } else{ if(ind==1){ ll2 = res1[[2]] optEdgeU(res1[[1]], tree=tree1, data, nh=nh[X1[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k) tmpEL = getEL2(res1[[1]], nh[X1[,2]]) tmpE = X1[,2] tmpE[5] = X1[5,1] tree = changeEdgeLength(tree, tmpE, tmpEL) } if(ind==2){ ll2 = res2[[2]] optEdgeU(res2[[1]], tree=tree2, data, nh=nh[X2[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k) tmpEL = getEL2(res2[[1]], nh[X2[,2]]) tmpE = X2[,2] tmpE[5] = X1[5,1] tree <- changeEdge(tree, X1[c(2,3),2]) tree = changeEdgeLength(tree, tmpE, tmpEL) } if(ind==3){ ll2 = res3[[2]] optEdgeU(res3[[1]], tree=tree3, data, nh=nh[X3[,2]], g=g, w=w, eig=eig, bf=bf, ll.0=ll.0, INV=INV, k=k) tmpEL = getEL2(res3[[1]], nh[X3[,2]]) tmpE = X3[,2] tmpE[5] = X1[5,1] tree <- changeEdge(tree, X1[c(1,3),2]) tree = changeEdgeLength(tree, tmpE, tmpEL) } } } nh=nodeHeight(tree) EL[tree$edge[,2]] = tree$edge.length loli = dad if(ind>1){ # print("NNI swap") nchanges = nchanges+1 anc = Ancestors(tree, 1:max(tree$edge), "parent") cvector = allChildren(tree) sibs <- Siblings(tree, 1:max(tree$edge)) } } } # ll2 <- pml.fit(tree, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, INV=INV) ll2 <- pml.fit4(tree, data, bf=bf, k=k, eig=eig, ll.0=ll.0, INV=INV, w=w, g=g) eps = (ll - ll2) / ll2 if(control$trace>1) cat(ll, " -> ", ll2, "\n") if(control$trace>1) cat("swap:", nchanges) ll=ll2 iter = iter+1 } list(tree=tree, logLik=ll, iter=iter, swap=nchanges) } # maybe into treeManipulation addAllTips <- function(tree, mapping){ tree2 <- tree uni <- unique(mapping[,2]) for(i in uni) tree2 = addTips2Tree(tree2, mapping[mapping[,2]==i,1],i) tree2 } addTips2Tree <- function (tree, tips, where){ nTips <- length(tree$tip.label) edge = tree$edge where <- match(where, tree$tip.label) ind = match(where, edge[,2]) nt <- as.integer(length(tips)) edge[edge>nTips] = edge[edge>nTips]+nt m = max(edge) edge[ind,2] <- m+1L newEdge <- cbind(m+1L,c(where,(nTips+1L):(nTips+nt))) edge <- rbind(newEdge, edge) storage.mode(edge) <- "integer" if(!is.null(tree$edge.length)) tree$edge.length = c(rep(0, nrow(newEdge)), tree$edge.length) tree$edge = edge tree$Nnode = tree$Nnode + 1L tree$tip.label = c(tree$tip.label, tips) attr(tree, "order") <- NULL tree <- reorder(tree, "postorder") tree } optim.pml <- function (object, optNni = FALSE, optBf = FALSE, optQ = FALSE, optInv = FALSE, optGamma = FALSE, optEdge = TRUE, optRate = FALSE, optRooted=FALSE, control = pml.control(epsilon = 1e-8, maxit = 10, trace = 1L), model = NULL, rearrangement = ifelse(optNni, "NNI","none"), subs = NULL, ratchet.par = list(iter = 20L, maxit = 100L, prop = 1/3), ...) { optRatchet = FALSE optRatchet2 = FALSE if(rearrangement == "none"){ optNni = FALSE optRatchet = FALSE optRatchet2 = FALSE } if(rearrangement == "NNI")optNni = TRUE if(rearrangement == "stochastic") optRatchet = TRUE if(rearrangement == "ratchet") optRatchet2 = TRUE extras <- match.call(expand.dots = FALSE)$... pmla <- c("wMix", "llMix") wMix <- object$wMix llMix <- object$llMix # Mkv <- object$Mkv Mkv <- FALSE if(is.null(llMix)) llMix=0 if (!is.null(extras)) { names(extras) <- pmla[pmatch(names(extras), pmla)] existing <- match(pmla, names(extras)) if (!is.na(existing[1])) wMix <- eval(extras[[existing[1]]], parent.frame()) if (!is.na(existing[2])) llMix <- eval(extras[[existing[2]]], parent.frame()) } tree = object$tree call = object$call ratchet=FALSE ratchet2=FALSE if(optRatchet == TRUE){ if(optRooted==FALSE){ optNni=TRUE optEdge=TRUE ratchet=TRUE } } if(optRatchet2 == TRUE){ optNni=TRUE optEdge=TRUE ratchet2=TRUE } data <- object$data addTaxa <- FALSE if(optNni) { # test this more dup <- duplicated(data) # duplicated_phyDat(data) if(any(dup)){ # && optNNI orig.data <- data addTaxa <- TRUE labels <- names(data) ldup <- labels[dup] ind2 <- match(subset(data, dup), data) tree2 <- drop.tip(tree, ldup) tree <- reorder(tree2, "postorder") mapping <- cbind(labels[dup], labels[ind2]) } if(!is.binary.tree(tree)) tree = multi2di(tree) optEdge = TRUE } if(is.rooted(tree)) { if(optRooted==FALSE && optEdge==TRUE){ tree = unroot(tree) attr(tree, "order") <- NULL tree = reorder(tree, "postorder") warning("I unrooted the tree", call. = FALSE) } } if(is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") if(any(tree$edge.length < 1e-08)) { tree$edge.length[tree$edge.length < 1e-08] <- 1e-08 # save to change to new update.pml object <- update.pml(object, tree = tree) } if(optEdge & optRate) { warning("You can't optimise edges and rates at the same time, only edges are optimised!", call. = FALSE) optRate = FALSE } if(optRooted){ optEdge = FALSE if(!is.rooted(tree)) stop("Tree must be rooted!") if(!is.ultrametric(tree)) stop("Tree must be ultrametric!") } trace <- control$trace data = subset(data, tree$tip.label) type <- attr(data, "type") if (type == "AA" & !is.null(model)){ object = update(object, model=model) } if (type == "CODON") { if(is.null(model)) model <- "codon1" model <- match.arg(model, c("codon0", "codon1", "codon2", "codon3", "YN98")) dnds <- object$dnds tstv <- object$tstv if(!is.null(model)){ if(model == "codon0") optQ = FALSE else optQ = TRUE if(model == "YN98") optBf = TRUE } } Q = object$Q if(is.null(subs)) subs = c(1:(length(Q) - 1), 0) bf = object$bf eig = object$eig inv = object$inv k = object$k if(k==1 & optGamma){ optGamma = FALSE message('only one rate class, ignored optGamma') } # if(Mkv==TRUE & optInv==TRUE){ # optInv = FALSE # message('cannot estimate invariant sites and Mkv model, ignored optInv') # } shape = object$shape w = object$w g = object$g if (type == "DNA" & !is.null(model)) { tmp = subsChoice(model) optQ = tmp$optQ if (!optQ) Q = rep(1, 6) optBf = tmp$optBf if (!optBf) bf = c(0.25, 0.25, 0.25, 0.25) subs = tmp$subs } ll0 <- object$logLik INV <- object$INV ll.0 <- object$ll.0 rate <- object$rate ll = ll0 ll1 = ll0 opti = TRUE nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) nTips <- as.integer(length(tree$tip.label)) # on.exit(.C("ll_free")) # .C("ll_init", nr, nTips, nc, as.integer(k)) # .INV <- .iind <- NULL on.exit({ tmp <- pml.fit(tree, data, bf, shape = shape, k = k, Q = Q, levels = attr(data, "levels"), inv = inv, rate = rate, g = g, w = w, eig = eig, INV = INV, ll.0 = ll.0, llMix = llMix, wMix = wMix, site = TRUE) # df <- ifelse(optRooted, tree$Nnode, length(tree$edge.length)) # length(tree$edge.length) # nTips # nNodes # uniqueTips # uniqueNodes # kmax>1 # bf # Q if(addTaxa){ tree <- addAllTips(tree, mapping) data <- orig.data } df <- ifelse(optRooted, tree$Nnode, length(tree$edge.length)) df <- switch(type, DNA = df + (k>1) + (inv > 0) + length(unique(bf)) - 1 + length(unique(Q)) - 1, AA = df + (k>1) + (inv > 0) + optBf * (length(unique(bf)) - 1), CODON = df + (k>1) + (inv > 0) + length(unique(bf)) - 1 + (dnds != 1) + (tstv != 1), USER = df + (k>1) + (inv > 0) + length(unique(bf)) - 1 + length(unique(Q)) - 1) object = list(logLik = tmp$loglik, inv = inv, k = k, shape = shape, Q = Q, bf = bf, rate = rate, siteLik = tmp$siteLik, weight = attr(data, "weight"), g = g, w = w, eig = eig, data = data, model = model, INV = INV, ll.0 = ll.0, tree = tree, lv = tmp$resll, call = call, df = df, wMix = wMix, llMix = llMix) if (type == "CODON") { object$dnds <- dnds object$tstv <- tstv } class(object) = "pml" extras = pairlist(bf = bf, Q = Q, inv = inv, shape = shape, rate = rate)[c(optBf, optQ, optInv, optGamma, optRate)] if (length(extras)) { existing <- !is.na(match(names(extras), names(call))) for (a in names(extras)[existing]) call[[a]] <- extras[[a]] if (any(!existing)) { call <- c(as.list(call), extras[!existing]) call <- as.call(call) } } object$call = call pml.free() return(object) }) pml.init(data, k) if (optEdge) { # check if non-negative least-squares is better for start of optimisation treetmp <- nnls.phylo(tree, dist.ml(data)) treetmp$edge.length[treetmp$edge.length < 1e-8] <- 1e-8 tmplogLik <- pml.fit(treetmp, data, bf, k = k, inv = inv, g = g, w = w, eig = eig, INV = INV, ll.0 = ll.0, llMix = llMix, wMix = wMix) if(tmplogLik>ll) tree <- treetmp res <- optimEdge(tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, INV=INV, control = pml.control(epsilon = 1e-07, maxit = 5, trace=trace - 1)) if(trace > 0) cat("optimize edge weights: ", ll, "-->", res[[2]], "\n") if (res[[2]] > ll){ ll <- res[[2]] tree <- res[[1]] } } if(optRooted){ res <- optimRooted(tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, INV=INV, control = pml.control(epsilon = 1e-07, maxit = 10, trace = trace-1)) if(trace > 0) cat("optimize edge weights: ", ll, "-->", res[[2]], "\n") if(res[[2]] > ll){ ll <- res[[2]] tree <- res[[1]] } } rounds = 1 while (opti) { if (optBf) { res = optimBf(tree, data, bf = bf, inv = inv, Q = Q, w = w, g = g, INV = INV, rate = rate, k = k, llMix = llMix) bf = res[[1]] eig = edQt(Q = Q, bf = bf) if (inv > 0) ll.0 <- as.matrix(INV %*% (bf * inv)) if (wMix > 0) ll.0 <- ll.0 + llMix if (trace > 0) cat("optimize base frequencies: ", ll, "-->", res[[2]], "\n") ll = res[[2]] } if (optQ) { if(type=="CODON"){ # if(is.null(model)) model <- "codon1" # model <- match.arg(model, c("codon0", "codon1", "codon2", "codon3", "YN98")) ab <- c(tstv, dnds) res <- switch(model, YN98 = optimCodon(tree,data, Q=rep(1,1830), subs=.sub, syn=.syn, bf = bf, w = w, g = g, inv = inv, INV = INV, ll.0 = ll.0, rate = rate, k = k, ab=log(ab), optK=TRUE, optW = TRUE), codon1 = optimCodon(tree,data, Q=rep(1,1830), subs=.sub, syn=.syn, bf = bf, w = w, g = g, inv = inv, INV = INV, ll.0 = ll.0, rate = rate, k = k, ab=log(ab), optK=TRUE, optW = TRUE), codon2 = optimCodon(tree,data, Q=rep(1,1830), subs=.sub, syn=.syn, bf = bf, w = w, g = g, inv = inv, INV = INV, ll.0 = ll.0, rate = rate, k = k, ab=log(ab), optK=FALSE, optW = TRUE), codon3 = optimCodon(tree,data, Q=rep(1,1830), subs=.sub, syn=.syn, bf = bf, w = w, g = g, inv = inv, INV = INV, ll.0 = ll.0, rate = rate, k = k, ab=log(ab), optK=TRUE, optW = FALSE)) tmp <- res[[5]] m = length(tmp) dnds = tmp[m] if(m>1) tstv <- tmp[1] } else res = optimQ(tree, data, Q = Q, subs = subs, bf = bf, w = w, g = g, inv = inv, INV = INV, ll.0 = ll.0, rate = rate, k = k) Q = res[[1]] eig = edQt(Q = Q, bf = bf) if (trace > 0) cat("optimize rate matrix: ", ll, "-->", res[[2]], "\n") ll = res[[2]] } if(optInv) { res = optimInv(tree, data, inv = inv, INV = INV, Q = Q, bf = bf, eig = eig, k = k, shape = shape, rate = rate) inv = res[[1]] w = rep(1/k, k) g = discrete.gamma(shape, k) w = (1 - inv) * w if (wMix > 0) w <- (1 - wMix) * w g = g/(1 - inv) g <- g * rate ll.0 = as.matrix(INV %*% (bf * inv)) if (wMix > 0) ll.0 <- ll.0 + llMix if (trace > 0) cat("optimize invariant sites: ", ll, "-->", res[[2]], "\n") ll = res[[2]] } if(optGamma) { res = optimGamma(tree, data, shape = shape, k = k, inv = inv, INV = INV, Q = Q, bf = bf, eig = eig, ll.0 = ll.0, rate = rate) shape = res[[1]] w = rep(1/k, k) g = discrete.gamma(shape, k) if (inv > 0) { w = (1 - inv) * w g = g/(1 - inv) } if (wMix > 0) w <- (1 - wMix) * w g <- g * rate if (trace > 0) cat("optimize shape parameter: ", ll, "-->", res[[2]], "\n") ll = res[[2]] } if(optRate) { res = optimRate(tree, data, rate = rate, inv = inv, INV = INV, Q = Q, bf = bf, eig = eig, k = k, shape = shape, w = w, ll.0 = ll.0) if (res[[2]] > ll)rate = res[[1]] g = discrete.gamma(shape, k) w = rep(1/k, k) if (inv > 0) { w = (1 - inv) * w g = g/(1 - inv) } if (wMix > 0) w <- (1 - wMix) * w g <- g * rate if (trace > 0) cat("optimize rate: ", ll, "-->", res[[2]], "\n") ll = res[[2]] } if (optEdge) { res <- optimEdge(tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, control = pml.control(epsilon = 1e-08, maxit = 5, trace=trace - 1)) if (trace > 0) cat("optimize edge weights: ", ll, "-->", res[[2]], "\n") if (res[[2]] > ll){ ll <- res[[2]] tree <- res[[1]] } } if(optRooted){ res <- optimRooted(tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, INV=INV, control = pml.control(epsilon = 1e-07, maxit = 10, trace = trace-1)) if(trace > 0) cat("optimize edge weights: ", ll, "-->", res[[2]], "\n") if (res[[2]] > ll){ ll <- res[[2]] tree <- res[[1]] } } if(optNni) { swap = 0 iter = 1 while (iter < 4) { if(optEdge){ # tmp <- pml.nni.old(tree, data, w, g, eig, bf, ll.0, ll, ...) tmp <- pml.nni(tree, data, w=w, g=g, eig=eig, bf=bf, ll.0=ll.0, ll=ll, INV=INV, ...) swap = swap + tmp$swap res <- optimEdge(tmp$tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, control = pml.control(epsilon = 1e-08, maxit = 3, trace=0)) ll2 = res[[2]] tree <- res[[1]] } else{ tmp <- rooted.nni(tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, INV=INV, ...) swap = swap + tmp$swap res <- optimRooted(tmp$tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, INV=INV, control = pml.control(epsilon = 1e-08, maxit = 5, trace = trace-1)) tree <- tmp$tree ll2 = tmp$logLik } if (trace > 0) cat("optimize topology: ", ll, "-->", ll2, "\n") ll = ll2 iter = iter + 1 if (tmp$swap == 0) { iter = 4 } } if (trace > 0) cat(swap, "\n") if (swap > 0) rounds = 1 if (swap == 0) optNni = FALSE } epsR <- 1e-8 if( (ratchet==TRUE) && (optNni == FALSE) ){ maxR = ratchet.par$iter maxit = ratchet.par$maxit kmax=1 i=1 while(i < maxit){ tree2<- rNNI(tree, moves=round(nTips * ratchet.par$prop), n=1) #tree <- rSPR(tree, moves=10, k=3, n=1) swap = 1 ll2 <- pml.fit(tree2, data, bf, shape = shape, k = k, Q = Q, levels = attr(data, "levels"), inv = inv, rate = rate, g = g, w = w, eig = eig, INV = INV, ll.0 = ll.0, llMix = llMix, wMix = wMix, site = FALSE) while(swap>0){ # tmp <- pml.nni(tree2, data, w, g, eig, bf, ll.0, ll=ll2, ...) tmp <- pml.nni(tree2, data, w=w, g=g, eig=eig, bf=bf, ll.0=ll.0, ll=ll2, INV=INV, ...) swap = tmp$swap res <- optimEdge(tmp$tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, control = pml.control(epsilon = 1e-08, maxit = 3, trace=0)) if (trace > 1) cat("optimize topology: ", ll2, "-->", res[[2]], "\n", "swaps:", tmp$swap, "\n") ll2 = res[[2]] tree2 <- res[[1]] } if(ll2 > (ll + epsR)){ tree <- tree2 ll <- ll2 kmax=1 } else kmax = kmax+1 if(trace > 0) print(paste("Ratchet iteration ", i,", best pscore so far:",ll)) i=i+1 if(kmax > maxR) i <- maxit } optNni = TRUE ratchet=FALSE rounds = 1 } if( (ratchet2==TRUE) && (optNni == FALSE) ){ if(optRooted){ FUN <- function(x, bf, Q, k, shape){ dm <- dist.ml(x, bf=bf, Q=Q, k=k, shape=shape) tr <- wpgma(dm) # nnls.phylo(tr, dm, TRUE) tr } } else{ FUN <- function(x, bf, Q, k, shape){ dm <- dist.ml(x, bf=bf, Q=Q) tr <- fastme.bal(dm, TRUE, FALSE, FALSE) tr$edge.length[tr$edge.length < 1e-8] = 1e-8 tr #nnls.phylo(tr, dm) } } maxR = ratchet.par$iter maxit = ratchet.par$maxit kmax=1 i=1 while(i < maxit){ tree2 <- bootstrap.phyDat(data, FUN, bs = 1, bf=bf, Q=Q, k=k, shape=shape)[[1]] tree2 <- checkLabels(tree2, tree$tip.label) tree2 <- reorder(tree2, "postorder") swap = 1 # ll2 <- pml.fit(tree, data, bf, shape = shape, k = k, Q = Q, # levels = attr(data, "levels"), inv = inv, rate = rate, # g = g, w = w, eig = eig, INV = INV, ll.0 = ll.0, llMix = llMix, # wMix = wMix, site = FALSE) #print(ll2) ll2 <- pml.fit(tree2, data, bf, shape = shape, k = k, Q = Q, levels = attr(data, "levels"), inv = inv, rate = rate, g = g, w = w, eig = eig, INV = INV, ll.0 = ll.0, llMix = llMix, wMix = wMix, site = FALSE) #print(ll2) while(swap>0){ # tmp <- pml.nni(tree2, data, w, g, eig, bf, ll.0, ll=ll2, ...) tmp <- pml.nni(tree2, data, w=w, g=g, eig=eig, bf=bf, ll.0=ll.0, ll=ll2, INV=INV, ...) swap = tmp$swap res <- optimEdge(tmp$tree, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, control = pml.control(epsilon = 1e-08, maxit = 3, trace=0)) if (trace > 1) cat("optimize topology: ", ll2, "-->", res[[2]], "\n", "swaps:", tmp$swap, "\n") ll2 = res[[2]] tree2 <- res[[1]] } if(ll2 > (ll + epsR)){ tree <- tree2 ll <- ll2 kmax=1 } else kmax = kmax+1 if(trace > 0) print(paste("Ratchet iteration ", i,", best pscore so far:",ll)) i=i+1 if(kmax > maxR) i <- maxit } optNni = TRUE ratchet2=FALSE rounds = 1 } if(rounds > control$maxit) opti <- FALSE if ((( ll1 - ll ) / ll < control$eps ) && rounds > 2 ) #abs(ll1 - ll) opti <- FALSE rounds = rounds + 1 ll1 = ll } } ######################################################################################### # # experimental pml.nni # ######################################################################################### indexNNI3 <- function(tree){ parent = tree$edge[, 1] child = tree$edge[, 2] ind = reorder(tree)$edge[,2] nTips <- length(tree$tip.label) ind = ind[ind>nTips] # ind = which(child %in% parent) Nnode = tree$Nnode # a d # \ / # e-----f c is closest to root, f is root from subtree # / \ # b c c(a,b,c,d,e,f) edgeMatrix = matrix(0,(Nnode-1), 6) pvector <- numeric(max(parent)) pvector[child] <- parent tips <- !logical(max(parent)) tips[parent] <- FALSE # cvector <- allCildren(tree) cvector <- vector("list",max(parent)) for(i in 1:length(parent)) cvector[[parent[i]]] <- c(cvector[[parent[i]]], child[i]) k=1L for(i in ind){ f = pvector[i] #f ab = cvector[[i]] #a,b ind1 = cvector[[f]] #c,d cd = ind1[ind1 != i] if(pvector[f])cd=c(pvector[f], cd) # cd edgeMatrix[k, 1:6] = c(ab,cd,i,f) k=k+1L } edgeMatrix } # EL ausserhalb index2tree <- function(x, tree, root=length(tree$tip.label)+1L){ EL = numeric(max(tree$edge)) EL[tree$edge[,2]] = tree$edge.length #edgeLengthIndex() pa = c(5L,5L,6L,6L,6L) ch = c(1L,2L,5L,4L,3L) elind = c(1L,2L,5L,4L,6L) # raus if(x[6L]==root) el = EL[x[ch]] #1 Zeile raus else el = EL[x[elind]] #edgeLengthIndex() # structure(list(edge = structure(c(x[pa], x[ch]), .Dim = c(5L, 2L)), # tip.label = tree$tip.label, edge.length = el, Nnode = 2L), # .Names = c("edge", "tip.label", "edge.length", "Nnode"), class = "phylo", order = "postorder") structure(list(edge = structure(c(x[pa], x[ch]), .Dim = c(5L, 2L)), edge.length = el, Nnode = 2L), .Names = c("edge", "edge.length", "Nnode"), class = "phylo", order = "postorder") } index2tree2 <- function(x, tree, root=length(tree$tip.label)+1L){ EL = numeric(max(tree$edge)) EL[tree$edge[,2]] = tree$edge.length pa = c(6L,6L,5L,5L,5L) ch = c(3L,4L,6L,1L,2L) elr = c(3L,4L,5L,1L,2L) eln = c(6L,4L,5L,1L,2L) if(x[6L]==root) el = EL[x[elr]] else el = EL[x[eln]] structure(list(edge = structure(c(x[pa], x[ch]), .Dim = c(5L, 2L)), edge.length = el, Nnode = 2L), .Names = c("edge", "edge.length", "Nnode"), class = "phylo", order = "postorder") } # weight, nr, nc, contrast, nco (Reihenfolge beibehalten) # INV raus # evi, eve, contrast2 ausserhalb definieren optimQuartet <- function (tree, data, eig, w, g, bf, rate, ll.0, nTips, weight, nr, nc, contrast, nco, llcomp =-Inf, control = pml.control(epsilon = 1e-08, maxit = 5, trace=0), ...) { # if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") { # tree <- reorder(tree, "postorder") # print("reorder") # } # nTips <- length(tree$tip.label) el <- tree$edge.length tree$edge.length[el < 1e-08] <- 1e-08 oldtree = tree k = length(w) loglik = pml.quartet(tree, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, nTips=nTips, weight=weight, nr=nr, nc=nc, contrast=contrast, nco=nco) start.ll <- old.ll <- new.ll <- loglik # contrast <- attr(data, "contrast") contrast2 <- contrast %*% eig[[2]] evi = (t(eig[[3]]) * bf) # weight <- attr(data, "weight") eps = 1 iter = 0 # treeP = tree child = tree$edge[, 2] parent = tree$edge[, 1] m <- max(tree$edge) EL = tree$edge.length n = length(tree$edge.length) ind.inv <- which(ll.0>0) # nr = as.integer(length(weight)) # nc = as.integer(length(bf)) # nco = as.integer(nrow(contrast)) eve = eig[[2]] lg = k rootNode = getRoot(tree) # raus ScaleEPS = 1.0/4294967296.0 # anc = Ancestors(tree, 1:m, "parent") # anc0 = as.integer(c(0L, anc)) while (eps > control$eps && iter < control$maxit) { EL <- .Call("optQrtt", as.integer(parent), as.integer(child), eig, evi, EL, w, g, as.integer(nr), as.integer(nc), as.integer(nTips), as.double(contrast), as.double(contrast2), nco, data, as.double(weight), as.double(ll.0)) iter = iter + 1 # tree$edge.length = EL[tree$edge[,2]] tree$edge.length = EL # [treeP$edge[,2]] # vormals treeP newll <- pml.quartet(tree, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, nTips=nTips, weight=weight, nr=nr, nc=nc, contrast=contrast, nco=nco) # vormals treeP eps = ( old.ll - newll ) / newll if( (eps<0) || (newll < llcomp) ) return(list(tree=oldtree, logLik=old.ll, c(eps, iter))) oldtree = tree # vormals treeP if(control$trace>1) cat(old.ll, " -> ", newll, "\n") old.ll = newll # loli = parent[1] } if(control$trace>0) cat(start.ll, " -> ", newll, "\n") list(tree=tree, logLik=newll, c(eps, iter)) # vormals treeP } # weight, nr, nc, contrast, nco rein # inv, INV, site, ... raus # wMix, rate last pml.quartet <- function (tree, data, bf = rep(.25, 4), k = 1, rate = 1, g, w, eig, ll.0 = NULL, ind.ll0=NULL, llMix = NULL, wMix = 0, nTips, weight, nr, nc, contrast, nco, ..., site=FALSE) { # k <- as.integer(k) # m = 1 # if(any(g<.gEps)){ # for(i in 1:length(g)){ # if(g[i]<.gEps){ # inv <- inv+w[i] # } # } # w <- w[g>.gEps] # g <- g[g>.gEps] # k <- length(w) # } # if (is.null(INV)) # INV <- Matrix(lli(data, tree), sparse=TRUE) # in C if (is.null(ll.0)){ ll.0 <- numeric(nr) } if(is.null(ind.ll0)){ ind = which(ll.0>0) } else ind = ind.ll0 # if(inv>0) # ll.0 <- as.matrix(INV %*% (bf * inv)) # if (wMix > 0) # ll.0 <- ll.0 + llMix # node <- tree$edge[, 1] # edge <- tree$edge[, 2] # root <- as.integer(node[length(node)]) # el <- as.double(tree$edge.length) node = as.integer(tree$edge[, 1] - nTips - 1L) # min(node)) edge = as.integer(tree$edge[, 2] - 1L) # contrast = attr(data, "contrast") # nco = as.integer(dim(contrast)[1]) # dlist=data, nr, nc, weight, k ausserhalb definieren # pmlPart einbeziehen siteLik <- .Call("PML4", dlist=data, as.double(tree$edge.length), as.double(w), as.double(g), nr, nc, as.integer(k), eig, as.double(bf), node, edge, nTips, nco, contrast, N=as.integer(length(edge))) # in C if(!is.null(ll.0)){ # ind = which(ll.0>0) siteLik[ind] = log(exp(siteLik[ind]) + ll.0[ind]) } if(wMix >0) siteLik <- log(exp(siteLik) * (1-wMix) + llMix ) loglik <- sum(weight * siteLik) return(loglik) } index2edge <- function(x, root){ ch = c(1L,2L,5L,4L,3L) elind = c(1L,2L,5L,4L,6L) if(x[6L]==root) el = x[ch] else el = x[elind] el } pml.nni <- function (tree, data, w, g, eig, bf, ll.0, ll, INV=INV, ...) { k = length(w) INDEX <- indexNNI3(tree) tmpl <- pml.fit4(tree, data, bf=bf, g=g, w=w, eig=eig, INV=INV, ll.0=ll.0, k=k, ...) # parent = tree$edge[,1] # child = tree$edge[,2] nr <- as.integer(attr(data, "nr")) nc <- as.integer(attr(data, "nc")) weight = as.numeric(attr(data, "weight")) contrast <- attr(data, "contrast") nco = as.integer(dim(contrast)[1]) contrast2 <- contrast %*% eig[[2]] evi = (t(eig[[3]]) * bf) nTips = as.integer(length(tree$tip.label)) # EL <- numeric(max(parent)) # EL[child] <- tree$edge.length m <- dim(INDEX)[1] loglik = numeric(2*m) edgeMatrix <- matrix(0, 2*m, 5) anc <- Ancestors(tree, 1:max(tree$edge), "parent") loli <- getRoot(tree) ind1 <- c(1,4,3,2,5) # ind2 <- c(4,2,3,1,5) # for(i in 1:m){ ei = INDEX[i,] # el0 = evector[INDEX[i,]] tree0 = index2tree(INDEX[i, ], tree, nTips+1L) ch = ei[5] pa = ei[6] # move up while(pa != loli){ tmpr = match(loli, INDEX[,5]) treetmp = index2tree(INDEX[tmpr, ], tree, nTips+1L) tmpl <- pml.quartet(treetmp, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, nTips=nTips, weight=weight, nr=nr, nc=nc, contrast=contrast, nco=nco) loli = anc[loli] } llt0 <- pml.quartet(tree0, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, nTips=nTips, weight=weight, nr=nr, nc=nc, contrast=contrast, nco=nco) # new0 <- optimQuartet(tree0, data, eig=eig, w=w, g=g, bf=bf, rate=rate, ll.0=ll.0, nTips=nTips, # weight=weight, nr=nr, nc=nc, contrast=contrast, nco=nco, # control = list(epsilon = 1e-08, maxit = 3, trace=0)) tree2 <- tree1 <- tree0 tree1$edge[,2] <- tree1$edge[ind1,2] tree1$edge.length <- tree1$edge.length[ind1] tree2$edge[,2] <- tree2$edge[ind2,2] tree2$edge.length <- tree2$edge.length[ind2] new1 <- optimQuartet(tree1, data, eig=eig, w=w, g=g, bf=bf, ll.0=ll.0, nTips=nTips, weight=weight, nr=nr, nc=nc, contrast=contrast, nco=nco, llcomp = ll+1e-8, ...) # new0$logLik+1e-8) new2 <- optimQuartet(tree2, data, eig=eig, w=w, g=g, bf=bf, ll.0=ll.0, nTips=nTips, weight=weight, nr=nr, nc=nc, contrast=contrast, nco=nco, llcomp = ll+1e-8, ...) #new0$logLik+1e-8) loglik[(2*i)-1]=new1$logLik loglik[(2*i)]=new2$logLik edgeMatrix[(2*i)-1,]=new1$tree$edge.length edgeMatrix[(2*i),]=new2$tree$edge.length # godown or recompute if(any (INDEX[i, c(1,2)]>nTips )) { tree00 <- index2tree2(INDEX[i, ], tree, nTips+1L) tmp3 <- pml.quartet(tree00, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, nTips=nTips, weight=weight, nr=nr, nc=nc, contrast=contrast, nco=nco) loli <- getRoot(tree00) } else tmp3 <- pml.quartet(tree0, data, bf=bf, g=g, w=w, eig=eig, ll.0=ll.0, k=k, nTips=nTips, weight=weight, nr=nr, nc=nc, contrast=contrast, nco=nco) } swap <- 0 eps0 <- 1e-6 candidates <- loglik > ll + eps0 # cat("candidates", sum(candidates), "\n") INDEX2 <- t(apply(INDEX, 1, index2edge, root=getRoot(tree))) while(any(candidates)){ ind = which.max(loglik) loglik[ind]=-Inf if( ind %% 2 ) swap.edge = c(2,4) else swap.edge = c(1,4) IND <- index2edge(INDEX[(ind+1) %/% 2,],nTips+1L) treeT <- changeEdge(tree, IND[swap.edge], IND, edgeMatrix[ind,]) test <- pml.fit4(treeT, data, bf = bf, k=k, g=g, w=w, eig=eig, ll.0=ll.0, INV=INV, ...) if(test <= ll + eps0) candidates[ind] = FALSE if(test > ll + eps0) { ll = test swap=swap+1 tree <- treeT indi <- which(rep(colSums(apply(INDEX,1,match,INDEX[(ind+1)%/%2,],nomatch=0))>0,each=2)) candidates[indi] <- FALSE loglik[indi] <- -Inf } } # trees <- vector("list", 2*m) # for(i in 1:length(loglik)){ # ind = i # if( ind %% 2 ) swap.edge = c(2,4) # else swap.edge = c(1,4) # IND = index2edge(INDEX[(ind+1)%/%2,]) # tree2 <- try( changeEdge(tree, IND[swap.edge], IND, edgeMatrix[ind,]) ) # trees[[i]] <- tree2 # } # class(trees) <- "multiPhylo" # trees <- .compressTipLabel(trees) # , all=loglik list(tree=tree, loglik=ll, swap=swap, candidates = candidates) } phangorn/R/RcppExports.R0000644000175100001440000000052313012623216014715 0ustar hornikusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 allDescCPP <- function(orig, nTips) { .Call('phangorn_allDescCPP', PACKAGE = 'phangorn', orig, nTips) } allChildrenCPP <- function(orig) { .Call('phangorn_allChildrenCPP', PACKAGE = 'phangorn', orig) } phangorn/R/neighborNet.R0000644000175100001440000001212012761653173014703 0ustar hornikusers# computes all n(n-1)/2 cyclic splits cyclicSplits <- function(k, labels=NULL){ k = as.integer(k) l = (k-1L) %/% 2L res <- vector("list", k*(k-1L)/2) res[1:k] = 1L:k ind = k if(k>3){ fun = function(x,y){ tmp = (1L:y)+x tmp %% (k+1L) + tmp %/% (k+1L) } if(k>4){ for(i in 2:l){ res[(ind+1):(ind+k)] <- lapply(0L:(k-1L), fun, i) ind <- ind+k } } if((k%%2L)==0){ m <- k%/%2 res[(ind+1):(ind+m)] <- lapply(0L:(m-1L), fun, m) } } if(is.null(labels)) labels=(as.character(1:k)) attr(res, 'labels') =labels attr(res, "cycle") = 1:k class(res)="splits" res } distC <- function(d, CL){ l=length(CL) res = matrix(0, l, l) for(i in 1:(l-1)){ for(j in (i+1):l) res[i,j] = mean.default(d[CL[[i]], CL[[j]]]) } res + t(res) } reduc <- function(d, x, y, z){ u <- 2/3* d[x, ] + d[y,]/3 v <- 2/3* d[z, ] + d[y,]/3 uv <- (d[x,y] + d[x,z] + d[y,z])/3 d[x, ] <- u d[, x] <- u d[z, ] <- v d[, z] <- v d[y, ] <- 0 d[, y] <- 0 d[x, z] <- d[z, x] <- uv diag(d) <- 0 d } # computes ordering getOrderingNN <- function (x) { x = as.matrix(x) labels <- attr(x, "Labels") if (is.null(labels)) labels = colnames(x) d = x #as.matrix(x) l = dim(d)[1] CL = vector("list", l) CL[1:l] <- ORD <- 1:l lCL <- length(CL) ord <- CL while (lCL>1){ i = 0 j = 0 # browser() DM = distC(d, CL) l = nrow(DM) if(l>2){ r = rowSums(DM)/(l - 2) tmp <- .C("out", as.double(DM), as.double(r), as.integer(l), as.integer(i), as.integer(j), PACKAGE = "phangorn") e1 = tmp[[4]] e2 = tmp[[5]] } else {e1 = 1 e2=2} n1 <- length(CL[[e1]]) n2 <- length(CL[[e2]]) if(n1==1 & n2==1){ newCL <- c(CL[[e1]], CL[[e2]]) newOrd = newCL CL = c(CL[-c(e1,e2)], list(newCL)) ord <- c(ord[-c(e1,e2)], list(newCL)) lCL <- lCL - 1L } else{ CLtmp = c(as.list(CL[[e1]]), as.list(CL[[e2]]), CL[-c(e1,e2)]) ltmp =length(CLtmp) DM2 = distC(d, CLtmp) if(ltmp>2) rtmp = rowSums(DM2)/(ltmp - 2) DM2 = DM2 - outer(rtmp, rtmp, "+") TMP = DM2[1:n1, (n1+1):(n1+n2)] #browser() # dtmp = d[CL[[e1]], CL[[e2]]] # rtmp = numeric(n1+n2) # for(ii in 1:(n1+n2)){ # for(jj in 1:ltmp){if(ii!=jj) rtmp[ii]=rtmp[ii] + mean.default(d[CLtmp[[ii]], CLtmp[[jj]]]) # } # } #browser() # rtmp = rtmp/(ltmp-2) # TMP2 = dtmp + rep(rtmp[1:n1],n2) + rep(rtmp[(n1+1):(n1+n2)], each=n1) #browser() blub = which.min(TMP) # print(blub) #print("blub") if(n1==2 & n2==1){ if(blub == 2){ newCL <- c(CL[[e1]][1], CL[[e2]]) newOrd <- c(CL[[e1]], ord[[e2]]) d <- reduc(d, CL[[e1]][1], CL[[e1]][2], CL[[e2]]) } else{ newCL <- c(CL[[e2]], CL[[e1]][2]) newOrd <- c(ord[[e2]], ord[[e1]]) d <- reduc(d, CL[[e2]], CL[[e1]][1], CL[[e1]][2]) } } if(n1==1 & n2==2){ if(blub==1){ newCL <- c(CL[[e1]], CL[[e2]][2]) newOrd <- c(CL[[e1]], ord[[e2]]) d <- reduc(d, CL[[e1]], CL[[e2]][1], CL[[e2]][2]) } else{ newCL <- c(CL[[e2]][1], CL[[e1]]) newOrd <- c(ord[[e2]], ord[[e1]]) d <- reduc(d, CL[[e2]][1], CL[[e2]][2], CL[[e1]]) } } if(n1==2 & n2==2){ if(blub==1){ newCL <- c(CL[[e1]][2], CL[[e2]][2]) newOrd <- c(rev(ord[[e1]]), ord[[e2]]) d <- reduc(d, CL[[e1]][2], CL[[e1]][1], CL[[e2]][1]) d <- reduc(d, CL[[e1]][2], CL[[e2]][1], CL[[e2]][2]) } if(blub==2){ newCL <- c(CL[[e1]][1], CL[[e2]][2]) newOrd <- c(ord[[e1]], ord[[e2]]) d <- reduc(d, CL[[e1]][1], CL[[e1]][2], CL[[e2]][1]) d <- reduc(d, CL[[e1]][1], CL[[e2]][1], CL[[e2]][2]) } if(blub==3){ newCL <- c(CL[[e1]][2], CL[[e2]][1]) newOrd <- c(rev(ord[[e1]]), rev(ord[[e2]])) d <- reduc(d, CL[[e1]][2], CL[[e1]][1], CL[[e2]][2]) d <- reduc(d, CL[[e1]][2], CL[[e2]][2], CL[[e2]][1]) } if(blub==4){ newCL <- c(CL[[e1]][1], CL[[e2]][1]) newOrd <- c(ord[[e1]], rev(ord[[e2]])) d <- reduc(d, CL[[e1]][1], CL[[e1]][2], CL[[e2]][2]) d <- reduc(d, CL[[e1]][1], CL[[e2]][2], CL[[e2]][1]) } } CL <- c(CL[-c(e1,e2)], list(newCL)) ord <- c(ord[-c(e1,e2)], list(newOrd)) lCL <- lCL - 1L } } newOrd } # neighborNet <- function(x, ord=NULL){ x = as.matrix(x) labels <- attr(x, "Labels")[[1]] if (is.null(labels)) labels = colnames(x) l <- length(labels) #browser() if(is.null(ord))ord <- getOrderingNN(x) spl <- cyclicSplits(l, labels[ord]) spl <- nnls.splits(spl, x) # nnls.split mit nnls statt quadprog attr(spl, "cycle") <- 1:l as.networx(spl) } phangorn/R/Densi.R0000644000175100001440000000430013005450715013467 0ustar hornikusersgetAges <- function(x){ fun=function(x) max(node.depth.edgelength(x)) height=NULL if(inherits(x,"phylo")) height <- fun(x) if(inherits(x,"multiPhylo")){ if(!is.null(attr(x, "TipLabel"))){ x = unclass(x) x = .uncompressTipLabel(x) x = unclass(x) height = sapply(x, fun) } else{ x = unclass(x) height = sapply(x, fun) } } height } densiTree <- function(x, type="cladogram", alpha=1/length(x), consensus=NULL, optim=FALSE, scaleX=FALSE, col=1, width=1, cex=.8, ...) { if(!inherits(x,"multiPhylo"))stop("x must be of class multiPhylo") compressed <- ifelse(is.null(attr(x, "TipLabel")), FALSE, TRUE) if(is.null(consensus))consensus <- superTree(x) if(inherits(consensus,"multiPhylo")) consensus = consensus[[1]] type <- match.arg(type, c("phylogram", "cladogram")) consensus = reorder(consensus, "postorder") e2 = reorder(consensus)$edge[,2] nTip = as.integer(length(consensus$tip.label)) tiporder = e2[e2<=nTip] maxBT = max(getAges(x)) if(scaleX) maxBT=1.0 label = rev(pretty(c(maxBT,0))) maxBT = max(label) xy = plotPhyloCoor(consensus, ...) yy = xy[,2] plot.new() tl = which.max(nchar(consensus$tip.label)) sw <- strwidth(consensus$tip.label[tl],cex=cex) * 1.1 plot.window(xlim=c(0, 1.0+sw), ylim=c(0, nTip+1)) axis(side=1,at=seq(0,1.0, length.out=length(label)), labels=label) text(x=rep(1.0,Ntip(consensus)),y=yy[1:nTip],labels=consensus$tip.label,pos=4,cex=cex) tip.order = yy[1:nTip] for (treeindex in 1:length(x)) { tmp <- reorder(x[[treeindex]], "postorder") if(!compressed) tip.order <- match(tmp$tip.label, consensus$tip.label) xy <- plotPhyloCoor(tmp, tip.order=tiporder, ...) xx = xy[,1] yy = xy[,2] if(scaleX) xx <- xx/max(xx) else xx <- xx/maxBT xx <- xx + (1.0 - max(xx)) e1=tmp$edge[,1] e2=tmp$edge[,2] if(type=="cladogram") cladogram.plot(tmp$edge, xx, yy, edge.color=adjustcolor(col, alpha.f=alpha), edge.width=width, edge.lty=1) if(type=="phylogram"){ Ntip <- min(e1)-1L Nnode <- tmp$Nnode phylogram.plot(tmp$edge, Ntip, Nnode, xx, yy, TRUE, edge.color=adjustcolor(col, alpha.f=alpha), edge.width=width, 1) } } } phangorn/R/networx.R0000644000175100001440000016472513014640521014151 0ustar hornikusers# # splits format, networx, Matrix, lento plot # as.splits <- function (x, ...){ if(inherits(x, "splits")) return(x) UseMethod("as.splits") } as.Matrix <- function (x, ...){ if (inherits(x,"Matrix")) return(x) UseMethod("as.Matrix") } as.matrix.splits <- function(x, zero.print = 0L, one.print=1L, ...){ m = length(x) labels = attr(x, "labels") n = length(labels) res = matrix(zero.print, m, n) for(i in 1:m)res[i,x[[i]]]=one.print dimnames(res) = list(names(x), labels) res } as.Matrix.splits <- function(x, ...){ labels = attr(x, "labels") l = length(x) j = unlist(x) # i = rep(1:l, sapply(x, length)) i = rep(1:l, lengths(x)) sparseMatrix(i,j, x = rep(1L, length(i)), dimnames = list(NULL, labels)) # included x und labels } print.splits <- function (x, maxp = getOption("max.print"), zero.print = ".", one.print="|", ...) { x.orig <- x cx <- as.matrix(x, zero.print = zero.print, one.print=one.print) print(cx, quote = FALSE, right = TRUE, max = maxp) invisible(x.orig) } "[.splits" = function(x, i){ tmp = attributes(x) result = unclass(x)[i] if(!is.null(tmp$weights)) tmp$weights = tmp$weights[i] if(!is.null(tmp$confidences)) tmp$confidences = tmp$confidences[i] if(!is.null(tmp$intervals)) tmp$intervals = tmp$intervals[i] if(!is.null(tmp$data)) tmp$data = tmp$data[i,, drop=FALSE] attributes(result) = tmp result } changeOrder <- function(x, labels){ oldL <- attr(x, "labels") ind <- match(oldL,labels) for(i in 1:length(x)) x[[i]] <- sort(ind[x[[i]]]) if(!is.null(attr(x, "cycle"))) attr(x, "cycle") <- ind[attr(x, "cycle")] attr(x, "labels") <- labels x } #orderSplitLabel = function(x, order){ # label = attr(x, "labels") # nTips = length(label) # ord = match(label, order) # for(i in 1:length(x)) # x[[i]] = sort(ord[x[[i]]]) # attr(x, "labels") = order # x #} # returns order of x$edge presenceAbsenceOld <- function(x, y){ X <- as.splits(x) Y <- as.splits(y) labels <- attr(X, "labels") # if(inherits(x,"phylo")) X <- X[x$edge[,2]] # if(inherits(y,"phylo")) Y <- Y[y$edge[,2]] Y <- changeOrder(Y, labels) # orderSplitLabel nTips <- length(labels) X <- oneWise(X, nTips) Y <- oneWise(Y, nTips) res <- match(X, Y) res <- !is.na(res) if(inherits(x, "networx")){ res <- res[x$splitIndex] } if(class(x)[1]=="phylo"){ # res <- res[x$edge[,2]] x$node.label = res[-c(1:length(labels))] return(x) } res } presenceAbsence <- function(x,y){ spl <- as.splits(y) l <- length(spl) attr(spl, "confidences") <- rep(1, l) addConfidences(x, y) } matchSplits <- function(x, y, as.in=TRUE){ tiplabel <- attr(x, "label") if(any(is.na(match(tiplabel, attr(y, "label"))))) stop("x and y have different labels!") nTips <- length(tiplabel) y <- changeOrder(y, tiplabel) y <- SHORTwise(y, nTips) if(as.in) return(match(SHORTwise(x, nTips), y, nomatch = 0L) > 0L) match(SHORTwise(x, nTips), y) } optCycle <- function(splits, tree){ tips = tree$tip.label tree = reorder(tree) nodes = sort(unique(tree$edge[,1])) M = as.matrix(splits) l = as.integer(nrow(M)) m = as.integer(ncol(M)) tmp = tree$edge[,2] tmp = tmp[tmp<=m] start <- .C("countCycle", M[, tmp], l, m, integer(1))[[4]] best = start eps = 1 if(eps>0){ for(i in 1:length(nodes)){ tmptree = rotate(tree, nodes[i]) tmp = tmptree$edge[,2] tmp = tmp[tmp<=m] tmpC <- .C("countCycle", M[, tmp], l, m, integer(1))[[4]] if(tmpC < best){ best <- tmpC tree = tmptree } } eps = start - best } tree # list(best, tree) } countCycles <- function(splits, tree=NULL, ord=NULL){ M = as.matrix(splits) l = as.integer(nrow(M)) m = as.integer(ncol(M)) if(!is.null(tree)) ord = getOrdering(tree) res <- .C("countCycle2", M[, ord], l, m, integer(l))[[4]] res } c.splits <- function (..., recursive=FALSE) { x <- list(...) if (length(x) == 1 && !inherits(x[[1]], "splits")) x <- x[[1]] n <- length(x) match.names <- function(a, b) { if (any(!(a %in% b))) stop("names do not match previous names") } if (n == 1) return(x[[1]]) labels <- attr(x[[1]], "labels") cycle <- attr(x[[1]], "cycle") for (i in 2:n) { match.names(labels, attr(x[[i]], "labels")) x[[i]] <- changeOrder(x[[i]], labels) } w <- as.vector(sapply(x, attr, "weights")) x <- lapply(x, unclass) res <- structure(do.call("c", x), class=c("splits", "prop.part")) names(res) <- NULL # res <- structure(NextMethod("c"), class=c("splits", "prop.part")) attr(res, "labels") <- labels attr(res, "weights") <- w attr(res, "cycle") <- cycle res } distinct.splits <- function(...){ tmp <- c(...) res <- unique(tmp) attributes(res) <- attributes(tmp) attr(res, "weights") <- tabulate(match(tmp, res)) res } ## as.splits.phylo # computes splits from phylo as.splits.phylo <- function(x, ...){ result <- bip(x) if(!is.null(x$edge.length)){ edge.weights <- numeric(max(x$edge)) edge.weights[x$edge[,2]] <- x$edge.length attr(result, "weights") <- edge.weights } if(!is.null(x$node.label)){ conf <- x$node.label if(is.character(conf)) conf <- as.numeric(conf) if(max(na.omit(conf)) > (1 + 1e-8))conf <- conf / 100 #if(!is.null(scale)) conf <- conf / scale attr(result, "confidences") <- c(rep(1, length(x$tip.label)), conf) # attr(result, "confidences") <- c(rep("", length(x$tip.label)), x$node.label) } attr(result, "labels") <- x$tip.label class(result) <- c('splits', 'prop.part') result } # computes splits from multiPhylo object (e.g. bootstrap, MCMC etc.) # unrooted trees as.splits.multiPhylo <- function(x, ...){ # if(inherits(x,"multiPhylo"))x = .uncompressTipLabel(x) # if(inherits(x,"multiPhylo"))class(x)='list' # prop.part allows not yet multiPhylo # firstTip = x[[1]]$tip[1] # x = lapply(x, root, firstTip) # old trick lx <- length(x) x <- unroot(x) # lapply(x, unroot) # class(x) <- "multiPhylo" splits <- prop.part(x) class(splits)='list' weights = attr(splits, 'number') lab = attr(splits,'labels') attr(splits,'labels') <- attr(splits, 'number') <- NULL l = length(lab) splitTips = vector('list', l) for(i in 1:l) splitTips[[i]] = i result = c(splitTips,splits) attr(result, "weights") = c(rep(lx, l), weights) attr(result, "confidences") <- attr(result, "weights") / lx attr(result, "summary") <- list(confidences="ratio", ntrees=l, clades=FALSE) attr(result, "labels") <- lab class(result) = c('splits', 'prop.part') result } as.splits.prop.part <- function(x, ...){ if(is.null(attr(x, "number"))) attr(x, "weights") = rep(1, length(x)) else{ attr(x, "weights") = attr(x, "number") attr(x, "confidences") = attr(x, "number") / attr(x, "number")[1] } class(x) = c('splits', 'prop.part') x } as.splits.networx <- function(x, ...){ # if(!is.null(attr(x, "splits")))attr(x, "splits") if(!is.null(x$splits)) x$splits else warning("No split object included!") } as.prop.part.splits <- function(x, ...){ attr(x, "number") = attr(x, "weights") attr(x, "weights") = NULL class(x) = c('prop.part') x } ## as.splits.phylo as.phylo.splits <- function (x, result = "phylo", ...) { result <- match.arg(result, c("phylo", "all")) labels = attr(x, "labels") nTips = length(labels) weights = attr(x, "weights") nTips = length(labels) x = SHORTwise(x, nTips) dm = as.matrix(compatible(x)) rs = rowSums(dm) ind = which(rs == 0) if (any(rs > 0)) { tmp = which(rs > 0) candidates = tmp[order(rs[tmp])] for (i in candidates) { if (sum(dm[ind, i]) == 0) ind = c(ind, i) } } splits = x[ind] weights = weights[ind] l = length(ind) res = matrix(0L, l, nTips) for (i in 1:l) res[i, splits[[i]]] = 1L dm2 = (crossprod(res * weights, 1 - res)) dm2 = dm2 + t(dm2) dimnames(dm2) = list(labels, labels) tree <- di2multi(NJ(dm2), tol = 1e-08) attr(tree, "order") = NULL tree <- reorder(tree) tree <- optCycle(x, tree) tree <- reorder(tree, "postorder") if (result == "phylo") return(tree) # tree = reroot(tree, Ancestors(tree, 1, "parent")) spl = as.splits(tree) spl = SHORTwise(spl, nTips) spl <- spl[tree$edge[,2]] list(tree = tree, index = tree$edge[, 2], split = spl, rest = x[-ind]) } # computes compatible splits compatible <- function(obj){ labels = attr(obj, "labels") if(!inherits(obj, "splits"))stop("obj needs to be of class splits") l = length(labels) n = length(obj) bp = matrix(0L, n, l) for(i in 1:n)bp[i,obj[[i]]] = 1L bp[bp[, 1] == 0L, ] = 1L - bp[bp[, 1] == 0L, ] k=1 res = matrix(0L, n, n) tmp1 = tcrossprod(bp) #sum(bp[i,]* bp[j,]) tmp2 = tcrossprod(1L - bp) #sum((1L - bp[i,])*(1L - bp[j,])) tmp3 = tcrossprod(bp, 1L - bp) #sum(bp[i,]*(1L - bp[j,])) tmp4 = tcrossprod(1L - bp, bp) #sum((1L - bp[i,])*bp[j,]) res[(tmp1 * tmp2 * tmp3 * tmp4)>0]=1L k = k+1 res = res[lower.tri(res)] attr(res, "Size") <- n attr(res, "Diag") <- FALSE attr(res, "Upper") <- FALSE class(res) <- "dist" return(res) } compatible2 <- function (obj1, obj2=NULL) { if (!inherits(obj1, "splits")) stop("obj needs to be of class splits") labels = attr(obj1, "labels") l = length(labels) n = length(obj1) bp1 = as.matrix(obj1) bp1[bp1[, 1] == 0L, ] = 1L - bp1[bp1[, 1] == 0L, ] if(!is.null(obj2)){ m = length(obj2) bp2 = as.matrix(obj2) labels2 = attr(obj2, "labels") bp2 = bp2[, match(labels2, labels), drop=FALSE] bp2[bp2[, 1] == 0L, ] = 1L - bp2[bp2[, 1] == 0L, ] } else bp2 = bp1 if(is.null(obj2)) res = matrix(0L, n, n) else res = matrix(0L, n, m) tmp1 = tcrossprod(bp1, bp2) tmp2 = tcrossprod(1L - bp1, 1L - bp2) tmp3 = tcrossprod(bp1, 1L - bp2) tmp4 = tcrossprod(1L - bp1, bp2) res[(tmp1 * tmp2 * tmp3 * tmp4) > 0] = 1L if(is.null(obj2)){ res = res[lower.tri(res)] attr(res, "Size") <- n attr(res, "Diag") <- FALSE attr(res, "Upper") <- FALSE class(res) <- "dist" } return(res) } compatible3 <- function(x, y=NULL) { if (!inherits(x, "splits")) stop("x needs to be of class splits") if(is.null(y)) y <- x if (!inherits(y, "splits")) stop("y needs to be of class splits") xlabels = attr(x, "labels") ylabels = attr(y, "labels") if(identical(xlabels, ylabels)) labels = xlabels else labels = intersect(xlabels, ylabels) nx = length(x) ny = length(y) bp1 = as.matrix(x)[,labels, drop=FALSE] bp2 = as.matrix(y)[,labels, drop=FALSE] rs1 = rowSums(bp1) rs2 = rowSums(bp2) res = matrix(0L, nx, ny) tmp1 = tcrossprod(bp1, bp2) res = matrix(0L, nx, ny) for(i in 1:nx){ for(j in 1:ny){ if(tmp1[i, j]==rs1[i]) res[i,j] = 1 if(tmp1[i, j]==rs2[j]) res[i,j] = 2 if(tmp1[i, j]==rs1[i] & tmp1[i, j]==rs2[j])res[i,j] = 3 } } if(is.null(y)){ res = res[lower.tri(res)] attr(res, "Size") <- length(x) attr(res, "Diag") <- FALSE attr(res, "Upper") <- FALSE class(res) <- "dist" } return(res) } # # splits # splitsNetwork <- function(dm, splits=NULL, gamma=.1, lambda=1e-6, weight=NULL){ dm = as.matrix(dm) k = dim(dm)[1] if(!is.null(splits)){ # tmp = which(sapply(splits, length)==k) tmp = which(lengths(splits)==k) splits = splits[-tmp] lab = attr(splits, "labels") dm = dm[lab, lab] } if(is.null(splits)){ X2 = designAll(k, TRUE) X=X2[[1]] } else X = as.matrix(splits2design(splits)) y = dm[lower.tri(dm)] if(is.null(splits))ind = c(2^(0:(k-2)),2^(k-1)-1) else ind = which(lengths(splits)==1) # else ind = which(sapply(splits, length)==1) # y2 = lm(y~X[,ind]-1)$res n = dim(X)[2] ridge <- lambda * diag(n) ridge[ind,ind] <- 0 if(!is.null(weight)) Dmat <- crossprod(X * sqrt(weight)) + ridge else Dmat <- crossprod(X) + ridge if(!is.null(weight)) dvec <- crossprod(X * sqrt(weight),y * sqrt(weight)) else dvec <- crossprod(X, y) # Dmat <- as.matrix(Dmat) # dvec <- as.vector(dvec) ind1 <- rep(1,n) ind1[ind] <- 0 Amat <- cbind(ind1,diag(n)) bvec <- c(gamma, rep(0,n)) # needs quadprog::solve.QP.compact solution <- quadprog::solve.QP(Dmat,dvec,Amat,bvec=bvec, meq=1)$sol ind2 <- which(solution>1e-8) n2 <- length(ind2) ind3 = which(duplicated(c(ind2, ind), fromLast = TRUE)[1:n2]) ridge2 <- lambda * diag(n2) ridge2[ind3,ind3] <- 0 if(!is.null(weight)) Dmat <- crossprod(X[, ind2] * sqrt(weight)) + ridge2 else Dmat <- crossprod(X[, ind2]) + ridge2 if(!is.null(weight)) dvec <- crossprod(X[, ind2] * sqrt(weight),y * sqrt(weight)) else dvec <- crossprod(X[, ind2], y) Amat2 <- diag(n2) bvec2 <- rep(0, n2) # needs quadprog::solve.QP.compact # bvec2 not used solution2 <- quadprog::solve.QP(Dmat, dvec, Amat2)$sol RSS1 = sum((y-X[,ind2]%*%solution[ind2])^2) RSS2 = sum((y-X[,ind2]%*%solution2)^2) if(is.null(splits)){ splits = vector("list", length(ind2)) for(i in 1:length(ind2))splits[[i]] = which(X2[[2]][ind2[i],]==1) } else splits = splits[ind2] attr(splits, "weights") = solution[ind2] attr(splits, "unrestricted") = solution2 attr(splits, "stats") = c(df=n2, RSS_p = RSS1, RSS_u=RSS2) attr(splits,"labels") =dimnames(dm)[[1]] class(splits)='splits' return(splits) } allSplits = function(k, labels=NULL){ result <- lapply(1:(2^(k-1)-1),dec2Bin) if(is.null(labels)) labels=(as.character(1:k)) attr(result, 'labels') =labels class(result)='splits' result } allCircularSplits <- function(k, labels=NULL){ k = as.integer(k) l = (k-1L) %/% 2L res <- vector("list", k*(k-1L)/2) res[1:k] = 1L:k ind = k if(k>3){ fun = function(x,y){ tmp = (1L:y)+x tmp %% (k+1L) + tmp %/% (k+1L) } if(k>4L){ for(i in 2:l){ res[(ind+1):(ind+k)] <- lapply(0L:(k-1L), fun, i) ind <- ind+k } } if((k%%2L)==0){ m <- k%/%2 res[(ind+1):(ind+m)] <- lapply(0L:(m-1L), fun, m) } } if(is.null(labels)) labels <- as.character(1:k) attr(res, 'labels') <- labels attr(res, "cycle") <- 1:k class(res)="splits" res } getIndex = function(left, right, n){ if(n1) # & l X[k,j]){ Vstop = ord[j-1] ordStop = j-1 } } fromTo <- ordStart:ordStop if(ordStart>ordStop) fromTo <- c(ordStart:nTips, 1:ordStop) fromTo = ord[fromTo] # print(fromTo) g = graph(t(res$edge), directed=FALSE) isChild = (rsY == (Y %*% X[k,]))[index] sp2 = NULL sp0 = NULL for(i in 2:length(fromTo)){ # sptmp = get.shortest.paths(g, fromTo[i-1], fromTo[i], # output=c("epath"))$epath[[1]] sptmp = shortest_paths(g, fromTo[i-1], fromTo[i], output=c("epath"))$epath[[1]] sp2 = c(sp2, sptmp[-c(1, length(sptmp))]) sp0 = c(sp0, sptmp) } sp0 = unique(sp0) if(length(sp2)>0){ # blub = which(dm[index[sp2], ind[k]]>0) TMP = rowSums(dm2[index[sp2], 1:k, drop=FALSE]) blub = which(TMP>0) sp2 = sp2[blub] } if(length(sp2)==0){ isChild = (rsY == (Y %*% X[k,]))[index] sp0 = which(isChild == TRUE) edge1 = unique(as.vector(res$edge[sp0,])) edge2 = as.vector(res$edge[-sp0,]) asdf = edge1 %in% edge2 sp = edge1[asdf] } if(length(sp2)>0) sp = unique(as.vector(t(res$edge[sp2,]))) parent = res$edge[,1] child = res$edge[,2] j = ord[which(X[k,]==1)] anc = unique(parent[match(j, child)]) maxVert = max(parent) l = length(sp) newVert = (maxVert+1) : (maxVert+l) sp01 = setdiff(sp0, sp2) for(i in 1:l) res$edge[sp01,][res$edge[sp01,]==sp[i]] = newVert[i] newindex = rep(ind[k], l) if(length(sp)>1)newindex = c(index[sp2], newindex) index = c(index, newindex) # connect new and old vertices newEdge = matrix(cbind(sp, newVert), ncol=2) if(length(sp)>1){ # copy edges qwer = match(as.vector(res$edge[sp2,]), sp) newEdge = rbind(matrix(newVert[qwer], ncol=2), newEdge) } res$edge = rbind(res$edge, newEdge) res$Nnode = max(res$edge) - nTips res$splitIndex = index res$edge.length <- rep(1, nrow(res$edge)) class(res) = c("networx", "phylo") attr(res, "order") = NULL } res$edge.length = weight[index] # ausserhalb res$Nnode = max(res$edge) - nTips res$splitIndex = index # attr(res, "splits") = x res$splits = x class(res) = c("networx", "phylo") attr(res, "order") = NULL res } as.networx <- function (x, ...) { if (inherits(x, "networx")) return(x) UseMethod("as.networx") } getOrdering <- function(x){ tree = as.phylo(x) nTips = length(tree$tip.label) ord = reorder(tree)$edge[,2] ord = ord[ord<=nTips] ind = which(ord == 1L) if(ind>1) ord = c(ord[ind:nTips], ord[c(1:(ind-1L))]) ord } ## as.splits.phylo addTrivialSplits <- function(obj){ label <- attr(obj, "label") nTips <- length(label) weight <- attr(obj, "weights") if(is.null(weight)) weight = rep(1, length(obj)) STree = stree(nTips, tip.label = attr(obj, "labels")) STree$edge.length=NULL spRes <- as.splits(STree)[STree$edge[,2]] tmpIndex = match(spRes, SHORTwise(obj, nTips)) if(any(is.na(tmpIndex))){ l.na = sum(is.na(tmpIndex)) obj <- c(obj, spRes[is.na(tmpIndex)]) weight = c(weight, rep(0, l.na)) attr(obj, "weights") <- weight } obj } as.networx.splits <- function(x, planar=FALSE, coord = c("none", "2D", "3D"), ...){ label <- attr(x, "label") x = addTrivialSplits(x) nTips <- length(label) weight <- attr(x, "weights") if(is.null(weight)) weight = rep(1, length(x)) attr(x, "weights") <- weight x <- oneWise(x, nTips) l <- lengths(x) # l <- sapply(x, length) if(any(l==nTips))x <- x[l!=nTips] # get rid of trivial splits ext <- sum(l==1 | l==(nTips-1)) if(!is.null(attr(x, "cycle"))){ c.ord <- attr(x, "cycle") } else c.ord <- getOrdering(x) attr(x, "cycle") = c.ord dm <- as.matrix(compatible2(x)) # which splits are in circular ordering circSplits = which(countCycles(x, ord=c.ord)==2) if(length(circSplits) == length(x)) planar=TRUE tmp = circNetwork(x, c.ord) attr(tmp, "order") = NULL if(planar){ return(reorder(tmp)) } ll <- lengths(x) # ll <- sapply(x, length) ind <- tmp$splitIndex # match(sp, x) ind2 = union(ind, which(ll==0)) # which(duplicated(x)) ind2 = union(ind2, which(ll==nTips)) ord <- order(colSums(dm)) ord <- setdiff(ord, ind2) if(length(ord)>0){ for(i in 1:length(ord)){ tmp = addEdge(tmp, x, ord[i]) tmp$edge.length = weight[tmp$splitIndex] tmp$Nnode = max(tmp$edge) - nTips class(tmp) = c("networx", "phylo") } } tmp$edge.length = weight[tmp$splitIndex] tmp$Nnode = max(tmp$edge) - nTips attr(x, "cycle") <- c.ord # attr(tmp, "splits") = x tmp$splits <- x class(tmp) = c("networx", "phylo") tmp <- reorder(tmp) coord <- match.arg(coord) vert <- switch(coord, "none" = NULL, "2D" = coords(tmp, dim="2D"), "3D" = coords(tmp, dim="3D")) # attr(tmp, "coords") <- coordinates tmp$plot <- list(vertices=vert) tmp } #as.networx.phylo <- function(x, ...){ # spl <- as.splits(x) # as.networx(x, ...) #} ## as.splits.phylo as.networx.phylo <- function(x, ...){ spl <- as.splits(x) spl <- spl[x$tree[,2]] x$splitIndex <- 1:nrow(x$edge) # attr(x, "splits") = spl x$splits <- spl class(x) <- c("networx", "phylo") x } #as.igraph.networx <- function(x, directed=FALSE){ # graph(t(x$edge), directed=directed) #} consensusNet <- function (obj, prob = 0.3, ...) { l = length(obj) spl = as.splits(obj) w = attr(spl, "weights") ind = (w/l) > prob spl = spl[ind] attr(spl, "confidences") = (w/l)[ind] # attr(spl, "weights") = w[ind] res = as.networx(spl) res$edge.labels = as.character(res$edge.length / l * 100) res$edge.labels[res$edge[,2]<=length(res$tip.label)] = "" reorder(res) } createLabel <- function(x, y, label_y, type="edge", nomatch=NA){ spl_x <- as.splits(x) if(inherits(x, "phylo", TRUE)==1) spl_x <- spl_x[x$edge[,2]] spl_y <- as.splits(y) if(inherits(y, "phylo", TRUE)==1) spl_y <- spl_y[y$edge[,2]] tiplabel <- attr(spl_x, "label") nTips <- length(tiplabel) spl_y <- changeOrder(spl_y, tiplabel) spl_y <- SHORTwise(spl_y, nTips) ind <- match(SHORTwise(spl_x, nTips), spl_y) pos <- which(!is.na(ind)) res <- rep(nomatch, length(spl_x)) if(length(label_y)==1L) label_y <- rep(label_y, length(spl_y)) res[pos] <- label_y[ind[pos]] if(type=="edge" && inherits(x, "networx")){ return(res[x$splitIndex]) } res } addConfidences <- function (x, y, ...) UseMethod("addConfidences") # y now more general addConfidences.splits <- function(x, y, ...){ tiplabel <- attr(x, "label") nTips = length(tiplabel) # x = addTrivialSplits(x) if(inherits(y,"phylo")){ ind <- match(tiplabel, y$tip.label) if (any(is.na(ind)) | length(tiplabel) != length(y$tip.label)) stop("trees have different labels") y$tip.label <- y$tip.label[ind] ind2 <- match(1:length(ind), y$edge[, 2]) y$edge[ind2, 2] <- order(ind) } spl <- as.splits(y) spl <- changeOrder(spl, tiplabel) spl <- SHORTwise(spl, nTips) # ind <- match(SHORTwise(x, nTips), spl) # ind ind <- match(SHORTwise(x, nTips), spl) # pos <- which(ind > nTips) pos <- which(!is.na(ind)) confidences <- numeric(length(x)) #character confidences[pos] <- attr(spl, "confidences")[ind[pos]] # y$node.label[ind[pos] - nTips] attr(x, "confidences") <- confidences x } addConfidences.networx <- function(x, y, ...){ spl <- x$splits spl <- addConfidences(spl, y, ...) x$splits <- spl x } ## as.splits.phylo addConfidences.phylo <- function(x, y, ...){ # call <- x$call conf = attr(addConfidences(as.splits(x), y), "confidences") if(is.character(conf)) conf <- as.numeric(conf) nTips = length(x$tip.label) x$node.label = conf[-c(1:nTips)] * 100 x } reorder.networx <- function (x, order = "cladewise", index.only = FALSE, ...) { order <- match.arg(order, c("cladewise", "postorder")) if (!is.null(attr(x, "order"))) if (attr(x, "order") == order) return(x) g <- graph(t(x$edge)) # if(order == "cladewise") neword <- topological.sort(g, "out") # else neword <- topological.sort(g, "in") if(order == "cladewise") neword <- topo_sort(g, "out") else neword <- topo_sort(g, "in") neworder <- order(match(x$edge[,1], neword)) if(index.only) return(neworder) x$edge <- x$edge[neworder, ] if (!is.null(x$edge.length)) x$edge.length <- x$edge.length[neworder] if (!is.null(x$edge.labels)) x$edge.labels <- x$edge.labels[neworder] if (!is.null(x$splitIndex))x$splitIndex <- x$splitIndex[neworder] attr(x, "order") <- order x } coords <- function(obj, dim="3D"){ # if(is.null(attr(obj,"order")) || (attr(obj, "order")=="postorder") ) # obj = reorder.networx(obj) l = length(obj$edge.length) ind1 = which(!duplicated(obj$splitIndex)) n = max(obj$edge) adj = spMatrix(n, n, i = obj$edge[,2], j = obj$edge[,1], x = rep(1, length(obj$edge.length))) # Matrix:: g = graph_from_adjacency_matrix(adj, "undirected") # g = graph.adjacency(adj, "undirected") ########## # add this # g2 <- graph(t(obj$edge), directed=FALSE) # g2 <- set.edge.attribute(g, "weight", value=rep(1, nrow(obj$edge)) if(dim=="3D"){ coord <- layout_nicely(g, dim=3) # coord <- layout_with_kk(g, dim=3) # coord <- layout.kamada.kawai(g, dim=3) k = matrix(0, max(obj$splitIndex), 3) for(i in ind1){ tmp = coord[obj$edge[i, 2],] - coord[obj$edge[i, 1],] k[obj$splitIndex[i], ] = kart2kugel(tmp[1], tmp[2], tmp[3]) } k[obj$splitIndex[ind1],1] = obj$edge.length[ind1] res = matrix(0, vcount(g), 3) for(i in 1:l){# unique(obj$splitIndex) j = obj$edge[i,1] m = obj$edge[i,2] p = obj$splitIndex[i] res[m,] = res[j,] + kugel2kart(k[p,1], k[p,2], k[p,3]) } } else{ coord <- layout_nicely(g, dim=2) # coord <- layout_with_kk(g, dim=2) # coord <- layout.kamada.kawai(g, dim=2) k = matrix(0, max(obj$splitIndex), 2) for(i in ind1){ tmp = coord[obj$edge[i, 2],] - coord[obj$edge[i, 1],] k[obj$splitIndex[i], ] = kart2kreis(tmp[1], tmp[2]) } k[obj$splitIndex[ind1],1] = obj$edge.length[ind1] res = matrix(0, vcount(g), 2) for(i in 1:l){# unique(obj$splitIndex) j = obj$edge[i,1] m = obj$edge[i,2] p = obj$splitIndex[i] res[m,] = res[j,] + kreis2kart(k[p,1], k[p,2]) } } res } kart2kugel <- function(x,y,z){ r = sqrt(x*x+y*y+z*z) alpha = atan(sqrt(x*x+y*y) / z) if(z<0) alpha = alpha+pi beta = atan(y/x) if(x<0) beta = beta+pi c(r,alpha,beta) } kart2kreis <- function(x,y){ r = sqrt(x*x+y*y) alpha = atan(y/x) if(x<0) alpha = alpha+pi c(r,alpha) } kreis2kart <- function(r,alpha){ c(r*cos(alpha), r*sin(alpha)) } kugel2kart <- function(r,alpha,beta){ x = r * sin(alpha) * cos(beta) y = r * sin(alpha) * sin(beta) z = r * cos(alpha) c(x,y,z) } edgeLabels <- function(xx,yy,zz=NULL, edge){ XX <- (xx[edge[, 1]] + xx[edge[, 2]])/2 YY <- (yy[edge[, 1]] + yy[edge[, 2]])/2 if(!is.null(zz)){ ZZ <- (zz[edge[, 1]] + zz[edge[, 2]])/2 return(cbind(XX, YY, ZZ)) } cbind(XX, YY) } plot.networx = function(x, type="3D", use.edge.length = TRUE, show.tip.label=TRUE, show.edge.label=FALSE, edge.label=NULL, show.node.label = FALSE, node.label=NULL, show.nodes=FALSE, tip.color = "black", edge.color="black", edge.width = 3, edge.lty = 1, split.color=NULL, split.width = NULL, split.lty = NULL, font = 3, cex = par("cex"), cex.node.label=cex, cex.edge.label=cex, col.node.label = tip.color, col.edge.label = tip.color, font.node.label = font, font.edge.label = font, ...){ type = match.arg(type, c("3D", "2D")) if(use.edge.length==FALSE) x$edge.length[] = 1 # test # x = reorder(x) nTips = length(x$tip.label) conf = attr(x$splits,"confidences") * 100 index = x$splitIndex if(is.null(edge.label) & !is.null(conf))edge.label = conf[index] if(is.null(node.label))node.label = as.character(1:max(x$edge)) if(show.tip.label)node.label[1:nTips] = "" lspl <- max(x$splitIndex) if(!is.null(split.color)){ if(length(split.color)!=lspl) stop("split.color must be same length as splits") else edge.color <- split.color[x$splitIndex] } if(!is.null(split.width)){ if(length(split.width)!=lspl) stop("split.color must be same length as splits") else edge.width <- split.width[x$splitIndex] } if(!is.null(split.lty)){ if(length(split.lty)!=lspl) stop("split.color must be same length as splits") else edge.lty <- split.lty[x$splitIndex] } chk <- FALSE if(type=="3D") chk <- requireNamespace("rgl", quietly = TRUE) #.check.pkg("rgl") if(!chk && type=="3D"){ warning("type=\"3D\" requires the package \"rgl\"\n, plotting =\"2D\" instead!\n") type="2D" } # use precomputed vertices when available coord <- NULL if(!is.null(x$.plot)) coord <- x$.plot$vertices if(type=="3D") { if(is.null(coord) || ncol(coord)!=3) coord <- coords(x, dim="3D") plotRGL(coord, x, show.tip.label=show.tip.label, show.edge.label=show.edge.label, edge.label = edge.label, show.node.label = show.node.label, node.label=node.label, show.nodes=show.nodes, tip.color = tip.color, edge.color=edge.color, edge.width = edge.width, font = font, cex = cex, cex.node.label=cex.node.label, cex.edge.label=cex.edge.label, col.node.label = col.node.label, col.edge.label = col.edge.label, font.node.label = font.node.label, font.edge.label = font.edge.label) } else{ if(is.null(coord) || ncol(coord)!=2) coord <- coords(x, dim="2D") plot2D(coord, x, show.tip.label=show.tip.label, show.edge.label=show.edge.label, edge.label = edge.label, show.node.label = show.node.label, node.label=node.label, show.nodes=show.nodes, tip.color = tip.color, edge.color=edge.color, edge.width = edge.width, edge.lty=edge.lty,font = font, cex = cex, cex.node.label=cex.node.label, cex.edge.label=cex.edge.label, col.node.label = col.node.label, col.edge.label = col.edge.label, font.node.label = font.node.label, font.edge.label = font.edge.label, add=FALSE) } x$.plot <- list(vertices = coord, edge.color=edge.color, edge.width=edge.width, edge.lty = edge.lty) invisible(x) } plotRGL <- function(coords, net, show.tip.label=TRUE, show.edge.label=FALSE, edge.label=NULL, show.node.label=FALSE, node.label=NULL, show.nodes=FALSE, tip.color = "blue", edge.color="grey", edge.width = 3, font = 3, cex = par("cex"), cex.node.label=cex, cex.edge.label=cex, col.node.label=tip.color, col.edge.label=tip.color, font.node.label=font, font.edge.label=font, ...){ # chk <- .check.pkg("rgl") # if(!chk) open3d <- segments3d <- spheres3d <- rgl.texts <- function(...)NULL open3d <- rgl::open3d segments3d <- rgl::segments3d spheres3d <- rgl::spheres3d rgl.texts <- rgl::rgl.texts edge = net$edge x = coords[,1] y = coords[,2] z = coords[,3] nTips = length(net$tip.label) segments3d(x[t(edge)],y[t(edge)],z[t(edge)], col=rep(edge.color, each=2), lwd=edge.width) radius=0 if(show.nodes){ radius = sqrt((max(x)-min(x))^2 + (max(y)-min(y))^2 + (max(z)-min(z))^2) / 200 spheres3d(x[1:nTips], y[1:nTips],z[1:nTips], radius=2*radius, color="cyan") spheres3d(x[-c(1:nTips)], y[-c(1:nTips)],z[-c(1:nTips)], radius=radius, color="magenta") } if(show.tip.label){ if(is.null(net$translate)) rgl.texts(x[1:nTips]+2.05*radius,y[1:nTips],z[1:nTips],net$tip.label, color=tip.color, cex=cex, font=font) else rgl.texts(x[net$translate$node]+2.05*radius,y[net$translate$node],z[net$translate$node],net$tip.label, color=tip.color, cex=cex, font=font) } if(show.edge.label){ ec = edgeLabels(x, y, z, edge) if(is.null(edge.label)) edge.label = net$splitIndex #else edge.label = net$splitIndex rgl.texts(ec[,1], ec[,2], ec[,3], edge.label, color=col.edge.label, cex=cex.edge.label, font=font.edge.label) } if(show.node.label){ rgl.texts(x, y, z, node.label, color=col.node.label, cex=cex.node.label, font=font.node.label) } } plot2D <- function(coords, net, show.tip.label=TRUE, show.edge.label=FALSE, edge.label=NULL, show.node.label=FALSE, node.label=NULL, tip.color = "blue", edge.color="grey", edge.width = 3, edge.lty=1, font = 3, cex = par("cex"), cex.node.label=cex, cex.edge.label=cex, col.node.label=tip.color, col.edge.label=tip.color, font.node.label=font, font.edge.label=font, add=FALSE, ...){ edge = net$edge label = net$tip.label xx = coords[,1] yy = coords[,2] nTips = length(label) # cex=1 xlim <- range(xx) ylim <- range(yy) if(show.tip.label){ offset <- max(nchar(label)) * 0.018 * cex * diff(xlim) xlim = c(xlim[1]-offset, xlim[2]+offset) ylim = c(ylim[1]-0.03 * cex * diff(ylim), ylim[2]+0.03 * cex * diff(ylim)) } if(!add){ plot.new() plot.window(xlim, ylim, asp=1) } cladogram.plot(edge, xx, yy, edge.color, edge.width, edge.lty) if(show.tip.label){ if(is.null(net$translate)) ind=match(1:nTips, edge[,2]) else ind=match(net$translate$node, edge[,2]) pos = rep(4, nTips) XX <- xx[edge[ind, 1]] - xx[edge[ind, 2]] pos[XX>0] = 2 YY <- yy[edge[ind, 1]] - yy[edge[ind, 2]] pos2 <- rep(3, nTips) pos2[YY>0] = 1 # needed if tiplabels are not at internal nodes XX[is.na(XX)] = 0 YY[is.na(YY)] = 0 pos[abs(YY)>abs(XX)] <- pos2[abs(YY)>abs(XX)] if(is.null(net$translate)) text(xx[1:nTips], yy[1:nTips], labels=label, pos=pos, col=tip.color, cex=cex, font=font) else text(xx[net$translate$node], yy[net$translate$node], labels=label, pos=pos, col=tip.color, cex=cex, font=font) } if(show.edge.label){ ec = edgeLabels(xx,yy, edge=edge) if(is.null(edge.label))edge.label = net$splitIndex # show only one edge label em <- apply(ec, 1, function(x)max(abs(x))) si <- net$splitIndex for(i in unique(si)){ tmp <- si==i if(sum(tmp)>1){ w <- which(tmp) wm <- which.max(em[w]) edge.label[w[-wm]] <- "" } } text(ec[,1], ec[,2], labels=edge.label, col=col.edge.label, cex=cex.edge.label, font=font.edge.label) } if(show.node.label){ text(xx, yy, labels=node.label, col=col.node.label, cex=cex.node.label, font=font.node.label) } } ## as.splits.phylo lento <- function (obj, xlim = NULL, ylim = NULL, main = "Lento plot", sub = NULL, xlab = NULL, ylab = NULL, bipart=TRUE, trivial=FALSE, col = rgb(0,0,0,.5), ...) { if (inherits(obj,"phylo")){ if(inherits(obj,"phylo",TRUE)==1) obj <- as.splits(obj)[obj$edge[,2]] obj <- as.splits(obj) } if (inherits(obj,"multiPhylo")) obj = as.splits(obj) labels = attr(obj, "labels") l = length(labels) if(!trivial){ triv = lengths(obj) # triv = sapply(obj, length) ind = logical(length(obj)) ind[(triv >1) & (triv < (l-1))] = TRUE if(length(col)==length(obj)) col=col[ind] obj = obj[ind] } CM = compatible(obj) support = attr(obj, "weights") if (is.null(support)) support = rep(1, length(obj)) conflict = -as.matrix(CM) %*% support n = length(support) if (is.null(ylim)) { eps = (max(support) - min(conflict)) * 0.05 ylim = c(min(conflict) - eps, max(support) + eps) } if (is.null(xlim)) { xlim = c(0, n + 1) } ord = order(support, decreasing = TRUE) support = support[ord] conflict = conflict[ord] if(length(col)==length(obj)) col=col[ord] plot.new() plot.window(xlim, ylim) title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...) segments(0:(n - 1), support, y1 = conflict, ...) segments(1:n, support, y1 = conflict, ...) segments(0:(n - 1), support, x1 = 1:n, ...) segments(0:(n - 1), conflict, x1 = 1:n, ...) abline(h = 0) axis(2, ...) aty = diff(ylim)/(l+1) at = min(ylim) + (1:l) * aty if(bipart){ Y = rep(at, n) X = rep((1:n)-.5, each=l) Circles = matrix(1, l, n) for(i in 1:n) Circles[obj[[ord[i]]],i] = 19 # axis(4, labels=labels, at=at) col = rep(col, each=l) text(x=n+.1,y=at, labels, pos=4, ...) points(X,Y,pch = as.numeric(Circles), col = col, ...) } invisible(list(support = cbind(support, conflict), splits=obj[ord])) } write.splits = function (x, file = "", zero.print = ".", one.print = "|", print.labels = TRUE, ...) { labels = attr(x, "labels") x.orig <- x cx <- as.matrix(x, zero.print = zero.print, one.print = one.print) w = FALSE if (!is.null(attr(x, "names"))) { nam = TRUE vnames = format(attr(x, "names")) } nam = FALSE if (!is.null(attr(x, "weights"))) { w = TRUE weight = format(attr(x, "weights")) } d = FALSE if (!is.null(attr(x, "data"))) { d = TRUE data = attr(x, "data") } if(print.labels){for(i in 1:length(labels)) cat(labels[i], "\n", file = file, append = TRUE)} if (w) cat("weight", "\t", file = file, append = TRUE) if (d) cat(paste(colnames(data), "\t"), file = file, append = TRUE) cat("\n", file = file, append = TRUE) #"Matrix", for (i in 1:length(x)) { if (nam) cat(vnames[i], "\t", file = file, append = TRUE) if (d) cat(paste(data[i, ], "\t"), file = file, append = TRUE) if (w) cat(weight[i], "\t", file = file) cat("\n", paste(cx[i, ], collapse = ""),"\n", file = file, append = TRUE) } } write.nexus.splits <- function (obj, file = "", weights=NULL, taxa=TRUE, append=FALSE) { taxa.labels <- attr(obj, "labels") ntaxa <- length(taxa.labels) obj <- oneWise(obj, ntaxa) ind <- which(lengths(obj)==ntaxa) # ind <- which(sapply(obj, length)==ntaxa) if(length(ind))obj <- obj[-ind] nsplits <- length(obj) if(is.null(weights))weight <- attr(obj, "weights") if (is.null(weight)) fwei <- FALSE else fwei <- TRUE # if (is.null(weight)) weight = numeric(nsplits) + 100 if(!append){cat("#NEXUS\n\n", file = file) cat("[Splits block for Spectronet or Splitstree]\n", file = file, append = TRUE) cat("[generated by phangorn:\n", file = file, append = TRUE) cat(format(citation("phangorn"), "text"), "]\n\n", file = file, append = TRUE)} # TAXON BLOCK if(taxa){ cat(paste("BEGIN TAXA;\n\tDIMENSIONS ntax=", ntaxa, ";\n", sep = ""), file = file, append = TRUE) cat("\tTAXLABELS", paste(taxa.labels, sep = " "), ";\nEND;\n\n", file = file, append = TRUE) } # SPLITS BLOCK cat(paste("BEGIN SPLITS;\n\tDIMENSIONS ntax=", ntaxa, " nsplits=", nsplits, ";\n", sep = ""), file = file, append = TRUE) format = "\tFORMAT labels=left" # weights=yes if(fwei) format = paste(format, "weights=yes") else format = paste(format, "weights=no") fcon = fint = flab = FALSE if(!is.null(attr(obj, "confidences"))){ format = paste(format, "confidences=yes") fcon=TRUE conf = attr(obj, "confidences") if(storage.mode(conf) == "character"){ conf[conf==""] = "0" attr(obj, "confidences") = conf } } else format = paste(format, "confidences=no") if(!is.null(attr(obj, "intervals"))){ format = paste(format, "intervals=yes") fint=TRUE } else format = paste(format, "intervals=no") if(!is.null(attr(obj, "splitlabels"))) flab=TRUE format = paste(format, ";\n", sep = "") cat(format, file = file, append = TRUE) if(!is.null(attr(obj, "cycle"))){ cycle <- paste(attr(obj, "cycle"), collapse = " ") cat("\tCYCLE\t", cycle, ";\n", sep="", file = file, append = TRUE) } cat("\tMATRIX\n", file = file, append = TRUE) for (i in 1:nsplits){ slab <- ifelse(flab, attr(obj, "splitlabels")[i], i) swei <- ifelse(fwei, paste(weight[i], "\t"), "") scon <- ifelse(fcon, paste(attr(obj, "confidences")[i], "\t"), "") sint <- ifelse(fint, paste(attr(obj, "intervals")[i], "\t"), "") # cat("\t\t", slab, "\t", weight[i], "\t", scon, sint, paste(obj[[i]], collapse=" "), # ",\n", file = file, append = TRUE, sep = "") cat("\t\t", slab, "\t", swei, scon, sint, paste(obj[[i]], collapse=" "), ",\n", file = file, append = TRUE, sep = "") } cat("\t;\nEND;\n", file = file, append = TRUE) } write.nexus.networx <- function(obj, file = "", taxa=TRUE, splits=TRUE, append=FALSE){ if(!append){ cat("#NEXUS\n\n", file = file) cat("[Splits block for Spectronet or Splitstree]\n", file = file, append = TRUE) cat("[generated by phangorn:\n", file = file, append = TRUE) cat(format(citation("phangorn"), "text"), "]\n\n", file = file, append = TRUE) } ntaxa <- length(obj$tip.label) # TAXON BLOCK if(taxa){ cat(paste("BEGIN TAXA;\n\tDIMENSIONS NTAX=", ntaxa, ";\n", sep = ""), file = file, append = TRUE) if(splits)taxalabel <- attr(obj$splits, "labels") else taxalabel <- obj$tip.label cat("\tTAXLABELS", paste(taxalabel, sep = " "), ";\nEND;\n\n", file = file, append = TRUE) } # SPLITS BLOCK spl <- obj$splits if(splits){ # spl <- changeOrder(spl, obj$tip.label) # orderSplitLabel write.nexus.splits(spl, file = file, weights=NULL, append = TRUE, taxa=FALSE) } nvertices <- max(obj$edge) # if(is.null(attr(obj, "coords"))) if(is.null(obj$.plot$vertices)) vertices <- coords(obj, "2D") else vertices <- obj$.plot$vertices # y-axis differs between R and SplitsTree vertices[,2] <- -vertices[,2] if(is.null(obj$.plot)) edge.col <- obj$.plot$edge.color else edge.col=NULL nedges <- nrow(obj$edge) # NETWORK BLOCK cat(paste("BEGIN NETWORK;\nDIMENSIONS ntax=", ntaxa, "\tnvertices=", nvertices, "\tnedges=", nedges,";\n", sep = ""), file = file, append = TRUE) cat("DRAW to_scale;\n", file = file, append = TRUE) cat("TRANSLATE\n", file = file, append = TRUE) if(is.null(obj$translate)){ for(i in 1:ntaxa){ cat(i, " ", obj$tip.label[i], ",\n", sep="", file = file, append = TRUE) } } else { translate <- obj$translate for(i in 1:nrow(translate)){ cat(translate$node[i], " ", translate$label[i], ",\n", sep="", file = file, append = TRUE) } } cat(";\nVERTICES\n", file = file, append = TRUE) for(i in 1:nvertices){ cat(i, "\t", vertices[i,1], "\t", vertices[i,2], ",\n", sep="", file = file, append = TRUE) } if(!is.null(obj$tip.label)){ cat(";\nVLABELS\n", file = file, append = TRUE) if(is.null(obj$translate)){ for(i in 1:ntaxa){ cat(i, "\t", obj$tip.label[i], ",\n", sep="", file = file, append = TRUE) } } else{ for(i in 1:nrow(translate)){ cat(translate[i,1], " ", translate[i,2], ",\n", sep="", file = file, append = TRUE) } } } # cnet$splitIndex if splits = TRUE cat(";\nEDGES\n", file = file, append = TRUE) if(is.null(obj$.plot$edge.color)) edge.col="black" else edge.col <- obj$.plot$edge.color if(length(edge.col)netStart][1] dims <- grep("DIMENSION", X, ignore.case = TRUE) dims <- dims[(dims>netStart) & (dims0){ tmp = X[dims] tmp = gsub("\\s+", "", tmp) ntaxa <- as.numeric(sub("(.+?)(ntax\\s*\\=\\s*)(\\d+)(.+)", "\\3", tmp, perl = TRUE, ignore.case = TRUE)) nvertices <- as.numeric(sub("(.+?)(nvertices\\s*\\=\\s*)(\\d+)(.+)", "\\3", tmp, perl = TRUE, ignore.case = TRUE)) nedges <- as.numeric(sub("(.+?)(nedges\\s*\\=\\s*)(\\d+)(.+)", "\\3", tmp, perl = TRUE, ignore.case = TRUE)) } transl <- grep("TRANSLATE", X, ignore.case = TRUE) translation <- if (length(transl) == 1 && transl > netStart) TRUE else FALSE translate.nodes <- FALSE if (translation) { end <- semico[semico > transl][1] x <- X[(transl + 1):end] x <- unlist(strsplit(x, "[,; \t]")) x <- x[nzchar(x)] x <- gsub("['\']", "", x) if(length(x) == 2*ntaxa){ TRANS <- matrix(x, ncol = 2, byrow = TRUE) TRANS[, 2] <- gsub("['\"]", "", TRANS[, 2]) TRANS <- list(node=as.numeric(TRANS[,1]), label=TRANS[,2]) # translate.nodes <- TRUE } else{ # TRANS <- matrix(NA, nrow = ntaxa, ncol = 2) y <- as.numeric(x) node <- numeric(ntaxa) label <- character(ntaxa) k=1 for(i in 1:length(x)){ if(!is.na(y[i])) tmp <- y[i] else{ node[k] <- tmp label[k] <- x[i] # TRANS[k, ] <- c(tmp, x[i]) k=k+1 } } TRANS <- list(node=node, label=label) } # n <- dim(TRANS)[1] } vert <- grep("VERTICES", X, ignore.case = TRUE) start <- vert[vert>max(dims, netStart)][1] + 1 end <- semico[semico>start][1] -1 VERT <- matrix(0, nvertices, 3, dimnames = list(NULL, c("id", "x", "y"))) j=1 for(i in start:end){ tmp <- X[i] # tmp <- sub("\\s+", "", tmp) tmp <- gsub("\\,", "", tmp) tmp <- strsplit(tmp, "[[:space:]]")[[1]] VERT[j,1] <- as.numeric(tmp[1]) VERT[j,2] <- as.numeric(tmp[2]) VERT[j,3] <- as.numeric(tmp[3]) j=j+1 } edges <- grep("EDGES", X, ignore.case = TRUE) start <- edges[edges>max(dims, netStart)][1] + 1 end <- semico[semico>start][1] -1 EDGE <- NULL if(splits) EDGE <- matrix(0, nedges, 4, dimnames = list(NULL, c("id", "vert_id_2", "vert_id_2", "splits_id"))) else EDGE <- matrix(0, nedges, 3, dimnames = list(NULL, c("id", "vert_id_2", "vert_id_2"))) j=1 for(i in start:end){ tmp <- X[i] tmp <- gsub("\\,", "", tmp) # tmp <- sub("\\s+", "", tmp) tmp <- strsplit(tmp, "[[:space:]]")[[1]] EDGE[j,1] <- as.numeric(tmp[1]) EDGE[j,2] <- as.numeric(tmp[2]) EDGE[j,3] <- as.numeric(tmp[3]) if(splits){ EDGE[j,4] <- as.numeric(sub("s=", "", tmp[4], ignore.case = TRUE)) } j=j+1 } swapEdge <- function(x, old, new) { x[x==new] <- -1L x[x==old] <- new x[x==-1L] <- old x } swapRow <- function(x, old, new) { tmp <- x[old,] x[old,] <- x[new,] x[new,] <- tmp x } splitIndex <- if(ncol(EDGE)==4) EDGE[,4] else NULL # quick and dirty el = sqrt(rowSums((VERT[EDGE[,2],c(2:3)] - VERT[EDGE[,3],c(2:3)])^2)) edge <- EDGE[,c(2:3)] vert <- VERT[,c(2:3)] if(translate.nodes){ # oldLabel <- as.integer(as.numeric(TRANS[,1])) oldLabel <- as.integer(TRANS$node) for(i in 1:ntaxa){ edge <- swapEdge(edge, oldLabel[i], i) vert <- swapRow(vert, oldLabel[i], i) } } dimnames(edge) <- NULL # y-axis differs between in R and SplitsTree vert[,2] <- -vert[,2] # translate=data.frame(as.numeric(TRANS[,1]), TRANS[,2], stringsAsFactors=FALSE) plot <- list(vertices=vert) obj <- list(edge=edge, tip.label=TRANS$label, edge.length=el, Nnode=max(edge)-ntaxa, splitIndex=splitIndex, splits=spl, translate=TRANS) obj$.plot <- list(vertices = vert, edge.color="black", edge.width=3, edge.lty = 1) class(obj) <- c("networx", "phylo") reorder(obj) obj } read.nexus.splits <- function(file) { X <- scan(file = file, what = "", sep = "\n", quiet = TRUE) semico <- grep(";", X) X=gsub("\\[(.*?)\\]", "", X) # get rid of comments i1 <- grep("TAXLABELS", X, ignore.case = TRUE) taxlab <- ifelse(length(i1)>0, TRUE, FALSE) if (taxlab) { end <- semico[semico >= i1][1] x <- X[(i1):end] # assumes not a 'new line' after "TRANSLATE" x <- gsub("TAXLABELS", "", x, ignore.case = TRUE) x <- unlist(strsplit(x, "[,; \t]")) x <- x[nzchar(x)] x <- gsub("['\"]", "", x) xntaxa <- length(x) } sp <- grep("SPLITS;", X, ignore.case = TRUE) spEnd <- grep("END;", X, ignore.case = TRUE) spEnd <- spEnd[spEnd>sp][1] dims <- grep("DIMENSION", X, ignore.case = TRUE) cyc <- grep("CYCLE", X, ignore.case = TRUE) matr <- grep("MATRIX", X, ignore.case = TRUE) format <- grep("FORMAT", X, ignore.case = TRUE) start <- matr[matr>sp][1] + 1 end <- semico[semico>start][1] -1 format <- format[(format>sp) & (format0){ tmp = X[format] tmp = gsub("\\;", "", tmp) tmp = gsub("\\s+", "", tmp) flab = grepl("labels=left", tmp, ignore.case = TRUE) fwei = grepl("weights=yes", tmp, ignore.case = TRUE) fcon = grepl("confidences=yes", tmp, ignore.case = TRUE) fint = grepl("intervals=yes", tmp, ignore.case = TRUE) # = as.numeric(na.omit(as.numeric(strsplit(tmp, " ")[[1]]))) ind = cumsum(c(flab, fwei, fcon, fint)) mformat = sum(c(flab, fwei, fcon, fint)) } if(fint)intervals = numeric(end - start + 1) if(fcon)confidences = numeric(end - start + 1) if(flab)labels = vector("character", end - start + 1) for(i in start:end){ tmp = X[i] tmp = sub("\\s+", "", tmp) tmp = strsplit(tmp, "\t")[[1]] if(flab)labels[j] = as.numeric(tmp[ind[1]]) if(fwei)weights[j] = as.numeric(tmp[ind[2]]) if(fcon)confidences[j] = as.numeric(tmp[ind[3]]) if(fint)intervals[j] = as.numeric(tmp[ind[4]]) tmp = tmp[length(tmp)] tmp = gsub("\\,", "", tmp) res[[j]] = as.integer(na.omit(as.numeric(strsplit(tmp, " ")[[1]]))) j=j+1 } if(length(cyc)>0){ tmp = X[cyc] tmp = gsub("\\;", "", tmp) tmp = gsub("CYCLE", "", tmp, ignore.case = TRUE) tmp = sub("\\s+", "", tmp) cyc = as.integer(na.omit(as.numeric(strsplit(tmp, " ")[[1]]))) } attr(res, "labels") = x if(fwei)attr(res, "weights") = weights if(fint)attr(res, "intervals") = intervals if(fcon)attr(res, "confidences") = confidences if(flab)attr(res, "splitlabels") = labels attr(res, "cycle") = cyc class(res) = "splits" res } ################################################################################ # delta.score ################################################################################ # Calculated from mathematical description given in Gray et al. (2010) Phil. # Trans. Roy. Soc. B. # delta.score reference: Holland et al. (2002) Mol. Biol. Evol. ################################################################################ # Calculating Delta and Q-residual scores # internal delta.quartet <- function(quartet,dist.dna) { m1 <- dist.dna[quartet[1],quartet[2]] + dist.dna[quartet[3],quartet[4]] m2 <- dist.dna[quartet[1],quartet[3]] + dist.dna[quartet[2],quartet[4]] m3 <- dist.dna[quartet[1],quartet[4]] + dist.dna[quartet[2],quartet[3]] m <- sort(c(m1,m2,m3),decreasing=T) if((m[1]-m[3])!=0) { ret <- (m[1]-m[2])/(m[1]-m[3]) } else { ret <- 0 } return(ret) } delta.score <- function(x, arg="mean", ...) { # dist.dna <- as.matrix(dist.dna(dna,"raw")) # dist.dna(dna,"raw") is equivalent to dist.hamming(as.phyDat(dna), exclude="all") dist.dna <- as.matrix(dist.hamming(x, ...)) # Number of quartets # choose(length(names(x)),4) # Create all quartets all.quartets <- t(combn(names(x),4)) delta.values <- apply(all.quartets[,],1,delta.quartet,dist.dna) if (!arg%in%c("all", "mean","sd")) stop("return options are: all, mean, or sd") if (arg=='all') return(delta.values) if (arg=='mean') return(mean(delta.values)) if (arg=='sd') return(sd(delta.values)) } phangorn/R/phyDat.R0000644000175100001440000010345713005450715013673 0ustar hornikusers# # Data structures for ML and MP # fast.table <- function (data) { if(!is.data.frame(data)) data = as.data.frame(data, stringsAsFactors = FALSE) da = do.call("paste", c(data, sep = "\r")) ind = !duplicated(da) levels = da[ind] cat <- factor(da,levels = levels) nl <- length(levels(cat)) bin <- (as.integer(cat) - 1) pd <- nl bin <- bin[!is.na(bin)] if (length(bin)) bin <- bin + 1 y <- tabulate(bin, pd) result=list(index = bin, weights = y, data = data[ind,]) result } phyDat.default <- function (data, levels = NULL, return.index = TRUE, contrast = NULL, ambiguity = "?", compress=TRUE, ...) { if (is.matrix(data)) nam = row.names(data) else nam = names(data) if(is.null(nam))stop("data object must contain taxa names") if(inherits(data, "character")) data <- as.matrix(data) if (inherits(data,"DNAbin")) data = as.character(data) if (is.matrix(data)) data = as.data.frame(t(data), stringsAsFactors = FALSE) # new 4.4.2016 bug fix (reported by Eli Levy Karin) # if (is.vector(data) && !is.list(data))data = as.data.frame(data, stringsAsFactors = FALSE) else data = as.data.frame(data, stringsAsFactors = FALSE) # data = data.frame(as.matrix(data), stringsAsFactors = FALSE) if(length(data[[1]])==1) compress=FALSE if(compress){ ddd = fast.table(data) data = ddd$data weight = ddd$weight index = ddd$index } else{ p = length(data[[1]]) weight = rep(1, p) index = 1:p } q = length(data) p = length(data[[1]]) tmp <- vector("list", q) if (!is.null(contrast)) { levels = colnames(contrast) all.levels = rownames(contrast) rownames(contrast) = NULL } else { if (is.null(levels)) stop("Either argument levels or contrast has to be supplied") l = length(levels) contrast = diag(l) all.levels = levels if (!is.null(ambiguity)) { all.levels = c(all.levels, ambiguity) k = length(ambiguity) if (k > 0) contrast = rbind(contrast, matrix(1, k, l)) } } # row.names(data) = as.character(1:p) # data = na.omit(data) # rn = as.numeric(rownames(data)) d = dim(data) att = attributes(data) # print(system.time(match(unlist(data), all.levels))) data = match(unlist(data), all.levels) attr(data, "dim") = d data = as.data.frame(data, stringsAsFactors=FALSE) attributes(data) = att # att = attributes(data) # data = lapply(data, match, all.levels) # avoid unlist # attributes(data) = att row.names(data) = as.character(1:p) data = na.omit(data) aaa = match(index, attr(data, "na.action")) index = index[is.na(aaa)] index = match(index, unique(index)) rn = as.numeric(rownames(data)) attr(data, "na.action") = NULL weight = weight[rn] p = dim(data)[1] names(data) = nam attr(data, "row.names") = NULL attr(data, "weight") = weight attr(data, "nr") = p attr(data, "nc") = length(levels) if (return.index) attr(data, "index") = index attr(data, "levels") = levels attr(data, "allLevels") = all.levels attr(data, "type") = "USER" attr(data, "contrast") = contrast class(data) = "phyDat" data } phyDat.DNA = function (data, return.index = TRUE) { if (is.matrix(data)) nam = row.names(data) else nam = names(data) if (inherits(data,"DNAbin")) data = as.character(data) if(inherits(data, "character")) data <- as.matrix(data) if (is.matrix(data)) data = as.data.frame(t(data), stringsAsFactors = FALSE) else data = as.data.frame(data, stringsAsFactors = FALSE) data = data.frame(tolower(as.matrix(data)), stringsAsFactors = FALSE) ac = c("a", "c", "g", "t", "u", "m", "r", "w", "s", "y", "k", "v", "h", "d", "b", "n", "?", "-") AC = matrix(c(c(1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1), c(0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1), c(0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1), c(0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1)), 18, 4, dimnames = list(NULL, c("a", "c", "g", "t"))) # ddd = fast.table(data) compress=TRUE if(length(data[[1]])==1) compress=FALSE if(compress){ ddd = fast.table(data) data = ddd$data weight = ddd$weight index = ddd$index } else{ p = length(data[[1]]) weight = rep(1, p) index = 1:p } # data = ddd$data # index = ddd$index q = length(data) p = length(data[[1]]) d = dim(data) att = attributes(data) data = match(unlist(data), ac) attr(data, "dim") = d data = as.data.frame(data, stringsAsFactors=FALSE) attributes(data) = att row.names(data) = as.character(1:p) data = na.omit(data) rn = as.numeric(rownames(data)) aaa = match(index, attr(data, "na.action")) index = index[is.na(aaa)] index = match(index, unique(index)) rn = as.numeric(rownames(data)) attr(data, "na.action") = NULL weight = weight[rn] # weight = ddd$weight[rn] p = dim(data)[1] names(data) = nam attr(data, "row.names") = NULL attr(data, "weight") = weight attr(data, "nr") = p attr(data, "nc") = 4 if (return.index) attr(data, "index") = index attr(data, "levels") = c("a", "c", "g", "t") attr(data, "allLevels") = ac attr(data, "type") = "DNA" attr(data, "contrast") = AC class(data) = "phyDat" data } phyDat.AA <- function (data, return.index = TRUE) { if(is.matrix(data)) nam = row.names(data) else nam = names(data) if (inherits(data,"DNAbin")) data = as.character(data) if(inherits(data, "character")) data <- as.matrix(data) if (is.matrix(data)) data = as.data.frame(t(data), stringsAsFactors = FALSE) else data = as.data.frame(data, stringsAsFactors = FALSE) data = data.frame(tolower(as.matrix(data)), stringsAsFactors = FALSE) aa <- c("a", "r", "n", "d", "c", "q", "e", "g", "h", "i", "l", "k", "m", "f", "p", "s", "t", "w", "y", "v") aa2 <- c("a", "r", "n", "d", "c", "q", "e", "g", "h", "i", "l", "k", "m", "f", "p", "s", "t", "w", "y", "v", "b", "z", "x", "-", "?") AA <- diag(20) AA <- rbind(AA, matrix(0, 5, 20)) AA[21, 3] <- AA[21, 4] <- 1 # Aspartate or Asparagine AA[22, 6] <- AA[22, 7] <- 1 # AA[23:25, ] = 1 dimnames(AA) <- list(aa2, aa) # ddd = fast.table(data) # data = ddd$data # index = ddd$index compress=TRUE if(length(data[[1]])==1) compress=FALSE if(compress){ ddd = fast.table(data) data = ddd$data weight = ddd$weight index = ddd$index } else{ p = length(data[[1]]) weight = rep(1, p) index = 1:p } q = length(data) p = length(data[[1]]) tmp <- vector("list", q) d = dim(data) att = attributes(data) data = match(unlist(data), aa2) attr(data, "dim") = d data = as.data.frame(data, stringsAsFactors=FALSE) attributes(data) = att row.names(data) = as.character(1:p) data = na.omit(data) rn = as.numeric(rownames(data)) aaa = match(index, attr(data, "na.action")) index = index[is.na(aaa)] index = match(index, unique(index)) rn = as.numeric(rownames(data)) attr(data, "na.action") = NULL # weight = ddd$weight[rn] weight = weight[rn] p = dim(data)[1] names(data) = nam attr(data, "row.names") = NULL attr(data, "weight") = weight attr(data, "nr") = p attr(data, "nc") = 20 if (return.index) attr(data, "index") = index attr(data, "levels") = aa attr(data, "allLevels") = aa2 attr(data, "type") = "AA" attr(data, "contrast") = AA class(data) = "phyDat" data } phyDat.codon <- function (data, return.index = TRUE) { if(is.matrix(data)) nam = row.names(data) else nam = names(data) if (inherits(data,"DNAbin")) data = as.character(data) if(inherits(data, "character")) data <- as.matrix(data) if (is.matrix(data)) data = as.data.frame(t(data), stringsAsFactors = FALSE) else data = as.data.frame(data, stringsAsFactors = FALSE) data = data.frame(tolower(as.matrix(data)), stringsAsFactors = FALSE) data[data=="u"] = "t" splseq = function (seq, frame = 0) { starts <- seq(from = frame + 1, to = length(seq), by = 3L) sapply(starts, function(x) paste(seq[x:(x + 2L)], collapse="")) } data = data.frame(lapply(data, splseq)) # ddd = fast.table(data) compress=TRUE if(nrow(data)==1) compress=FALSE if(compress){ ddd = fast.table(data) data = ddd$data weight = ddd$weight index = ddd$index } else{ p = length(data[[1]]) weight = rep(1, p) index = 1:p } codon = c("aaa", "aac", "aag", "aat", "aca", "acc", "acg", "act", "aga", "agc", "agg", "agt", "ata", "atc", "atg", "att", "caa", "cac", "cag", "cat", "cca", "ccc", "ccg", "cct", "cga", "cgc", "cgg", "cgt", "cta", "ctc", "ctg", "ctt", "gaa", "gac", "gag", "gat", "gca", "gcc", "gcg", "gct", "gga", "ggc", "ggg", "ggt", "gta", "gtc", "gtg", "gtt", "tac", "tat", "tca", "tcc", "tcg", "tct", "tgc", "tgg", "tgt", "tta", "ttc", "ttg", "ttt") # ohne Stopcodons "taa", "tag", "tga", CODON <- diag(61) dimnames(CODON) <- list(codon, codon) # data = ddd$data # index = ddd$index q = length(data) p = length(data[[1]]) tmp <- vector("list", q) d = dim(data) att = attributes(data) data = match(unlist(data), codon) attr(data, "dim") = d data = as.data.frame(data, stringsAsFactors=FALSE) attributes(data) = att row.names(data) = as.character(1:p) data = na.omit(data) rn = as.numeric(rownames(data)) aaa = match(index, attr(data, "na.action")) index = index[is.na(aaa)] index = match(index, unique(index)) rn = as.numeric(rownames(data)) attr(data, "na.action") = NULL # weight = ddd$weight[rn] weight = weight[rn] p = dim(data)[1] names(data) = nam attr(data, "row.names") = NULL attr(data, "weight") = weight attr(data, "nr") = p attr(data, "nc") = 61 if (return.index) attr(data, "index") = index attr(data, "levels") = codon attr(data, "allLevels") = codon attr(data, "type") = "CODON" attr(data, "contrast") = CODON class(data) = "phyDat" data } dna2codon <- function(x){ if(!inherits(x, "phyDat"))stop("x needs to be of class phyDat!") phyDat.codon(as.character(x)) } codon2dna <- function(x){ if(!inherits(x, "phyDat"))stop("x needs to be of class phyDat!") phyDat.DNA(as.character(x)) } as.phyDat <- function (x, ...){ if (inherits(x,"phyDat")) return(x) UseMethod("as.phyDat") } as.phyDat.factor <- function(x, ...){ nam <- names(x) lev <- levels(x) x <- as.character(x) names(x) <- nam phyDat(x, type="USER", levels = lev, ...) } as.phyDat.DNAbin <- function(x,...) phyDat.DNA(x,...) as.phyDat.alignment <- function (x, type="DNA",...) { x$seq <- tolower(x$seq) data <- sapply(x$seq, strsplit, "") names(data) <- x$nam if(type=="DNA") dat <- phyDat.DNA(data,...) if(type=="AA") dat <- phyDat.AA(data, ...) if(type=="CODON") dat <- phyDat.codon(data, ...) if(type=="USER") dat <- phyDat.default(data, ...) dat } #as.alignment.phyDat <- function(x, ...) as.alignment(as.character(x)) phyDat2alignment <- function(x){ z = as.character(x) nam = rownames(z) type = attr(x, "type") seq <- switch(type, DNA = tolower(apply(z, 1, paste, collapse="")), AA = toupper(apply(z, 1, paste, collapse=""))) names(seq) <- NULL res <- list(nb=length(seq), nam=nam, seq=seq, com=NA) class(res) = "alignment" res } as.phyDat.MultipleAlignment <- function(x, ...){ if (requireNamespace('Biostrings')){ if(inherits(x, "DNAMultipleAlignment")) res <- phyDat.DNA(Biostrings::as.matrix(x)) if(inherits(x, "RNAMultipleAlignment")) res <- phyDat.DNA(Biostrings::as.matrix(x)) if(inherits(x, "AAMultipleAlignment")) res <- phyDat.AA(Biostrings::as.matrix(x)) return(res) } return(NULL) } as.MultipleAlignment <- function (x, ...){ if (inherits(x,"MultipleAlignment")) return(x) UseMethod("as.MultipleAlignment") } as.MultipleAlignment.phyDat <- function(x, ...){ if (requireNamespace('Biostrings')){ z = as.character(x) nam = rownames(z) type = attr(x, "type") seq <- switch(type, DNA = tolower(apply(z, 1, paste, collapse="")), AA = toupper(apply(z, 1, paste, collapse=""))) if(type=="DNA") return(Biostrings::DNAMultipleAlignment(seq)) if(type=="AA") return(Biostrings::AAMultipleAlignment(seq)) } return(NULL) } phyDat2MultipleAlignment <- as.MultipleAlignment.phyDat as.phyDat.matrix <- function (x, ...) phyDat(data=x, ...) as.phyDat.character <- function (x, ...) phyDat(data=x, ...) as.phyDat.data.frame <- function (x, ...) phyDat(data=x, ...) acgt2ry <- function(obj){ ac = c("a", "c", "g", "t", "u", "m", "r", "w", "s", "y", "k", "v", "h", "d", "b", "n", "?", "-") AC = matrix(c(c(1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1), c(0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1), c(0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1), c(0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1)), 18, 4, dimnames = list(NULL, c("a", "c", "g", "t"))) ry = AC[c(7,10),] RY = AC %*% t(ry) RY[RY==2] = 1 dimnames(RY) = list(NULL, c("r", "y")) attr(obj, "levels") = c("r", "y") attr(obj, "nc") = 2 attr(obj, "type") = "USER" attr(obj, "contrast") = RY obj=phyDat.default(as.character(obj, allLevels=FALSE), levels = c("r", "y"), ambiguity = NULL) obj } # replace as.character.phyDat weniger Zeilen, works also for codons as.character.phyDat <- function (x, allLevels=TRUE, ...) { nr <- attr(x, "nr") nc <- attr(x, "nc") type <- attr(x, "type") labels = attr(x, "allLevels") if (!is.null(attr(x, "index"))) { index = attr(x, "index") if (is.data.frame(index)) index <- index[, 1] } else index = rep(1:nr, attr(x, "weight")) if (type == "USER") { #levels in acgt2ry if(!allLevels){ tmp = attr(x, "levels") contrast = attr(x, "contrast") # contrast=AC contrast[contrast>0] = 1 ind = which(rowSums(contrast)==1) contrast[rowSums(contrast)>1,] = 0 labels = rep(NA, length(attr(x, "allLevels"))) labels[ind] = tmp[contrast%*%c(1:length(tmp))] } } if(type == "AA") labels <- toupper(labels) if(type == "CODON"){ nr <- length(index) result = matrix(NA, nrow = length(x), ncol = 3L*nr) labels = strsplit(labels, "") for (i in 1:length(x)) result[i, ] <- unlist(labels[ x[[i]][index] ]) } else { result = matrix(NA, nrow = length(x), ncol = nr) for (i in 1:length(x)) result[i, ] <- labels[x[[i]]] result = result[, index, drop = FALSE] } rownames(result) = names(x) result } as.data.frame.phyDat <- function(x, ...){ nr <- attr(x, "nr") nc <- attr(x, "nc") labels <- attr(x, "allLevels") if(attr(x, "type") == "AA") labels <- toupper(labels) result <- vector("list", length(x)) if (is.null(attr(x, "index"))) index = rep(1:nr, attr(x, "weight")) else { index = attr(x, "index") if (is.data.frame(index)) index <- index[, 1] } for (i in 1:length(x)) result[[i]] <- labels[x[[i]][index]] attr(result, "names") <- names(x) attr(result, "row.names") <- 1:length(index) attr(result, "class") <- "data.frame" result } #as.DNAbin.phyDat <- function(x,...) { # if(attr(x, "type")=="DNA") return(as.DNAbin(as.character(x, ...))) # else stop("x must be a nucleotide sequence") #} # quite abit faster as.DNAbin.phyDat <- function (x, ...) { if(attr(x, "type")=="DNA"){ nr <- attr(x, "nr") ac = attr(x, "allLevels") result = matrix(as.raw(0), nrow = length(x), ncol = nr) # from ape ._cs_ cs <- c("a", "g", "c", "t", "r", "m", "w", "s", "k", "y", "v", "h", "d", "b", "n", "-", "?") # from ape ._bs_ bs <- as.raw(c(136, 72, 40, 24, 192, 160, 144, 96, 80, 48, 224, 176, 208, 112, 240, 4, 2)) ord <- match(ac, cs) ord[5] <- 4 for (i in 1:length(x)){ ind <- ord[x[[i]]] result[i,] <- bs[ind] } if (is.null(attr(x, "index"))) index = rep(1:nr, attr(x, "weight")) else { index = attr(x, "index") if (is.data.frame(index)) index <- index[, 1] } result = result[, index, drop = FALSE] rownames(result) = names(x) class(result) <- "DNAbin" return(result) } else stop("x must be a nucleotide sequence") } #if (getRversion() >= "2.15.1") utils::globalVariables("as.AAbin") as.AAbin.phyDat <- function(x,...) { if(attr(x, "type")=="AA") return(as.AAbin(as.character(x, ...))) else stop("x must be a amino acid sequence") } phyDat <- function (data, type="DNA", levels=NULL, return.index = TRUE,...) { if (inherits(data,"DNAbin")) type <- "DNA" pt <- match.arg(type, c("DNA", "AA", "CODON", "USER")) if(pt=="DNA") dat <- phyDat.DNA(data, return.index=return.index,...) if(pt=="AA") dat <- phyDat.AA(data, return.index=return.index, ...) if(pt=="CODON") dat <- phyDat.codon(data, return.index=return.index, ...) if(pt=="USER") dat <- phyDat.default(data, levels = levels, return.index=return.index, ...) dat } print.phyDat = function (x, ...) { cat(length(x), "sequences with",sum(attr(x,"weight")), "character and",attr(x,"nr"),"different site patterns.\n") cat("The states are",attr(x,"levels"), "\n") } # in C++ to replace aggregate or use of distinct from dplyr / data.table aggr <- function(weight, ind){ res <- numeric(max(ind)) for(i in seq_along(weight)) res[ind[i]] = res[ind[i]] + weight[i] res } # data has to be a data.frame in cbind.phyDat fast.table2 <- function (data) { if(!is.data.frame(data)) data <- as.data.frame(data, stringsAsFactors = FALSE) da <- do.call("paste", data) ind <- !duplicated(da) levels <- da[ind] cat <- factor(da,levels = levels) nl <- length(levels) bin <- (as.integer(cat) - 1L) bin <- bin[!is.na(bin)] if (length(bin)) bin <- bin + 1L result=list(index = bin, pos = ind) result } # new cbind.phyDat cbind.phyDat <- function(..., gaps="-", compress=TRUE){ object <- as.list(substitute(list(...)))[-1] x <- list(...) n <- length(x) if (n == 1) return(x[[1]]) type <- attr(x[[1]], "type") nr <- numeric(n) ATTR <- attributes(x[[1]]) nr[1] <- sum(attr(x[[1]], "weight")) levels <- attr(x[[1]], "levels") allLevels <- attr(x[[1]], "allLevels") gapsInd <- match(gaps, allLevels) snames <- vector("list", n) # names(x[[1]]) vec <- numeric(n+1) wvec <- numeric(n+1) objNames <- as.character(object) if(any(duplicated(objNames))) objNames <- paste0(objNames, 1:n) # tmp <- as.character(x[[1]]) for(i in 1:n){ snames[[i]] <- names(x[[i]]) nr[i] <- sum(attr(x[[i]], "weight")) vec[i+1] <- attr(x[[i]], "nr") wvec[i+1] <- sum(attr(x[[i]], "weight")) } vec <- cumsum(vec) wvec <- cumsum(wvec) snames <- unique(unlist(snames)) weight <- numeric(vec[n+1]) index <- numeric(wvec[n+1]) ATTR$names <- snames ATTR$nr <- vec[n+1] tmp <- matrix(gapsInd, vec[n+1], length(snames), dimnames = list(NULL, snames)) tmp <- as.data.frame(tmp) add.index <- TRUE for(i in 1:n){ nam <- names(x[[i]]) tmp[(vec[i]+1):vec[i+1], nam] <- x[[i]][nam] weight[(vec[i]+1):vec[i+1]] <- attr(x[[i]], "weight") } if(compress){ ddd <- fast.table2(tmp) tmp <- tmp[ddd$pos,] weight <- aggregate(weight, by=list(ddd$index), FUN=sum)$x } for(i in 1:n){ tmp2 <- attr(x[[i]], "index") if(!is.null(tmp2)){ if(is.data.frame(tmp2))index[(wvec[i]+1):wvec[i+1]] <- ddd$index[(vec[i]+1):vec[i+1]][tmp2[,1]] else index[(wvec[i]+1):wvec[i+1]] <- ddd$index[(vec[i]+1):vec[i+1]][tmp2] } else add.index <- FALSE } if(add.index)ATTR$index <- data.frame(index = index, genes=rep(objNames, nr)) ATTR$weight <- weight ATTR$nr <- length(weight) attributes(tmp) <- ATTR tmp } c.phyDat <- cbind.phyDat write.phyDat <- function(x, file, format="phylip", colsep = "", nbcol=-1, ...){ formats <- c("phylip", "nexus", "interleaved", "sequential", "fasta") format <- match.arg(tolower(format), formats) if(format=="nexus"){ type = attr(x, "type") if(type=="DNA") write.nexus.data(as.list(as.data.frame(x)), file, format = "dna",...) else write.nexus.data(as.list(as.data.frame(x)), file, format = "protein", ...) } else{ if(format=="phylip") format = "interleaved" write.dna(as.character(x), file, format=format, colsep = colsep, nbcol=nbcol, ...) } } read.phyDat <- function(file, format="phylip", type="DNA", ...){ formats <- c("phylip", "nexus", "interleaved", "sequential", "fasta", "clustal") format <- match.arg(tolower(format), formats) if(format=="nexus") data=read.nexus.data(file, ...) else { if(format=="phylip")format="interleaved" #"sequential" if (type == "DNA" || type == "CODON"){ data = read.dna(file, format, as.character = TRUE, ...) } if (type == "AA") data = read.aa(file, format=format, ...) # raus } phyDat(data, type, return.index = TRUE) } baseFreq <- function(obj, freq=FALSE, all=FALSE, drop.unused.levels = FALSE){ if (!inherits(obj,"phyDat")) stop("data must be of class phyDat") labels <- attr(obj, "allLevels") weight <- attr(obj,"weight") n <- length(obj) res <- numeric(length(labels)) D = diag(length(labels)) for(i in 1:n)res <- res + colSums(D[obj[[i]],, drop=FALSE]*weight) names(res) <- labels if(!all) res <- res[attr(obj, "levels")] if(!freq)res <- res/sum(res) if(drop.unused.levels) return(res[res>0]) res } phylo <- function(edge, tip, edge.length=NULL){ res <- list(edge=edge, tip.label=tip, edge.length=edge.length) class(res)="phylo" res } getCols <- function (data, cols) { attrib = attributes(data) attr(data, "class") <- "list" data = data[cols] if (is.character(cols)) attrib$names = cols else attrib$names = attrib$names[cols] attributes(data) = attrib attr(data, "class") <- "phyDat" data } # allows negative indexing subset(dat,,-c(3:5)) getRows <- function (data, rows, site.pattern = TRUE) { index <- attr(data, "index") if(is.data.frame(index))index = index[,1] if(!site.pattern){ # & all(rows>0) weight = tabulate(index[rows]) ind = which(weight>0) rows = ind # rows[ind] weight = weight[ind] } for (i in 1:length(data)){ if(is.matrix(data[[i]]))data[[i]] = data[[i]][rows,] else data[[i]] = data[[i]][rows] } attr(data, "weight") = attr(data, "weight")[rows] if(!site.pattern) attr(data, "weight") = weight attr(data, "nr") = length(attr(data, "weight")) attr(data, "index") = NULL data } subset.phyDat <- function (x, subset, select, site.pattern = TRUE,...) { if (!missing(subset)) x <- getCols(x, subset) if (!missing(select)){ # if(!site.pattern){ # if(is.data.frame(attr(x, "index"))) select <- attr(x, "index")[select,1] # else select <- attr(x, "index")[select] # } if(any(is.na(select))) return(NULL) x <- getRows(x, select, site.pattern=site.pattern) } x } duplicated_phyDat <- function(x, ...){ dm <- as.matrix(dist.hamming(x)) diag(dm) <- 1 res <- logical(nrow(dm)) if(all(dm>0)) return(res) tmp <- which(dm==0, arr.ind = TRUE, useNames = FALSE) tmp <- tmp[tmp[,1] < tmp[,2],2] res[unique(tmp)]=TRUE res } duplicated_map2 <- function(x, ...){ dm <- dist.hamming(x) if(all(dm>0)) return(NULL) dm <- as.matrix(dm) tmp <- which(dm==0, arr.ind = TRUE, useNames = FALSE) tmp <- tmp[tmp[,1] < tmp[,2],] tmp[] = names(x)[tmp] g <- graph_from_edgelist(tmp, directed = FALSE) clu <- components(g) gr <- groups(clu) mapping <- matrix(NA, 1,2) for(i in 1:length(gr)){ if(length(gr[[i]])==2)mapping <- rbind(mapping, gr[[i]]) else{ tmplab = gr[[i]] sg <- sum(dm[tmplab, tmplab]) if(sg==0)mapping <- rbind(mapping, cbind(tmplab[1], tmplab[-1])) } } mapping[-1,c(2,1)] } duplicated_map <- function(x, ...){ # exact matches dup <- duplicated(x) mapping <- NULL if(any(dup)){ # && optNNI labels <- names(x) ind <- match(subset(x, dup), x) mapping <- cbind(labels[dup], labels[ind]) } mapping } unique.phyDat <- function(x, incomparables=FALSE, identical=TRUE, ...){ if(identical) return(getCols(x, !duplicated(x))) getCols(x, !duplicated_phyDat(x)) } removeUndeterminedSites <- function(x, use.contrast=TRUE, undetermined=c("?", "n", "-"), ...){ nc <- attr(x, "nc") nr <- attr(x, "nr") contrast <- attr(x, "contrast") if(use.contrast) ind <- which( (contrast %*% rep(1, nc)) == nc ) else ind <- sort(match(undetermined, attr(x, "allLevels"))) tmp <- x[[1]] %in% ind for(i in 2:length(x)) tmp = tmp & (x[[i]] %in% ind) if(any(tmp)) x <- getRows(x, (1:nr)[!tmp]) #getRows(x, -which(tmp)) x } removeParsUninfoSites <- function(data){ nr <- attr(data, "nr") pis <- parsinfo(data) if (length(pis) > 0) data <- getRows(data, c(1:nr)[-pis[, 1]], TRUE) } allSitePattern <- function(n,levels=c("a","c","g","t"), names=NULL){ l=length(levels) X=vector("list", n) if(is.null(names))names(X) = paste0("t", 1:n) else names(X)=names for(i in 1:n) X[[i]] = rep(rep(levels, each=l^(i-1)),l^(n-i)) X = as.data.frame(X) phyDat.default(X, levels, compress=FALSE) } constSitePattern <- function(n,levels=c("a","c","g","t"), names=NULL){ l=length(levels) X=matrix(0, l,n) X = matrix(rep(levels, each=n), n, l) if(is.null(names))rownames(X) = paste0("t", 1:n) else rownames(X)=names phyDat.default(X, levels) } write.phylip <- function(data, weight, file=""){ n = sum(weight) m = dim(data)[2] cat(m,n,"\n",file = file) for(i in 1:m) cat(colnames(data)[i]," ",toupper(rep(data[,i],weight)),"\n", sep="", file=file, append=TRUE) } read.FASTA.AA <- function (file) { if (length(grep("^(ht|f)tp:", file))) { url <- file file <- tempfile() download.file(url, file) } sz <- file.info(file)$size x <- readBin(file, "raw", sz) icr <- which(x == as.raw(13)) if (length(icr)) x <- x[-icr] res <- .Call("rawStream2phyDat", x) aa <- c("a", "r", "n", "d", "c", "q", "e", "g", "h", "i", "l", "k", "m", "f", "p", "s", "t", "w", "y", "v") aa2 <- c("a", "r", "n", "d", "c", "q", "e", "g", "h", "i", "l", "k", "m", "f", "p", "s", "t", "w", "y", "v", "b", "z", "x", "-", "?") AA <- diag(20) AA <- rbind(AA, matrix(0, 5, 20)) AA[21, 3] <- AA[21, 4] <- 1 # Aspartate or Asparagine AA[22, 6] <- AA[22, 7] <- 1 # AA[23:25, ] = 1 dimnames(AA) <- list(aa2, aa) ddd = fast.table(res) data = ddd$data names(data) <- sub("^ +", "", names(data)) row.names(data) = NULL attr(data, "row.names") = NULL attr(data, "weight") = ddd$weight attr(data, "nr") = length(ddd$weight) attr(data, "nc") = 20 attr(data, "index") = as.integer(ddd$index) attr(data, "levels") = aa attr(data, "allLevels") = aa2 attr(data, "type") = "AA" attr(data, "contrast") = AA class(data) = "phyDat" data } # throw out read.aa <- function (file, format = "interleaved", skip = 0, nlines = 0, comment.char = "#", seq.names = NULL) { getTaxaNames <- function(x) { x <- sub("^ +", "", x) x <- sub(" +$", "", x) x <- sub("^['\"]", "", x) x <- sub("['\"]$", "", x) x } format <- match.arg(format, c("interleaved", "sequential", "fasta")) phylip <- if (format %in% c("interleaved", "sequential")) TRUE else FALSE if (format == "fasta") { obj <- read.FASTA.AA(file) return(obj) } X <- scan(file = file, what = character(), sep = "\n", quiet = TRUE, skip = skip, nlines = nlines, comment.char = comment.char) if (phylip) { fl <- X[1] oop <- options(warn = -1) fl.num <- as.numeric(unlist(strsplit(gsub("^ +", "", fl), " +"))) options(oop) if (all(is.na(fl.num))) stop("the first line of the file must contain the dimensions of the data") if (length(fl.num) != 2) stop("the first line of the file must contain TWO numbers") else { n <- fl.num[1] s <- fl.num[2] } X <- X[-1] obj <- vector("character", n * s) dim(obj) <- c(n, s) } if (format == "interleaved") { fl <- X[1] fl <- unlist(strsplit(fl, NULL)) bases <- grep("[-AaRrNnDdCcQqEeGgHhIiLlKkMmFfPpSsTtWwYyVvBbZzXx?]", fl) z <- diff(bases) for (i in 1:length(z)) if (all(z[i:(i + 8)] == 1)) break start.seq <- bases[i] if (is.null(seq.names)) seq.names <- getTaxaNames(substr(X[1:n], 1, start.seq - 1)) X[1:n] <- substr(X[1:n], start.seq, nchar(X[1:n])) X <- gsub(" ", "", X) nl <- length(X) for (i in 1:n) obj[i, ] <- unlist(strsplit(X[seq(i, nl, n)], NULL)) } if (format == "sequential") { fl <- X[1] taxa <- character(n) j <- 1 for (i in 1:n) { bases <- grep("[-AaRrNnDdCcQqEeGgHhIiLlKkMmFfPpSsTtWwYyVvBbZzXx?]", unlist(strsplit(X[j], NULL))) z <- diff(bases) for (k in 1:length(z)) if (all(z[k:(k + 8)] == 1)) break start.seq <- bases[k] taxa[i] <- substr(X[j], 1, start.seq - 1) sequ <- substr(X[j], start.seq, nchar(X[j])) sequ <- gsub(" ", "", sequ) j <- j + 1 while (nchar(sequ) < s) { sequ <- paste0(sequ, gsub(" ", "", X[j])) j <- j + 1 } obj[i, ] <- unlist(strsplit(sequ, NULL)) } if (is.null(seq.names)) seq.names <- getTaxaNames(taxa) } if (format == "fasta") return(read.FASTA.AA(file)) # start <- grep("^ {0,}>", X) # taxa <- X[start] # n <- length(taxa) # obj <- vector("list", n) # if (is.null(seq.names)) { # taxa <- sub("^ {0,}>", "", taxa) # seq.names <- getTaxaNames(taxa) # } # start <- c(start, length(X) + 1) # for (i in 1:n) obj[[i]] <- unlist(strsplit(gsub(" ", # "", X[(start[i] + 1):(start[i + 1] - 1)]), NULL)) # } if (phylip) { rownames(obj) <- seq.names obj <- tolower(obj) } else { names(obj) <- seq.names obj <- lapply(obj, tolower) } obj } genlight2phyDat <- function(x, ambiguity=NA){ tmp <- as.matrix(x) lev <- na.omit(unique(as.vector(tmp))) phyDat(tmp, "USER", levels=lev, ambiguity=ambiguity) } image.phyDat <- function(x, ...){ if(attr(x, "type")=="AA")image(as.AAbin(x), ...) if(attr(x, "type")=="DNA")image(as.DNAbin(x), ...) else return(NULL) } phangorn/R/fitch.R0000644000175100001440000005335412766054523013551 0ustar hornikusersfitch <- function (tree, data, site="pscore") { if (!inherits(data,"phyDat")) stop("data must be of class phyDat") levels <- attr(data, "levels") if(inherits(tree,"multiPhylo")){ TL = attr(tree,"TipLabel") if (!is.null(TL)){ data <- subset(data, TL) nTips <- length(TL) weight <- attr(data, "weight") nr <- attr(data, "nr") m <- nr*(2L*nTips - 1L) } } data <- prepareDataFitch(data) d = attributes(data) data <- as.integer(data) attributes(data) <- d if(inherits(tree,"phylo")) return(fit.fitch(tree, data, site)) else{ if(is.null(attr(tree,"TipLabel"))){ tree = unclass(tree) return(sapply(tree, fit.fitch, data, site)) } else{ tree = .uncompressTipLabel(tree) tree = unclass(tree) tree = lapply(tree, reorder, "postorder") site = ifelse(site == "pscore", 1L, 0L) on.exit(.C("fitch_free")) .C("fitch_init", as.integer(data), as.integer(nTips*nr), as.integer(m), as.double(weight), as.integer(nr)) return(sapply(tree, fast.fitch, nr, site)) } } } fit.fitch <- function (tree, data, returnData = c("pscore", "site", "data")) { if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") returnData <- match.arg(returnData) nr = attr(data, "nr") node <- tree$edge[, 1] edge <- tree$edge[, 2] weight = attr(data, "weight") m = max(tree$edge) q = length(tree$tip.label) result <- .Call("FITCH", data[, tree$tip.label], as.integer(nr), as.integer(node), as.integer(edge), as.integer(length(edge)), as.double(weight), as.integer(m), as.integer(q)) if (returnData == "site") return(result[[2]]) pscore <- result[[1]] res = pscore if (returnData == "data") res <- list(pscore = pscore, dat = result[[3]], site = result[[2]]) res } # NNI fnodesNew2 <- function (EDGE, nTips, nr) { node <- EDGE[, 1] edge <- EDGE[, 2] n = length(node) m= as.integer(max(EDGE)+1L) m2 = 2L*n root0 <- as.integer(node[n]) .Call("FNALL_NNI", as.integer(nr), node, edge, as.integer(n), as.integer(m), as.integer(m2), as.integer(root0)) } # SPR und bab kompakter fnodesNew5 <- function (EDGE, nTips, nr, m= as.integer(max(EDGE)+1L)) { node <- EDGE[, 1] edge <- EDGE[, 2] n = length(node) # in C m2 = 2L*n # in C root0 <- as.integer(node[n]) # in C .Call("FNALL5", as.integer(nr), node, edge, as.integer(n), as.integer(m), as.integer(m2), as.integer(root0), PACKAGE="phangorn") } random.addition <- function(data, method="fitch") { label <- names(data) nTips <- as.integer(length(label)) remaining <- as.integer(sample(nTips)) tree <- structure(list(edge = structure(c(rep(nTips+1L, 3), remaining[1:3]), .Dim = c(3L, 2L)), tip.label = label, Nnode = 1L), .Names = c("edge", "tip.label", "Nnode"), class = "phylo", order = "postorder") remaining <- remaining[-c(1:3)] if(nTips==3L) return(tree) nr <- attr(data, "nr") storage.mode(nr) <- "integer" n <- length(data) #- 1L data <- subset(data, ,order(attr(data, "weight"), decreasing=TRUE)) data <- prepareDataFitch(data) weight <- attr(data, "weight") m = nr*(2L*nTips - 2L) on.exit(.C("fitch_free")) .C("fitch_init", as.integer(data), as.integer(nTips*nr), as.integer(m), as.double(weight), as.integer(nr)) storage.mode(weight) <- "double" for (i in remaining) { edge = tree$edge[,2] score = fnodesNew5(tree$edge, nTips, nr)[edge] score <- .Call("FITCHTRIP3", as.integer(i), as.integer(nr), as.integer(edge), as.double(score), as.double(Inf)) res = min(score) nt = which.min(score) tree = addOne(tree, i, nt) } attr(tree, "pscore") = res tree } fast.fitch <- function (tree, nr, ps = TRUE) { node <- tree$edge[, 1] edge <- tree$edge[, 2] m = max(tree$edge) .Call("FITCH345", as.integer(nr), as.integer(node), as.integer(edge), as.integer(length(edge)), as.integer(m), as.integer(ps)) } fitch.spr <- function(tree, data){ nTips = as.integer(length(tree$tip.label)) nr = attr(data, "nr") minp = fast.fitch(tree, nr, TRUE) m=max(tree$edge) for(i in 1:nTips){ treetmp = dropTip(tree, i) edge = treetmp$edge[,2] # score = fnodesNew5(treetmp$edge, nTips, nr)[edge] score <- .Call("FNALL6", as.integer(nr), treetmp$edge[,1], edge, as.integer(m+1L), PACKAGE="phangorn")[edge] score <- .Call("FITCHTRIP3", as.integer(i), as.integer(nr), as.integer(edge), as.double(score), as.double(minp)) if(min(score)= p0) candidates[ind] = FALSE if (test < p0) { p0 <- test swap = swap + 1 tree <- tree2 indi <- which(INDEX[5,] %in% INDEX[1:5, ind]) candidates[indi] <- FALSE pscore[indi] <- Inf } } list(tree = tree, pscore = p0, swap = swap) } optim.fitch <- function(tree, data, trace=1, rearrangements = "SPR", ...) { if(!inherits(tree,"phylo")) stop("tree must be of class phylo") if(!is.binary.tree(tree)){ tree <- multi2di(tree) attr(tree, "order") <- NULL } if(is.rooted(tree)){ tree <- unroot(tree) attr(tree, "order") <- NULL } if(is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") if (class(data)[1] != "phyDat") stop("data must be of class phyDat") rt = FALSE nTips = as.integer(length(tree$tip.label)) nr = attr(data, "nr") pis <- parsinfo(data) p0 <- sum(attr(data, "weight")[pis[, 1]] * pis[, 2]) if (length(pis) > 0) data <- getRows(data, c(1:nr)[-pis[, 1]], TRUE) nr = attr(data, "nr") data <- subset(data,tree$tip.label,order(attr(data, "weight"), decreasing=TRUE)) dat <- prepareDataFitch(data) weight <- attr(data, "weight") m = nr*(2L*nTips - 2L) on.exit(.C("fitch_free")) .C("fitch_init", as.integer(dat), as.integer(nTips*nr), as.integer(m), as.double(weight), as.integer(nr)) tree$edge.length=NULL swap = 0 iter = TRUE pscore <- fast.fitch(tree, nr) while (iter) { res <- fitch.nni(tree, dat, ...) tree <- res$tree if(trace>1)cat("optimize topology: ", pscore + p0, "-->", res$pscore + p0, "\n") pscore = res$pscore swap = swap + res$swap if (res$swap == 0){ if(rearrangements=="SPR"){ tree <- fitch.spr(tree, dat) psc <- fast.fitch(tree, nr) if(trace>1)cat("optimize topology (SPR): ", pscore + p0 , "-->", psc + p0, "\n") if(pscore < psc+1e-6) iter=FALSE pscore <- psc } else iter = FALSE } } if(trace>0)cat("Final p-score",pscore + p0,"after ",swap, "nni operations \n") if(rt)tree <- ptree(tree, data) attr(tree, "pscore") = pscore + p0 tree } # branch and bound getOrder <- function (x) { label = names(x) dm = as.matrix(dist.hamming(x, FALSE)) ind = as.vector(which(dm == max(dm), arr.ind = TRUE)[1, ]) nTips = as.integer(length(label)) added = ind remaining <- c(1:nTips)[-ind] tree <- structure(list(edge = structure(c(rep(nTips+1L, 3), c(ind, 0L)), .Dim = c(3L, 2L)), tip.label = label, Nnode = 1L), .Names = c("edge", "tip.label", "Nnode"), class = "phylo", order = "postorder") l = length(remaining) res = numeric(l) nr <- attr(x, "nr") storage.mode(nr) <- "integer" n <- length(x) #- 1L data <- prepareDataFitch(x) weight <- attr(data, "weight") storage.mode(weight) <- "double" m = nr*(2L*nTips - 2L) on.exit(.C("fitch_free")) .C("fitch_init", as.integer(data), as.integer(nTips*nr), as.integer(m), as.double(weight), as.integer(nr)) for(i in 1:length(remaining)){ tree$edge[3,2]= remaining[i] res[i] = fast.fitch(tree, nr) } tmp = which.max(res) added = c(added, remaining[tmp]) remaining <- remaining[-tmp] tree$edge[,2]= added for (i in 4:(nTips - 1L)) { edge = tree$edge[,2] score0 = fnodesNew5(tree$edge, nTips, nr)[edge] l = length(remaining) res = numeric(l) nt = numeric(l) k = length(added)+1L for(j in 1:l){ score <- .Call("FITCHTRIP3", as.integer(remaining[j]), as.integer(nr), as.integer(edge), as.double(score0), as.double(Inf)) # score = score0[edge] + psc res[j] = min(score) nt[j] = which.min(score) } tmp = which.max(res) added = c(added, remaining[tmp]) tree = addOne(tree, remaining[tmp], nt[tmp]) remaining <- remaining[-tmp] } added = c(added, remaining) added } bab_old <- function (data, tree = NULL, trace = 1, ...) { o = order(attr(data, "weight"), decreasing = TRUE) data = subset(data, , o) nr <- attr(data, "nr") pis <- parsinfo(data) p0 <- sum(attr(data, "weight")[pis[, 1]] * pis[, 2]) if (length(pis) > 0) data <- getRows(data, c(1:nr)[-pis[, 1]], TRUE) tree <- pratchet(data, start = tree, trace = trace - 1, ...) data <- subset(data, tree$tip.label) nr <- as.integer(attr(data, "nr")) inord <- getOrder(data) lb <- lowerBound(data) nTips <- m <- length(data) nr <- as.integer(attr(data, "nr")) TMP <- matrix(0, m, nr) for (i in 4:m) { TMP[i, ] = lowerBound(subset(data, inord[1:i])) } weight <- as.double(attr(data, "weight")) data <- prepareDataFitch(data) m = nr*(2L*nTips - 2L) on.exit(.C("fitch_free")) .C("fitch_init", as.integer(data), as.integer(nTips*nr), as.integer(m), as.double(weight), as.integer(nr)) mmsAmb = 0 mmsAmb = TMP %*% weight mmsAmb = mmsAmb[nTips] - mmsAmb mms0 = 0 mms0 = mms0 + mmsAmb minPars = mms0[1] kPars = 0 if (trace) print(paste("lower bound:", p0 + mms0[1])) bound <- fast.fitch(tree, nr) if (trace) print(paste("upper bound:", bound + p0)) startTree <- structure(list(edge = structure(c(rep(nTips+1L, 3), as.integer(inord)[1:3]), .Dim = c(3L, 2L)), tip.label = tree$tip.label, Nnode = 1L), .Names = c("edge", "tip.label", "Nnode"), class = "phylo", order = "postorder") trees <- vector("list", nTips) trees[[3]] <- list(startTree$edge) for(i in 4:nTips) trees[[i]] <- vector("list", (2L*i) - 5L) # new # index M[i] is neues node fuer edge i+1 # index L[i] is length(node) tree mit i+1 L = as.integer( 2L*(1L:nTips) -3L ) M = as.integer( 1L:nTips + nTips - 1L ) PSC <- matrix(c(3,1,0), 1, 3) PSC[1,3] <- fast.fitch(startTree, nr) k = 4L Nnode = 1L npsc = 1 result <- list() while (npsc > 0) { a = PSC[npsc,1] b = PSC[npsc,2] PSC = PSC[-npsc,, drop=FALSE] tmpTree <- trees[[a]][[b]] edge = tmpTree[,2] score = fnodesNew5(tmpTree, nTips, nr, M[a])[edge] + mms0[a+1L] score <- .Call("FITCHTRIP3", as.integer(inord[a+1L]), as.integer(nr), as.integer(edge), as.double(score), as.double(bound), PACKAGE="phangorn") ms = min(score) if(ms<=bound){ if((a+1L) 0) data <- getRows(data, c(1:nr)[-pis[, 1]], TRUE) # added compressSites data <- compressSites(data) tree <- pratchet(data, start = tree, trace = trace - 1, ...) data <- subset(data, tree$tip.label) nr <- as.integer(attr(data, "nr")) inord <- getOrder(data) lb <- lowerBound(data) nTips <- m <- length(data) nr <- as.integer(attr(data, "nr")) TMP <- matrix(0, m, nr) for (i in 4:m) { TMP[i, ] = lowerBound(subset(data, inord[1:i])) } weight <- as.double(attr(data, "weight")) data <- prepareDataFitch(data) m = nr*(2L*nTips - 2L) on.exit(.C("fitch_free")) .C("fitch_init", as.integer(data), as.integer(nTips*nr), as.integer(m), as.double(weight), as.integer(nr)) mmsAmb = 0 mmsAmb = TMP %*% weight mmsAmb = mmsAmb[nTips] - mmsAmb mms0 = 0 mms0 = mms0 + mmsAmb minPars = mms0[1] kPars = 0 if (trace) print(paste("lower bound:", p0 + mms0[1])) bound <- fast.fitch(tree, nr) if (trace) print(paste("upper bound:", bound + p0)) startTree <- structure(list(edge = structure(c(rep(nTips+1L, 3), as.integer(inord)[1:3]), .Dim = c(3L, 2L)), tip.label = tree$tip.label, Nnode = 1L), .Names = c("edge", "tip.label", "Nnode"), class = "phylo", order = "postorder") trees <- vector("list", nTips) trees[[3]] <- list(startTree$edge) for(i in 4:nTips) trees[[i]] <- vector("list", (2L*i) - 5L) # new # index M[i] is neues node fuer edge i+1 # index L[i] is length(node) tree mit i+1 L = as.integer( 2L*(1L:nTips) -3L ) M = as.integer( 1L:nTips + nTips - 1L ) # PSC = matrix(0, sum(sapply(trees, length)), 3) # PSC[1,] = c(3,1,0) PSC <- matrix(c(3,1,0), 1, 3) PSC[1,3] <- fast.fitch(startTree, nr) k = 4L Nnode = 1L npsc = 1 result <- list() while (npsc > 0) { a = PSC[npsc,1] b = PSC[npsc,2] PSC = PSC[-npsc,, drop=FALSE] tmpTree <- trees[[a]][[b]] edge = tmpTree[,2] score = fnodesNew5(tmpTree, nTips, nr, M[a])[edge] + mms0[a+1L] score <- .Call("FITCHTRIP3", as.integer(inord[a+1L]), as.integer(nr), as.integer(edge), as.double(score), as.double(bound), PACKAGE="phangorn") ms = min(score) if(ms<=bound){ if((a+1L)0]=0L res[!(tmp0>(tmp1 - 1e-8))] = 10000000L apply(res, 1, which.min) } comp2 <- function(x, y){ res = matrix(rowSums(x), nrow(x), nrow(y)) tmp1 = matrix(rowSums(y), nrow(x), nrow(y), byrow=TRUE) tmp3 = tcrossprod(1-x, y) tmp0 = tcrossprod(x, y) tmp0[tmp3>0]=0L res[tmp0<2] = Inf apply(res, 2, which.min) } # single linkage of minimal coalescent times # extends speciesTree fom ape coalSpeciesTree <- function(tree, X, sTree=NULL){ if(is.null(X))return(speciesTree(tree)) trees = unclass(tree) States = lapply(tree, ancstat, X) NH = lapply(tree, nodeHeight) if(is.null(sTree)){ l <- attr(X, "nc") m <- choose(l, 2) SST <- matrix(0L, m, l) k <- 1 for(i in 1:(l-1)){ for(j in (i+1):l){ SST[k, i] <- SST[k,j] <- 1L k <- k+1 } } Y=matrix(Inf, length(NH), nrow(SST)) dm = rep(Inf, m) for(i in 1:length(NH)){ ind = comp2(States[[i]],SST) dm = pmin(dm, NH[[i]][ind]) # for(j in 1:length(ind))Y[i, ind[j]] = min(Y[i, ind[j]], NH[[i]][j]) } dm = structure(2*dm, Labels = attr(X, "levels"), Size = l, class = "dist", Diag = FALSE, Upper = FALSE) sTree <- upgma(dm, "single") # dm of pairwise states } else{ SST = ancstat(sTree, X) Y=matrix(Inf, length(NH), nrow(SST)) for(i in 1:length(NH)){ ind = comp(States[[i]],SST) for(j in 1:length(ind))Y[i, ind[j]] = min(Y[i, ind[j]], NH[[i]][j]) } STH = apply(Y, 2, min) sTree$edge.length = STH[sTree$edge[,1]] - STH[sTree$edge[,2]] } sTree } phangorn/R/simSeq.R0000644000175100001440000000716412761653173013714 0ustar hornikusers # # add codon models, change to phyDat statt 3* # simSeq <- function (x, ...) UseMethod("simSeq") simSeq.phylo = function(x, l=1000, Q=NULL, bf=NULL, rootseq=NULL, type = "DNA", model="USER", levels = NULL, rate=1, ancestral=FALSE, ...){ pt <- match.arg(type, c("DNA", "AA", "USER", "CODON")) if (pt == "DNA") levels <- c("a", "c", "g", "t") if (pt == "AA") levels <- c("a", "r", "n", "d", "c", "q", "e", "g", "h", "i", "l", "k", "m", "f", "p", "s", "t", "w", "y", "v") if (pt == "CODON"){ levels <- c("aaa", "aac", "aag", "aat", "aca", "acc", "acg", "act", "aga", "agc", "agg", "agt", "ata", "atc", "atg", "att", "caa", "cac", "cag", "cat", "cca", "ccc", "ccg", "cct", "cga", "cgc", "cgg", "cgt", "cta", "ctc", "ctg", "ctt", "gaa", "gac", "gag", "gat", "gca", "gcc", "gcg", "gct", "gga", "ggc", "ggg", "ggt", "gta", "gtc", "gtg", "gtt", "tac", "tat", "tca", "tcc", "tcg", "tct", "tgc", "tgg", "tgt", "tta", "ttc", "ttg", "ttt") Q <- as.numeric(.syn > 0) } if (pt == "USER") if(is.null(levels))stop("levels have to be supplied if type is USER") lbf = length(levels) if (type == "AA" & !is.null(model)) { # model <- match.arg(model, c("USER", "WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24")) model <- match.arg(model, c("USER", .aamodels)) if(model!="USER")getModelAA(model, bf=is.null(bf), Q=is.null(Q)) } if(is.null(bf)) bf = rep(1/lbf,lbf) if(is.null(Q)) Q = rep(1,lbf*(lbf-1)/2) if(is.matrix(Q)) Q=Q[lower.tri(Q)] eig = edQt(Q, bf) m = length(levels) if(is.null(rootseq))rootseq = sample(levels, l, replace=TRUE, prob=bf) x = reorder(x) edge = x$edge nNodes = max(edge) res = matrix(NA, l, nNodes) parent <- as.integer(edge[, 1]) child <- as.integer(edge[, 2]) root <- as.integer(parent[!match(parent, child, 0)][1]) res[, root] = rootseq tl = x$edge.length for(i in 1:length(tl)){ from = parent[i] to = child[i] P = getP(tl[i], eig, rate)[[1]] for(j in 1:m){ ind = res[,from]==levels[j] res[ind,to] = sample(levels, sum(ind), replace=TRUE, prob=P[,j]) } } k = length(x$tip.label) label = c(x$tip.label, as.character((k+1):nNodes)) colnames(res)=label if(!ancestral)res = res[, x$tip.label, drop=FALSE] if(pt=="DNA") return(phyDat.DNA(as.data.frame(res, stringsAsFactors = FALSE), return.index=TRUE)) if(pt=="AA") return(phyDat.AA(as.data.frame(res, stringsAsFactors = FALSE), return.index=TRUE)) if(pt=="USER") return(phyDat.default(as.data.frame(res, stringsAsFactors = FALSE), levels = levels, return.index=TRUE)) if(pt=="CODON"){ res <- apply(res, 2, function(x)unlist(strsplit(x, ""))) return(phyDat.codon(as.data.frame(res, stringsAsFactors = FALSE))) } } simSeq.pml <- function(x, ancestral=FALSE, ...){ g = x$g w = x$w if(x$inv>0){ w = c(x$inv, w) g = c(0.0, g) } n = length(w) res = vector("list", n) y = sample(n, sum(x$weight), replace=TRUE, prob=w) levels = attr(x$data, "levels") type = attr(x$data, "type") for(i in 1:n){ l = sum(y==i) res[[i]] = simSeq(x$tree, l, Q=x$Q, bf=x$bf, type=type, levels=levels, rate=g[i], ancestral=ancestral) } x = call("c.phyDat", quote(res[[1]])) if(n>1) x <- parse(text= paste("c(", "res[[1]]", paste0(",res[[", 2:n, "]]", collapse=""), ")")) eval(x) } phangorn/R/treeManipulation.R0000644000175100001440000006173713012677210015765 0ustar hornikusers# # tree manipulation # # no checks for postorder getRoot <- function (tree) { if(!is.null(attr(tree, "order")) && attr(tree, "order") == "postorder"){ return(tree$edge[nrow(tree$edge), 1]) } res = unique(tree$edge[, 1][!match(tree$edge[, 1], tree$edge[, 2], 0)]) if (length(res) == 1) return(res) else stop("There are apparently two root edges in your tree") } # renames root node reroot <- function (tree, node) { anc = Ancestors(tree, node, "all") l = length(anc) if(is.na(match(node,tree$edge[,1])))stop("node not in tree") if(l==0)return(tree) ind = match(c(node, anc[-l]), tree$edge[, 2]) tree$edge[ind, c(1, 2)] = tree$edge[ind, c(2, 1)] root = anc[l] tree$edge[tree$edge == root] = 0L tree$edge[tree$edge == node] = root tree$edge[tree$edge == 0L] = node # needed for unrooted trees tree <- collapse.singles(tree) attr(tree, "order") <- NULL reorder(tree, "postorder") } reroot2 <- function(tree, node) { if(node==getRoot(tree)) return(tree) anc = Ancestors(tree, node, "all") l = length(anc) ind = match(c(node, anc[-l]), tree$edge[, 2]) tree$edge[ind, c(1, 2)] = tree$edge[ind, c(2, 1)] reorderPruning(tree) } changeEdge = function (tree, swap, edge = NULL, edge.length = NULL) { attr(tree, "order") = NULL child <- tree$edge[, 2] tmp = numeric(max(child)) tmp[child] = 1:length(child) tree$edge[tmp[swap[1]], 2] = swap[2] tree$edge[tmp[swap[2]], 2] = swap[1] if (!is.null(edge)) { tree$edge.length[tmp[edge]] = edge.length } reorder(tree, "postorder") } changeEdgeLength = function (tree, edge, edge.length) { tree$edge.length[match(edge, tree$edge[,2])] = edge.length tree } # O(n) statt O(n^2) Speicher und Geschwindigkeit midpoint <- function(tree, node.labels = "support"){ # distance from node to root node2root <- function(x){ x = reorder(x, "postorder") el = numeric(max(x$edge)) parents <- x$edge[, 1] child <- x$edge[, 2] el[child] = x$edge.length l = length(parents) res <- numeric(max(x$edge)) for(i in l:1){ res[child[i]] = el[child[i]] + res[parents[i]] } res } oldtree <- tree tree = unroot(tree) nTips = length(tree$tip.label) maxD1 = node2root(tree)[1:nTips] ind = which.max(maxD1) tmproot = Ancestors(tree, ind, "parent") nTips = length(tree$tip.label) if(tmproot>nTips) tree = root(tree, node=tmproot) else tree = root(tree, tmproot) el = numeric(max(tree$edge)) el[tree$edge[,2]]=tree$edge.length maxdm = el[ind] tree$edge.length[tree$edge[,2]==ind] = 0 maxD1 = node2root(tree)[1:nTips] tree$edge.length[tree$edge[,2]==ind] = maxdm ind = c(ind, which.max(maxD1) ) maxdm = maxdm + maxD1[ind[2]] rn = max(tree$edge)+1 edge = tree$edge el = tree$edge.length children = tree$edge[,2] left = match(ind[1], children) tmp = Ancestors(tree, ind[2], "all") tmp= c(ind[2], tmp[-length(tmp)]) right = match(tmp, children) if(el[left]>= (maxdm/2)){ edge = rbind(edge, c(rn, ind[1])) edge[left,2] = rn el[left] = el[left] - (maxdm/2) el = c(el, maxdm/2) } else{ sel = cumsum(el[right]) i = which(sel>(maxdm/2))[1] edge = rbind(edge, c(rn, tmp[i])) edge[right[i],2] = rn eltmp = sel[i] - (maxdm/2) el = c(el, el[right[i]] - eltmp) el[right[i]] = eltmp } tree$edge.length = el tree$edge=edge tree$Nnode = tree$Nnode+1 attr(tree, "order") <- NULL tree <- reroot(tree, rn) if(!is.null(tree$node.label)){ node.label = tree$node.label tmp = node.label[1] node.label[1] = node.label[rn-nTips] node.label[rn-nTips] = tmp node.label[is.na(node.label)] = "" tree$node.label = node.label } attr(tree, "order") <- NULL tree <- reorder(tree, "postorder") # tree <- reorder(reroot(tree, rn), "postorder") # if(!is.null(oldtree$node.label))tree <- addConfidences.phylo(tree, oldtree) if(!is.null(oldtree$node.label)){ type <- match.arg(node.labels, c("support", "label", "delete")) if(type=="support") tree <- addConfidences.phylo(tree, oldtree) if(type=="delete") tree$node.label <- NULL } tree } pruneTree = function(tree, ..., FUN = ">="){ if(is.null(tree$node)) stop("no node labels") if(is.rooted(tree)) tree = unroot(tree) m = max(tree$edge) nTips = length(tree$tip.label) bs = rep(TRUE, m) bs[ (nTips+1) : m] = sapply(as.numeric(as.character(tree$node)), FUN,...) tree$edge.length[!bs[tree$edge[,2]]] = 0 reorder(di2multi(tree), "postorder") } # requires postorder # works fine with fit.fitch # for internal use in fitch.spr # pos statt i dropTip <- function(x, i, check.binary=FALSE, check.root=TRUE){ edge <- x$edge root <- getRoot(x) ch <- which(edge[,2] == i) pa <- edge[ch,1] edge = edge[-ch,] ind <- which(edge[,1] == pa) if(root == pa){ if(length(ind)==1){ edge = edge[-ind,] x$Nnode=x$Nnode-1L } if(length(ind)==2){ n = dim(edge)[1] newroot = edge[n-2L,1] newedge = edge[ind,2] if(newedge[1]==newroot)edge[n-1,] <- newedge else edge[n-1,] <- newedge[2:1] edge = edge[-n,] x$Nnode=x$Nnode-1L edge[edge==newroot] = root pa <- newroot } # todo handle unrooted trees } else{ nind <- which(edge[,2] == pa) # normal binary case if(length(ind)==1){ edge[nind,2] = edge[ind,2] edge <- edge[-ind,] x$Nnode <- x$Nnode-1L } } # edge[edge>pa] = edge[edge>pa] -1L x$edge <- edge x } # like drop tip and returns two trees, # to be used in fitch.spr # ch = allKids(edge, nTips) descAll = function (x, node, nTips, ch) { edge = x[,1] m = max(x) isInternal = logical(m) isInternal[(nTips+1):m] = TRUE desc = function(node, isInternal) { if (!isInternal[node]) return(node) res = NULL while (length(node) > 0) { tmp = unlist(ch[node]) res = c(res, tmp) node = tmp[isInternal[tmp]] } res } desc(node, isInternal) } dropNode <- function(x, i, check.binary=FALSE, check.root=TRUE, all.ch=NULL){ edge <- x$edge root <- getRoot(x) ch <- which(edge[,2] == i) # getIndexEdge(tip, edge) nTips <- length(x$tip.label) pa <- edge[ch,1] if(i>nTips){ # kids <- Descendants(x, i, "all") if(is.null(all.ch)) all.ch=allChildren(x) kids <- descAll(edge, i, nTips, all.ch) ind <- match(kids,edge[,2]) edge2 <- edge[sort(ind),] edge <- edge[-c(ch, ind),] } else edge = edge[-ch,] if(nrow(edge)<3)return(NULL) ind <- which(edge[,1] == pa) if(root == pa){ if(length(ind)==1){ edge = edge[-ind,] x$Nnode=x$Nnode-1L } if(length(ind)==2){ n = dim(edge)[1] newroot = edge[n-2L,1] newedge = edge[ind,2] if(newedge[1]==newroot)edge[n-1,] <- newedge else edge[n-1,] <- newedge[2:1] edge = edge[-n,] x$Nnode=length(unique(edge[,1])) edge[edge==newroot] = root pa <- newroot } # todo handle unrooted trees } else{ nind <- which(edge[,2] == pa) # normal binary case if(length(ind)==1){ edge[nind,2] = edge[ind,2] edge <- edge[-ind,] x$Nnode <- length(unique(edge[,1])) } } # # edge[edge>pa] = edge[edge>pa] -1L x$edge <- edge y <- x y$edge <- edge2 y$Nnode <- length(unique(edge2[,1])) list(x, y, pa) } # postorder remained tip in 1:nTips addOne <- function (tree, tip, i){ edge = tree$edge parent = edge[,1] l = dim(edge)[1] m = max(edge)+1L p = edge[i,1] k = edge[i,2] edge[i, 2] = m ind = match(p, parent) if(ind==1) edge = rbind(matrix(c(m,m,k,tip), 2, 2), edge) else edge = rbind(edge[1:(ind-1), ], matrix(c(m,m,k,tip), 2, 2), edge[ind:l, ]) tree$edge = edge tree$Nnode = tree$Nnode+1 tree } addOneTree <- function (tree, subtree, i, node){ edge = tree$edge parent = edge[,1] l = dim(edge)[1] m = node #max(edge)+1L p = edge[i,1] k = edge[i,2] edge[i, 2] = m edge2 = subtree$edge ind = match(p, parent) r2 = edge2[nrow(edge2),1] if(ind==1) edge = rbind(edge2, matrix(c(m,m,r2,k), 2, 2), edge) else edge = rbind(edge[1:(ind-1), ], edge2, matrix(c(m,m,r2,k), 2, 2), edge[ind:l, ]) tree$edge = edge tree$Nnode = tree$Nnode + subtree$Nnode + 1L attr(tree, "order") = NULL tips1 = as.integer(length(tree$tip.label) + 1L) tmproot = getRoot(tree) if(tmproot!=tips1){ tree$edge[tree$edge==tmproot] = 0L tree$edge[tree$edge==tips1] = tmproot tree$edge[tree$edge==0L] = tips1 } tree <- reorder(tree, "postorder") if(tmproot!=tips1) tree <- unroot(tree) tree } reorderPruning <- function (x, ...) { parents <- as.integer(x$edge[, 1]) child <- as.integer(x$edge[, 2]) root <- as.integer(parents[!match(parents, child, 0)][1]) # unique out if (length(root) > 2) stop("more than 1 root found") n = length(parents) m = max(x$edge) # edge parents neworder = .C("C_reorder", parents, child, as.integer(n), as.integer(m), integer(n), as.integer(root-1L), PACKAGE = "phangorn")[[5]] x$edge = x$edge[neworder,] x$edge.length = x$edge.length[neworder] attr(x, "order") <- "pruningwise" x } add.tip <- function(phy, n, edgeLength=NULL, tip=""){ ind <- which(phy$edge[,2] == n) phy <- new2old.phylo(phy) edge <- matrix(as.numeric(phy$edge),ncol=2) k <- min(edge) l <- max(edge) phy$edge <- rbind(phy$edge, c(k-1,phy$edge[ind,2])) phy$edge <- rbind(phy$edge, c(k-1,l+1)) phy$edge[ind,2] = k-1 phy$edge.length[ind] = edgeLength[1] phy$edge.length <- c(phy$edge.length, edgeLength[-1]) phy$tip.label <- c(phy$tip.label, tip) phy <- old2new.phylo(phy) phy <- reorder(phy, "postorder") phy } nnin <- function (tree, n) { attr(tree, "order") = NULL tree1 = tree tree2 = tree edge = matrix(tree$edge, ncol = 2) parent = edge[, 1] child = tree$edge[, 2] k = min(parent) - 1 ind = which(child > k)[n] if(is.na(ind))return(NULL) p1 = parent[ind] p2 = child[ind] ind1 = which(parent == p1) ind1 = ind1[ind1 != ind][1] ind2 = which(parent == p2) e1 = child[ind1] e2 = child[ind2[1]] e3 = child[ind2[2]] tree1$edge[ind1, 2] = e2 tree1$edge[ind2[1], 2] = e1 tree2$edge[ind1, 2] = e3 tree2$edge[ind2[2], 2] = e1 if(!is.null(tree$edge.length)){ tree1$edge.length[c(ind1, ind2[1])] = tree$edge.length[c(ind2[1] ,ind1)] tree2$edge.length[c(ind1, ind2[2])] = tree$edge.length[c(ind2[2] ,ind1)] } tree1 <- reorder(tree1, "postorder") tree2 <- reorder(tree2, "postorder") # tree1$tip.label <- tree2$tip.label <- NULL result = list(tree1, tree2) result } nni <- function (tree) { tip.label <- tree$tip.label attr(tree, "order") = NULL k = min(tree$edge[, 1]) - 1 n = sum(tree$edge[, 2] > k) result = vector("list", 2*n) l=1 for (i in 1:n) { tmp = nnin(tree, i) tmp[[1]]$tip.label <- tmp[[2]]$tip.label <- NULL result[c(l, l+1)] = tmp l = l + 2 } attr(result, "TipLabel") <- tip.label class(result) <- "multiPhylo" result } allTrees <- function (n, rooted = FALSE, tip.label = NULL) { n <- as.integer(n) nt <- as.integer(round(dfactorial(2 * (n + rooted) - 5))) if ((n + rooted) > 10) { nt <- dfactorial(2 * (n + rooted) - 5) stop("That would generate ", round(nt), " trees, and take up more than ", round(nt/1000), " MB of memory!") } if (n < 2) { stop("A tree must have at least two taxa.") } if (!rooted && n == 2) { stop("An unrooted tree must have at least three taxa.") } if (rooted) { edge <- matrix(NA, 2*n-2, 2) edge[1:2,] <- c(n+1L, n+1L, 1L, 2L) } else { edge <- matrix(NA, 2*n-3, 2) edge[1:3,] <- c(n+1L, n+1L, n+1L, 1L, 2L, 3L) } edges <- list() edges[[1]] <- edge m=1 nedge = 1 trees <- vector("list", nt) if ((n + rooted) > 3) { i = 3L + (!rooted) pa = n + 2L nr = 2L + (!rooted) while(i < (n+1L)){ nedge = nedge+2 m2 = m * nedge newedges <- vector("list", m2) for (j in 1:m) { edge <- edges[[j]] l <- nr # nrow(edge) edgeA <- edge edgeB <- edge for (k in 1L:l) { edge = edgeA node <- edge[k, 1] edge[k, 1] <- pa edge[l + 1, ] <- c(pa, i) edge[l + 2, ] <- c(node, pa) newedges[[(j - 1) * (l + rooted) + k]] <- edge } if(rooted) { edgeB[] <- as.integer(sub(n+1L, pa, edgeB)) edge = edgeB edge[l + 1, ] <- c(n+1L, i) edge[l + 2, ] <- c(n+1L, pa) newedges[[j * (l + 1)]] <- edge } } # end for edges <- newedges m=m2 i = i + 1L pa = pa + 1L nr = nr + 2L } # end for m } # end if for (x in 1:m) { tree <- list(edge = edges[[x]]) tree$Nnode <- n - 2L + rooted class(tree) <- "phylo" trees[[x]] <- reorderPruning(tree) } attr(trees, "TipLabel") <- if (is.null(tip.label)) paste("t", 1:n, sep = "") else tip.label class(trees) <- "multiPhylo" trees } # replace with dist.nodes dn <- function(x){ # if (!is.binary.tree(x) ) x <- multi2di(x, random = FALSE) if(is.null(x$edge.length))x$edge.length=rep(1,nrow(x$edge)) else x$edge.length[] = 1 dist.nodes(x) } rSPR = function (tree, moves = 1, n = length(moves), k=NULL) { if (n == 1) { trees = tree for (i in 1:moves) trees = kSPR(trees, k=k) } else { trees = vector("list", n) if(length(moves)==1) moves = rep(moves, n) for (j in 1:n) { tmp = tree if(moves[j]>0){ for (i in 1:moves[j]) tmp = kSPR(tmp, k=k) } tmp$tip.label = NULL trees[[j]] = tmp } attr(trees, "TipLabel") <- tree$tip.label class(trees) <- "multiPhylo" } trees } kSPR = function(tree, k=NULL){ l <- length(tree$tip.label) root= getRoot(tree) distN = dn(tree)[-c(1:l), -c(1:l)] distN[upper.tri(distN)]=Inf dN = distN[lower.tri(distN)] tab = tabulate(dN) # should be much faster # tab = table(dN) tab[1] = tab[1] * 2 tab[-1] = tab[-1] * 8 if(is.null(k)) k = 1:length(tab) k = na.omit((1:length(tab))[k]) if(length(k)>1)k = sample((1:length(tab))[k], 1, prob=tab[k] / sum(tab[k]) ) if(k==1) return(rNNI(tree, 1, 1)) index = which(distN==k, arr.ind=TRUE) + l m = dim(index)[1] if(m==0)stop("k is chosen too big") ind = index[sample(m, 1),] s1 = sample(c(1,2),1) if(s1==1)res = (oneOf4(tree, ind[1], ind[2], sample(c(1,2),1), sample(c(1,2),1))) if(s1==2)res = (oneOf4(tree, ind[2], ind[1], sample(c(1,2),1), sample(c(1,2),1))) res=reroot2(res, root) reorderPruning(res) } oneOf4 = function(tree, ind1, ind2, from=1, to=1){ if (!is.binary.tree(tree)) stop("Sorry, trees must be binary!") tree=reroot2(tree, ind2) trees = vector('list', 8) kids1 = Children(tree, ind1) anc = Ancestors(tree, ind1, "all") l = length(anc) kids2 = Children(tree, ind2) kids2 = kids2[kids2!=anc[l-1]] child = tree$edge[,2] tmp = numeric(max(tree$edge)) tmp[child] = 1:length(child) edge = tree$edge edge[tmp[kids1[-from]],1] = Ancestors(tree, ind1, "parent") edge[tmp[kids2[to]],1] = ind1 edge[tmp[ind1]] = ind2 tree$edge=edge reorderPruning(tree) } # faster than kSPR rSPR_Old <- function(tree, moves=1, n=1){ k=length(tree$edge[,1]) if(n==1){ trees = tree for(i in 1:moves) trees = sprMove(trees,sample(k,1)) } else{ trees = vector("list", n) for(j in 1:n){ tmp = tree for(i in 1:moves) tmp = sprMove(tmp,sample(k,1)) tmp$tip.label=NULL trees[[j]] = tmp } attr(trees, "TipLabel") <- tree$tip.label class(trees) <- "multiPhylo" } trees } sprMove <- function(tree, m){ if (is.rooted(tree)) tree <- unroot(tree) #stop("Sorry trees must be unrooted") if(!is.binary.tree(tree))stop("Sorry trees must be binary!") reroot2 <- function(tree, node){ anc = Ancestors(tree, node, "all") l = length(anc) ind = match(c(node, anc[-l]), tree$edge[,2]) tree$edge[ind,c(1,2)] = tree$edge[ind,c(2,1)] tree } changeEdge <- function(tree, new, old){ tree$edge[tree$edge==old] = 0L tree$edge[tree$edge==new] = old tree$edge[tree$edge==0L] = new # needed for unrooted trees tree <- collapse.singles(tree) tree } edge = tree$edge k = max(edge) nTips = length(tree$tip.label) nEdges = 2*nTips-3 if(m > nEdges) stop("m to big") parent = edge[,1] child = edge[,2] pv = integer(k) pv[child] = parent cv = list() for(i in unique(parent)) cv[[i]] = child[parent==i] bp = bip(tree) root <- parent[!match(parent, child, 0)][1] ch = child[m] pa = parent[m] candidates = !logical(k) candidates[root] = FALSE candidates[cv[[ch]]] = FALSE candidates[cv[[pa]]] = FALSE candidates[pv[pa]] = FALSE candidates[pa] = FALSE ind = which(candidates) l = sample(ind,1) cr=FALSE if(!any(is.na(match(bp[[l]], bp[[ch]]))) ){ newroot = cv[[ch]] #[ 1] newroot = newroot[newroot>nTips][1] tree <- reroot2(tree, newroot) edge = tree$edge parent = tree$edge[,1] child = tree$edge[,2] pv = integer(k) pv[child] = parent cv = list() for(i in unique(parent)) cv[[i]] = child[parent==i] tmp = pa pa=ch ch=tmp cr = TRUE } if(pa==root){ cp = cv[[pa]] newroot = cp[cp!=ch] newroot = newroot[newroot>nTips][1] # if(length(newroot)==0)browser() #!newroot = cp[cp>nTips][1] tree = reroot2(tree, newroot) edge = tree$edge parent = tree$edge[,1] child = tree$edge[,2] pv = integer(k) pv[child] = parent cv = list() for(i in unique(parent)) cv[[i]] = child[parent==i] cr = TRUE } el = tree$edge.length cp = cv[[pa]] sib = cp[cp!=ch] edge[child==l,1] = pa edge[child==pa,1] = pv[l] edge[child==sib,1] = pv[pa] el[child==sib] = el[child==sib] + el[child==pa] el[child==l] = el[child==l] / 2 el[child==pa] = el[child==l] tree$edge=edge tree$edge.length = el if(cr) tree <- changeEdge(tree,root,newroot) tree <- reorder(tree, "postorder") tree } rNNI <- function (tree, moves = 1, n = length(moves)) { k = length(na.omit(match(tree$edge[, 2], tree$edge[, 1]))) if (n == 1) { trees = tree if(moves>0){ for (i in 1:moves) trees = nnin(trees, sample(k, 1))[[sample(2,1)]] } trees$tip.label <- tree$tip.label } else { trees = vector("list", n) if(length(moves)==1) moves = rep(moves, n) for (j in 1:n) { tmp = tree if(moves[j]>0){ for (i in 1:moves[j]) tmp = nnin(tmp, sample(k, 1))[[sample(2,1)]] } tmp$tip.label = NULL trees[[j]] = tmp } attr(trees, "TipLabel") <- tree$tip.label class(trees) <- "multiPhylo" } trees } # # some generic tree functions # allAncestors <- function(x){ x = reorder(x, "postorder") parents <- x$edge[, 1] child <- x$edge[, 2] l = length(parents) res <- vector("list",max(x$edge)) for(i in l:1){ pa = parents[i] res[[child[i]]] = c(pa, res[[pa]]) } res } Ancestors <- function (x, node, type = c("all", "parent")) { parents <- x$edge[, 1] child <- x$edge[, 2] pvector <- integer(max(x$edge)) # parents pvector[child] <- parents type <- match.arg(type) if (type == "parent") return(pvector[node]) anc <- function(pvector, node){ res <- numeric(0) repeat { anc <- pvector[node] if (anc == 0) break res <- c(res, anc) node <- anc } res } if(length(node)==1) return(anc(pvector, node)) else allAncestors(x)[node] } #allChildren <- function(x){ # l = length(x$tip.label) # if(l<20){ # parent = x$edge[,1] # children = x$edge[,2] # res = vector("list", max(x$edge)) # for(i in 1:length(parent)) res[[parent[i]]] = c(res[[parent[i]]], children[i]) # return(res) # } # else{ # if (is.null(attr(x, "order")) || attr(x, "order") == "cladewise") # x <- reorder(x, "postorder") # parent = x$edge[,1] # children = x$edge[,2] # res <- .Call("AllChildren", as.integer(children), as.integer(parent), as.integer(max(x$edge))) # , PACKAGE="phangorn" # return(res) # } #} # alternative version using Rcpp allChildren <- function(x){ l = length(x$tip.label) if(l<20){ parent = x$edge[,1] children = x$edge[,2] res = vector("list", max(x$edge)) for(i in 1:length(parent)) res[[parent[i]]] = c(res[[parent[i]]], children[i]) return(res) } else{ if (is.null(attr(x, "order")) || attr(x, "order") == "cladewise") x <- reorder(x, "postorder") allChildrenCPP(x$edge) } } allDescendants <- function(x){ if (is.null(attr(x, "order")) || attr(x, "order") == "cladewise") x <- reorder(x, "postorder") nTips <- as.integer(length(x$tip.label)) allDescCPP(x$edge, nTips) } Children <- function(x, node){ if(length(node)==1)return(x$edge[x$edge[,1]==node,2]) allChildren(x)[node] } Descendants = function(x, node, type=c("tips","children","all")){ type <- match.arg(type) if(type=="children") return(Children(x, node)) if(type=="tips") return(bip(x)[node]) # new version using Rcpp if(length(node)>10) return(allDescendants(x)[node]) ch = allChildren(x) # out of the loop isInternal = logical(max(x$edge)) isInternal[ unique(x$edge[,1]) ] =TRUE desc = function(node, isInternal){ if(!isInternal[node])return(node) res = NULL while(length(node)>0){ tmp = unlist(ch[node]) res = c(res, tmp) node = tmp[isInternal[tmp]] } res } if(length(node)>1) return(lapply(node, desc, isInternal)) desc(node, isInternal) } Siblings = function (x, node, include.self = FALSE) { l = length(node) if(l==1){ v <- Children(x, Ancestors(x, node, "parent")) if (!include.self) v <- v[v != node] return(v) } else{ parents <- x$edge[, 1] child <- x$edge[, 2] pvector <- integer(max(x$edge)) # parents pvector[child] <- parents root <- as.integer(parents[!match(parents, child, 0)][1]) res = vector("list", l) ch = allChildren(x) if(include.self) return(ch[ pvector[node] ]) k = 1 for(i in node){ if(i != root){ tmp <- ch[[ pvector[i] ]] res[[k]] = tmp[tmp != i] } k=k+1 } } res } mrca.phylo <- function(x, node=NULL){ if(is.null(node)) return(mrca2(x)) anc <- Ancestors(x, node, type = "all") res <- Reduce(intersect, anc)[1] res } # should be in ape mrca2 <- function(phy, full=FALSE){ Nnode <- phy$Nnode Ntips <- length(phy$tip.label) phy <- reorder(phy) nodes <- unique(phy$edge[,1]) if(!full){ res <- Descendants(phy, nodes, "tips") M <- matrix(nodes[1], Ntips, Ntips) for(i in 2:Nnode)M[res[[i]], res[[i]]]<-nodes[i] diag(M) <- 1:Ntips dimnames(M) <- list(phy$tip.label, phy$tip.label) } else{ res <- Descendants(phy, nodes, "all") M <- matrix(nodes[1], Ntips+Nnode, Ntips+Nnode) for(i in 2:Nnode){ tmp = c(res[[i]], nodes[i]) M[tmp, tmp]<-nodes[i] } diag(M) <- 1:(Ntips+Nnode) dimnames(M) <- list(1:(Ntips+Nnode),1:(Ntips+Nnode)) } M } # mrca.phylo <- getMRCA phangorn/R/dist.p.R0000644000175100001440000000461412761653173013651 0ustar hornikusers dist.p <- function (x, cost="polymorphism", ignore.indels=TRUE) { if (!inherits(x,"phyDat")) stop("x has to be element of class phyDat") l = length(x) weight <- attr(x, "weight") n <- length(attr(x, "allLevels")) d = numeric((l * (l - 1))/2) lev = attr(x, "allLevels") if(is.null(cost)){ cost <- 1 - diag(n) dimnames(cost) = list(lev, lev) } # if(cost=="polymorphism" && attr(x, "type")=="DNA"){ if(cost=="polymorphism"){ costLev = c('a','c','t','u','g','x','m','r','w','s','y','k','v','h','d','b','-','?','n') cost <- matrix(c( #a,c,t,u,g,X,m,r,w,s,y,k,v,h,d,b,-,?,n, 0,2,2,2,2,1,1,1,1,3,3,3,2,2,2,4,2,0,0, #a 2,0,2,2,2,1,1,3,3,1,1,3,2,2,4,2,2,0,0, #c 2,2,0,0,2,1,3,3,1,3,1,1,4,2,2,2,2,0,0, #t 2,2,0,0,2,1,3,3,1,3,1,1,4,2,2,2,2,0,0, #u 2,2,2,2,0,1,3,1,3,1,3,1,2,4,2,2,2,0,0, #g 1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0, #X 1,1,3,3,3,1,0,2,2,2,2,4,1,1,3,3,3,0,0, #m 1,3,3,3,1,1,2,0,2,2,4,2,1,3,1,3,3,0,0, #r 1,3,1,1,3,1,2,2,0,4,2,2,3,1,1,3,3,0,0, #w 3,1,3,3,1,1,2,2,4,0,2,2,1,3,3,1,3,0,0, #s 3,1,1,1,3,1,2,4,2,2,0,2,3,1,3,1,3,0,0, #y 3,3,1,1,1,1,4,2,2,2,2,0,3,3,1,1,3,0,0, #k 2,2,4,4,2,1,1,1,3,1,3,3,0,2,2,2,4,0,0, #v 2,2,2,2,4,1,1,3,1,3,1,3,2,0,2,2,4,0,0, #h 2,4,2,2,2,1,3,1,1,3,3,1,2,2,0,2,4,0,0, #d 4,2,2,2,2,1,3,3,3,1,1,1,2,2,2,0,4,0,0, #b 2,2,2,2,2,1,3,3,3,3,3,3,4,4,4,4,0,0,0, #- 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, #? 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),#n ncol = 19,nrow=19,dimnames=list(costLev,costLev)) } lev1 = dimnames(cost)[[1]] if(any(is.na(match(lev, lev1)))) stop("Levels of x are not in levels of cost matrix!") if (ignore.indels) { cost["-",]=0 cost[,"-"]=0 } cost <- cost[lev, lev] k = 1 for (i in 1:(l - 1)) { for (j in (i + 1):l) { d[k] = sum(weight * cost[cbind(x[[i]], x[[j]])]) k = k + 1 } } attr(d, "Size") <- l if (is.list(x)) attr(d, "Labels") <- names(x) else attr(d, "Labels") <- colnames(x) attr(d, "Diag") <- FALSE attr(d, "Upper") <- FALSE attr(d, "call") <- match.call() attr(d, "method") <- "p" class(d) <- "dist" return(d) } phangorn/R/zzz.R0000644000175100001440000000110113005450715013256 0ustar hornikusers## zzz.R .packageName <- "phangorn" .aamodels <- c("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU","Blosum62","Dayhoff_DCMut","JTT_DCMut") .dnamodels <- c("JC", "F81", "K80", "HKY", "TrNe", "TrN", "TPM1", "K81", "TPM1u", "TPM2", "TPM2u", "TPM3", "TPM3u", "TIM1e", "TIM1", "TIM2e", "TIM2", "TIM3e", "TIM3", "TVMe", "TVM", "SYM", "GTR") # if g[i] is smaller .gEps inv is increased w[i] .gEps <- 1e-30 # .onLoad <- function(libname, pkgname) { # library.dynam("phangorn", pkgname, libname) #} phangorn/R/parsimony.R0000644000175100001440000006205113020624742014455 0ustar hornikusers# # Maximum Parsimony # rowMin = function(X){ d=dim(X) .Call("C_rowMin", X, as.integer(d[1]), as.integer(d[2]), PACKAGE = "phangorn") } sankoff.quartet <- function (dat, cost, p, l, weight) { erg <- .Call("sankoffQuartet", sdat = dat, sn = p, scost = cost, sk = l, PACKAGE = "phangorn") sum(weight * erg) } parsimony <- function(tree, data, method='fitch', ...){ if (class(data)[1] != "phyDat") stop("data must be of class phyDat") if(method=='sankoff') result <- sankoff(tree, data, ...) if(method=='fitch') result <- fitch(tree, data, ...) result } ancestral.pars <- function (tree, data, type = c("MPR", "ACCTRAN"), cost=NULL) { call <- match.call() type <- match.arg(type) if (type == "ACCTRAN") res = ptree(tree, data, retData = TRUE)[[2]] # if (type == "MPR") # res = mpr(tree, data) if (type == "MPR"){ res <- mpr(tree, data, cost=cost) attr(res, "call") = call return(res) } l = length(tree$tip.label) x = attributes(data) m = dim(res)[2] label = as.character(1:m) nam = tree$tip.label label[1:length(nam)] = nam x[["names"]] = label nc = attr(data, "nc") result = vector("list", m) Z = unique(as.vector(res)) tmp = t(sapply(Z, function(x)dec2bin(x, nc))) tmp = tmp / rowSums(tmp) # rownames(tmp) = Z dimnames(tmp) = list(Z, attr(data, "levels")) for(i in 1:m){ # tmp = t(sapply(res[,i], function(x, k=4)dec2bin(x, nc))) # result[[i]] = tmp / rowSums(tmp) no indices # test = match(res[,i], Z) sollte stimmen wegen fitch result[[i]] = tmp[as.character(res[,i]),,drop=FALSE] rownames(result[[i]]) = NULL } attributes(result) = x attr(result, "call") <- call result } pace <- ancestral.pars mpr.help = function (tree, data, cost=NULL) { tree<- reorder(tree, "postorder") if (!inherits(data,"phyDat")) stop("data must be of class phyDat") levels <- attr(data, "levels") l = length(levels) if (is.null(cost)) { cost <- matrix(1, l, l) cost <- cost - diag(l) } weight = attr(data, "weight") p = attr(data, "nr") kl = TRUE i = 1 dat <- prepareDataSankoff(data) for (i in 1:length(dat)) storage.mode(dat[[i]]) = "double" tmp = fit.sankoff(tree, dat, cost, returnData='data') p0 = tmp[[1]] datf = tmp[[2]] datp = pnodes(tree, datf, cost) nr = attr(data, "nr") nc = attr(data, "nc") node <- tree$edge[, 1] edge <- tree$edge[, 2] node = as.integer(node - 1) edge = as.integer(edge - 1) res <- .Call("sankoffMPR", datf, datp, as.numeric(cost), as.integer(nr),as.integer(nc), node, edge, PACKAGE="phangorn") root = getRoot(tree) res[[root]] <- datf[[root]] res } mpr <- function(tree, data, cost=NULL){ data = subset(data, tree$tip.label) att = attributes(data) nr = att$nr nc = att$nc res <- mpr.help(tree,data,cost) l = length(tree$tip.label) m = length(res) label = as.character(1:m) nam = tree$tip.label label[1:length(nam)] = nam att[["names"]] = label ntips = length(tree$tip.label) contrast = att$contrast eps=5e-6 rm = apply(res[[ntips+1]], 1, min) RM = matrix(rm,nr, nc) + eps for(i in 1:ntips) res[[i]] = contrast[data[[i]],,drop=FALSE] for(i in (ntips+1):m) res[[i]][] = as.numeric(res[[i]] < RM) fun = function(X){ rs = apply(X, 1, sum) X / rs } res <- lapply(res, fun) attributes(res) = att res } plotAnc <- function (tree, data, i = 1, col=NULL, cex.pie=par("cex"), pos="bottomright", ...) { y = subset(data, , i) # args <- list(...) # CEX <- if ("cex" %in% names(args)) # args$cex # else par("cex") CEX = cex.pie xrad <- CEX * diff(par("usr")[1:2])/50 levels = attr(data, "levels") nc = attr(data, "nc") y = matrix(unlist(y[]), ncol = nc, byrow = TRUE) l = dim(y)[1] dat = matrix(0, l, nc) for (i in 1:l) dat[i, ] = y[[i]] plot(tree, label.offset = 1.1 * xrad, plot = FALSE, ...) lastPP <- get("last_plot.phylo", envir = .PlotPhyloEnv) XX <- lastPP$xx YY <- lastPP$yy xrad <- CEX * diff(lastPP$x.lim * 1.1)/50 par(new = TRUE) plot(tree, label.offset = 1.1 * xrad, plot = TRUE, ...) if(is.null(col)) col = rainbow(nc) if(length(col)!=nc) warning("Length of color vector differs from number of levels!") BOTHlabels(pie = y, XX = XX, YY = YY, adj = c(0.5, 0.5), frame = "rect", pch = NULL, sel = 1:length(XX), thermo = NULL, piecol = col, col = "black", bg = "lightblue", horiz = FALSE, width = NULL, height = NULL, cex=cex.pie) legend(pos, levels, text.col = col) } prepareDataFitch <- function (data) { lev <- attr(data, "levels") l <- length(lev) nr <- attr(data, "nr") nc <- length(data) contrast <- attr(data, "contrast") tmp = contrast %*% 2L^c(0L:(l - 1L)) tmp = as.integer(tmp) attrData <- attributes(data) nam <- attrData$names attrData$names <- NULL data = unlist(data, FALSE, FALSE) X = tmp[data] attributes(X) <- attrData attr(X, "dim") <- c(nr, nc) dimnames(X) <- list(NULL, nam) X } compressSites <- function(data){ attrData <- attributes(data) lev <- attr(data, "levels") LEV <- attr(data,"allLevels") l <- length(lev) nr <- attr(data, "nr") nc <- length(data) data = unlist(data, FALSE, FALSE) attr(data, "dim") <- c(nr, nc) uni <- match(lev, LEV) fun = function(x, uni) { u = unique.default(x) res= if(any(is.na(match(u, uni)))) return(x) match(x, u) } data = t(apply(data, 1, fun, uni)) ddd = fast.table(data) data = ddd$data class(data) = "list" attrData$weight = tapply(attrData$weight,ddd$index, sum) attrData$index=NULL attrData$nr <- length(attrData$weight) attrData$compressed <- TRUE attributes(data) <- attrData data } # # Branch and bound # parsinfo <- function (x) { low = lowerBound(x) up = upperBound(x) ind = which(low==up) cbind(ind, low[ind]) } lowerBound <- function(x, cost=NULL){ tip <- names(x) att = attributes(x) nc = attr(x, "nc") nr = attr(x, "nr") contrast = attr(x, "contrast") rownames(contrast) = attr(x, "allLevels") colnames(contrast) = attr(x, "levels") attr(x, "weight") = rep(1, nr) attr(x, "index") = NULL y <- as.character(x) states <- apply(y, 2, unique.default) # duplicated function(x)x[duplicated(x)]="?" avoids looping if(nr==1) nst <- length(states) else{ if(is.matrix(states)){ states = as.data.frame(states, stringsAsFactors=FALSE) class(states) = "list" } nst <- sapply(states, length) } res = numeric(nr) ust = sort(unique(nst)) if(is.null(cost))cost <- 1 - diag(nc) if(any(ust>1)){ ust = ust[ust>1] m <- max(ust) tips = paste("t", 1:m, sep="") # for(i in ust){ dat = matrix(unlist(states[nst==i]), nrow=i, dimnames=list(tips[1:i], NULL)) dat = phyDat(dat, type="USER", contrast=contrast) tree = stree(i) res[nst==i] = sankoffNew(tree, dat, cost=cost, site="site")[attr(dat, "index")] } } res } upperBound <- function(x, cost=NULL){ tree = stree(length(x), tip.label=names(x)) if(is.null(cost))cost <- 1 - diag(attr(x, "nc")) sankoffNew(tree, x, cost=cost, site="site") } CI <- function (tree, data, cost = NULL, sitewise=FALSE) { if(sitewise) pscore = sankoffNew(tree, data, cost = cost, site="site") else pscore = sankoffNew(tree, data, cost = cost) weight = attr(data, "weight") data = subset(data, tree$tip.label) m = lowerBound(data, cost = cost) if(sitewise){ return((m/pscore)[attr(data, "index")]) } sum(m * weight)/pscore } RI <- function (tree, data, cost = NULL, sitewise=FALSE) { if(sitewise) pscore = sankoffNew(tree, data, cost = cost, site="site") else pscore = sankoffNew(tree, data, cost = cost) data = subset(data, tree$tip.label) weight = attr(data, "weight") m = lowerBound(data, cost = cost) g = upperBound(data, cost = cost) if(sitewise){ res <- (g - pscore) / (g - m) # res[is.nan(res)] <- 0 return(res[attr(data, "index")]) } m = sum(m * weight) g = sum(g * weight) (g - pscore)/(g - m) } # not used add.one <- function (tree, tip.name, i){ if (!inherits(tree,"phylo")) stop("tree should be an object of class 'phylo.'") nTips = length(tree$tip.label) tmpedge = tree$edge m = max(tmpedge) l = nrow(tmpedge) trees <- vector("list", l) tmp = tree tmp$tip.label = c(tree$tip.label, tip.name) tmpedge[tmpedge > nTips] <- tmpedge[tmpedge > nTips] + 1L tmp$Nnode = tmp$Nnode + 1L tmp$edge.length <- NULL tmpedge = rbind(tmpedge, matrix(c(m + 2L, m + 2L, 0L, nTips + 1L), 2, 2)) edge = tmpedge edge[l + 1L, 2] <- edge[i, 2] edge[i, 2] <- m + 2L neworder = .C("C_reorder", edge[, 1], edge[, 2], as.integer(l + 2L), as.integer(m + 2L), integer(l + 2L), as.integer(nTips + 1L), PACKAGE = "phangorn")[[5]] tmp$edge <- edge[neworder, ] tmp } # in bab oder raus mmsNew0 <- function (x, Y) { w <- attr(x, "weight") names(w) = NULL m = length(x) data <- matrix(unlist(x[1:m]), ncol = m) l = nrow(data) v = Y[,1] + 1L # v = numeric(l) # for (i in 1:l) v[i] = length(.Internal(unique(data[i, ], # FALSE, FALSE))) result = matrix(NA, sum(w), 6) Res = matrix(NA, sum(w), m) Res2 = matrix(NA, sum(w), m) j = 1 res = 0 bin = as.integer(2L^c(0L:30L)) for (i in 1:(l - 1L)) { if (w[i] > 0) { v2 = v[i] + v[(i + 1L):l] - 2L v3 = integer(l - i) ind = which(w[(i + 1):l] > 0) V3 = matrix(NA, m, l - i) k = length(ind) V3[, ind] <- t(data[ind + i, , drop = FALSE]) + 100L * data[i, ] v3[ind] <- apply(V3[, ind, drop = FALSE], 2, function(x) { length(unique.default(x, FALSE, FALSE)) - 1L }) # length(.Internal(unique(x, FALSE, FALSE))) - 1L }) r = v3 - v2 while (any(r > 0) && w[i] > 0) { a = which.max(r) w0 = min(w[i], w[i + a]) if (w0 == 0) { r[a] = 0 } else { res = res + w0 * v3[a] w[i] = w[i] - w0 w[i + a] = w[i + a] - w0 result[j, ] = c(i, a + i, w0, r[a], v3[a], v2[a]) abc = V3[, a] Res[j, ] = bin[match(abc, unique(abc))] Res2[j, ] = match(abc, unique(abc)) r[a] = 0 j = j + 1 } } } } result = na.omit(result) mm = max(result[, 5]) Res = na.omit(Res) Res2 = na.omit(Res2) maxr = max(Res2) resm = apply(Res2, 1, function(x) { length(unique.default(x, FALSE, FALSE)) - 1L }) # length(.Internal(unique(x, FALSE, FALSE))) - 1L }) Res2 = t(Res2) Res2 = phyDat(Res2, type="USER", levels=1:maxr) names(Res2) = as.character(1:m) resm = lowerBound(Res2) ind = which(w > 0) # data = data[ind, ] tmp = matrix(0, attr(Res2, "nr"), m) for (i in 4:m) { tmp[, i] = resm - upperBound(subset(Res2, 1:i)) } tmp = tmp[attr(Res2, "index"), , drop=FALSE] tmp2 = Y[result[,1],] + Y[result[,2],] tmp3 = pmax(tmp, tmp2) # Res = rbind(Res, data[ind, ]) tmp = rbind(tmp3, Y[ind, ]) weight = c(result[, 3], w[ind]) res = t(tmp) %*% weight #res[m] - res res } # # Sankoff # #old2new.phyDat <- function(data){} # works only for nucleotides old2new.phyDat <- function(obj){ att <- attributes(obj) l = length(obj) contrast <- attr(obj, "contrast") nr <- attr(obj, "nr") X = matrix(rep(rowSums(contrast), each=nr),nrow=nr) res <- vector("list", l) for(i in 1:l){ # browser() tmp = X - tcrossprod(obj[[i]], contrast) res[[i]] = unlist(apply(tmp, 1, function(x)which(x<1e-6)[1])) } attributes(res) <- att res } old2new.phyDat <- function(obj){ att <- attributes(obj) l = length(obj) contrast <- attr(obj, "contrast") nr <- attr(obj, "nr") X = matrix(rep(rowSums(contrast), each=nr),nrow=nr) for(i in 1:l)obj[[i]][obj[[i]]>0] = 1 res <- vector("list", l) contrast[contrast==0]=1e6 for(i in 1:l){ tmp = tcrossprod(obj[[i]], contrast) - X res[[i]] = unlist(apply(tmp, 1, function(x)which(x<1e-6)[1])) } attributes(res) <- att res } new2old.phyDat <- function(data){ contrast = attr(data, "contrast") for(i in 1:length(data)) data[[i]] = contrast[data[[i]],,drop=FALSE] data } prepareDataSankoff <- function(data){ contrast = attr(data, "contrast") contrast[contrast == 0] = 1e+06 contrast[contrast == 1] <- 0 for (i in 1:length(data)) data[[i]] = contrast[data[[i]], , drop = FALSE] data } sankoff <- function (tree, data, cost = NULL, site = 'pscore') { if (!inherits(data,"phyDat")) stop("data must be of class phyDat") data <- prepareDataSankoff(data) levels <- attr(data, "levels") l = length(levels) if (is.null(cost)) { cost <- matrix(1, l, l) cost <- cost - diag(l) } for (i in 1:length(data)) storage.mode(data[[i]]) = "double" if(inherits(tree,"phylo")) return(fit.sankoff(tree, data, cost, returnData =site)) if(inherits(tree,"multiPhylo")){ if(is.null(tree$TipLabel))tree = unclass(tree) return(sapply(tree, fit.sankoff, data, cost, site)) } } fit.sankoff <- function (tree, data, cost, returnData = c("pscore", "site", "data")) { if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") returnData <- match.arg(returnData) node <- tree$edge[, 1] edge <- tree$edge[, 2] weight = attr(data, "weight") nr = p = attr(data, "nr") q = length(tree$tip.label) nc = l = attr(data, "nc") m = length(edge) + 1 dat = vector(mode = "list", length = m) dat[1:q] = data[tree$tip.label] node = as.integer(node - 1) edge = as.integer(edge - 1) nTips = as.integer(length(tree$tip.label)) mNodes = as.integer(max(node) + 1) tips = as.integer((1:length(tree$tip.label))-1) res <- .Call("sankoff3", dat, as.numeric(cost), as.integer(nr),as.integer(nc), node, edge, mNodes, tips, PACKAGE="phangorn") root <- getRoot(tree) erg <- .Call("C_rowMin", res[[root]], as.integer(nr), as.integer(nc), PACKAGE = "phangorn") if (returnData=='site') return(erg) pscore <- sum(weight * erg) result = pscore if (returnData=="data"){ result <- list(pscore = pscore, dat = res) } result } pnodes <- function (tree, data, cost) { if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") node <- tree$edge[, 1] edge <- tree$edge[, 2] nr = nrow(data[[1]]) nc = ncol(data[[1]]) node = as.integer(node - 1) edge = as.integer(edge - 1) .Call("pNodes", data, as.numeric(cost), as.integer(nr),as.integer(nc), node, edge, PACKAGE="phangorn") } indexNNI <- function(tree){ parent = tree$edge[, 1] child = tree$edge[, 2] ind = which(child %in% parent) Nnode = tree$Nnode edgeMatrix = matrix(0,(Nnode-1),5) pvector <- numeric(max(parent)) pvector[child] <- parent tips <- !logical(max(parent)) tips[parent] <- FALSE # cvector <- allCildren(tree) cvector <- vector("list",max(parent)) for(i in 1:length(parent)) cvector[[parent[i]]] <- c(cvector[[parent[i]]], child[i]) k=0 for(i in ind){ p1 = parent[i] p2 = child[i] e34 = cvector[[p2]] ind1 = cvector[[p1]] e12 = ind1[ind1 != p2] if(pvector[p1])e12=c(p1,e12) edgeMatrix[k+1, ] = c(e12,e34,p2) k=k+1 } # vielleicht raus attr(edgeMatrix, 'root') <-cvector[[min(parent)]] edgeMatrix } sankoff.nni = function (tree, data, cost, ...) { if(is.rooted(tree))tree<- reorder(unroot(tree), "postorder") INDEX <- indexNNI(tree) rootEdges <- attr(INDEX,"root") if (!inherits(data,"phyDat")) stop("data must be of class phyDat") levels <- attr(data, "levels") l = length(levels) weight = attr(data, "weight") p = attr(data, "nr") kl = TRUE i = 1 tmp = fit.sankoff(tree, data, cost, returnData='data') p0 = tmp[[1]] datf = tmp[[2]] datp = pnodes(tree, datf, cost) parent = tree$edge[,1] child = tree$edge[,2] m <- dim(INDEX)[1] k = min(parent) pscore = numeric(2*m) for(i in 1:m){ ei = INDEX[i,] datn <- datf[ei[1:4]] if (!(ei[5] %in% rootEdges)) datn[1] = datp[ei[1]] pscore[(2*i)-1] <- sankoff.quartet(datn[ c(1, 3, 2, 4)], cost, p, l, weight) pscore[(2*i)] <- sankoff.quartet(datn[ c(1, 4, 3, 2)], cost, p, l, weight) } swap <- 0 candidates <- pscore < p0 while(any(candidates)){ ind = which.min(pscore) pscore[ind]=Inf if( ind %% 2 ) swap.edge = c(2,3) else swap.edge = c(2,4) tree2 <- changeEdge(tree, INDEX[(ind+1)%/%2,swap.edge]) test <- fit.sankoff(tree2, data, cost, 'pscore') if(test >= p0) candidates[ind] = FALSE if(test < p0) { p0 <- test swap=swap+1 tree <- tree2 candidates[ind] = FALSE indi <- which(rep(colSums(apply(INDEX,1,match,INDEX[(ind+1)%/%2,],nomatch=0))>0,each=2)) candidates[indi] <- FALSE pscore[indi] <- Inf } } list(tree = tree, pscore = p0, swap = swap) } optim.parsimony <- function(tree,data, method='fitch', cost=NULL, trace=1, rearrangements="SPR", ...){ if(method=='fitch') result <- optim.fitch(tree=tree, data=data, trace=trace, rearrangements=rearrangements, ...) if(method=='sankoff') result <- optim.sankoff(tree=tree, data=data, cost=cost, trace=trace, ...) result } # perturbation="ratchet", "stochastic" pratchet <- function (data, start=NULL, method="fitch", maxit=1000, k=10, trace=1, all=FALSE, rearrangements="SPR", perturbation="ratchet", ...) { eps = 1e-08 # if(method=="fitch" && (is.null(attr(data, "compressed")) || attr(data, "compressed") == FALSE)) # data <- compressSites(data) trace = trace - 1 uniquetree <- function(trees) { k = 1 res = trees[[1]] result = list() result[[1]]=res k=2 trees = trees[-1] while (length(trees) > 0) { # test and replace # change RF to do this faster RF.dist(res, trees) class(tree) = "multiPhylo" # rf2 = RF.dist(res, trees, FALSE) rf = sapply(trees, RF.dist, res, FALSE) if(any(rf==0))trees = trees[-which(rf == 0)] if (length(trees) > 0) { res = trees[[1]] result[[k]] = res k=k+1 trees = trees[-1] } } result } if (is.null(start)) start = optim.parsimony(nj(dist.hamming(data)), data, trace = trace, method=method, rearrangements=rearrangements, ...) tree = start data = subset(data, tree$tip.label) attr(tree, "pscore") = parsimony(tree, data, method=method, ...) mp <- attr(tree, "pscore") if (trace >= 0) print(paste("Best pscore so far:",attr(tree, "pscore"))) FUN = function(data, tree, method, rearrangements, ...) optim.parsimony(tree, data = data, method=method, rearrangements=rearrangements, ...) result = list() result[[1]] = tree kmax = 1 nTips = length(tree$tip.label) for (i in 1:maxit) { if(perturbation=="ratchet"){ bstrees <- bootstrap.phyDat(data, FUN, tree = tree, bs = 1, trace = trace, method=method, rearrangements=rearrangements, ...) trees <- lapply(bstrees, optim.parsimony, data, trace = trace, method=method, rearrangements=rearrangements, ...) } if(perturbation=="stochastic"){ treeNNI <- rNNI(tree, floor(nTips/2)) trees <- optim.parsimony(treeNNI, data, trace = trace, method = method, rearrangements = rearrangements, ...) trees <- list(trees) } if(inherits(result,"phylo"))m=1 else m = length(result) if(m>0) trees[2 : (1+m)] = result[1:m] pscores <- sapply(trees, function(data) attr(data, "pscore")) mp1 = min(pscores) if((mp1+eps) < mp) kmax=1 else kmax=kmax+1 mp=mp1 if (trace >= 0) print(paste("Best pscore so far:",mp)) ind = which(pscores < mp + eps) if (length(ind) == 1) { result = trees[ind] tree = result[[1]] } else { result = uniquetree(trees[ind]) l = length(result) tree = result[[sample(l, 1)]] } if(kmax == k) break() }# for if(!all) return(tree) if(length(result)==1) return(result[[1]]) class(result) = "multiPhylo" result } # pratchet optim.sankoff <- function(tree, data, cost=NULL, trace=1, ...) { if(!inherits(tree,"phylo")) stop("tree must be of class phylo") if(is.rooted(tree))tree <- unroot(tree) if(is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") if (class(data)[1] != "phyDat") stop("data must be of class phyDat") rt = FALSE dat <- prepareDataSankoff(data) l <- attr(dat, "nc") if (is.null(cost)) { cost <- matrix(1, l, l) cost <- cost - diag(l) # rt = TRUE } tree$edge.length=NULL swap = 0 iter = TRUE pscore <- fit.sankoff(tree,dat,cost,'pscore') while (iter) { res <- sankoff.nni(tree,dat,cost,...) tree <- res$tree if(trace>1)cat("optimize topology: ", pscore , "-->", res$pscore, "\n") pscore = res$pscore swap = swap + res$swap if (res$swap == 0) iter = FALSE } if(trace>0)cat("Final p-score",pscore,"after ",swap, "nni operations \n") if(rt)tree <- ptree(tree, data) attr(tree, "pscore") = pscore tree } # # ACCTRAN # ptree <- function (tree, data, type = "ACCTRAN", retData = FALSE) { if (!inherits(data,"phyDat")) stop("data must be of class phyDat") if (is.null(attr(tree, "order")) || attr(tree, "order") == "cladewise") tree <- reorder(tree, "postorder") # if (!is.binary.tree(tree)) # stop("Tree must be binary!") tmp = fitch(tree, data, site = "data") nr = attr(data, "nr") node <- tree$edge[, 1] edge <- tree$edge[, 2] weight = attr(data, "weight") m = length(edge) + 1 q = length(tree$tip.label) l = as.integer(length(edge)) nTips = length(tree$tip.label) dat = tmp[[2]] if (!is.rooted(tree)) { root = getRoot(tree) ind = edge[node == root] rSeq = .C("fitchTriplet", integer(nr), dat[, ind[1]], dat[, ind[2]], dat[, ind[3]], as.integer(nr)) dat[, root] = rSeq[[1]] } # result <- .C("ACCTRAN2", dat, as.integer(nr), numeric(nr), # as.integer(node[l:1L]), as.integer(edge[l:1L]), l, as.double(weight), numeric(l), as.integer(nTips)) result <- .C("ACCTRAN2", dat, as.integer(nr), as.integer(node[l:1L]), as.integer(edge[l:1L]), l, as.integer(nTips)) el = result[[5]][l:1L] if (!is.rooted(tree)) { ind2 = which(node[] == root) dat = matrix(result[[1]], nr, max(node)) result <- .C("ACCTRAN3", result[[1]], as.integer(nr), numeric(nr), as.integer(node[(l - 3L):1L]), as.integer(edge[(l - 3L):1L]), l - 3L, as.double(weight), numeric(l)) # , as.integer(nTips) el = result[[8]][(l - 3L):1L] pars = .C("fitchTripletACC4", dat[, root], dat[, ind[1]], dat[, ind[2]], dat[, ind[3]], as.integer(nr), numeric(1), numeric(1), numeric(1), as.double(weight), numeric(nr), integer(nr)) el[ind2[1]] = pars[[6]] el[ind2[2]] = pars[[7]] el[ind2[3]] = pars[[8]] } else { result <- .C("ACCTRAN3", result[[1]], as.integer(nr), numeric(nr), as.integer(node[l:1L]), as.integer(edge[l:1L]), l, as.double(weight), numeric(l)) # , as.integer(nTips) el = result[[8]][l:1L] } tree$edge.length = el if (retData) return(list(tree, matrix(result[[1]], nr, max(node)))) tree } acctran <- function(tree, data) ptree(tree, data, type="ACCTRAN", retData=FALSE) parsimony.plot <- function(tree, ...){ x = numeric(max(tree$edge)) x[tree$edge[,2]] = tree$edge.length plot(tree, ...) ind <- get("last_plot.phylo", envir = .PlotPhyloEnv)$edge[, 2] edgelabels(prettyNum(x[ind]), frame = "none") } phangorn/vignettes/0000755000175100001440000000000013020643153014111 5ustar hornikusersphangorn/vignettes/phangorn.bib0000644000175100001440000007170012761653173016426 0ustar hornikusers@article{Abascal2005, author = {Abascal, Federico and Zardoya, Rafael and Posada, David}, title = {ProtTest: selection of best-fit models of protein evolution}, volume = {21}, number = {9}, pages = {2104-2105}, year = {2005}, doi = {10.1093/bioinformatics/bti263}, URL = {http://bioinformatics.oxfordjournals.org/content/21/9/2104.abstract}, eprint = {http://bioinformatics.oxfordjournals.org/content/21/9/2104.full.pdf+html}, journal = {Bioinformatics} } @article{Leo2008, author = {de Oliveira Martins, Leonardo AND Leal, Élcio AND Kishino, Hirohisa}, journal = {PLoS ONE}, publisher = {Public Library of Science}, title = {Phylogenetic Detection of Recombination with a Bayesian Prior on the Distance between Trees}, year = {2008}, month = {07}, volume = {3}, url = {http://dx.plos.org/10.1371%2Fjournal.pone.0002651}, pages = {1-13}, number = {7}, doi = {10.1371/journal.pone.0002651} } @article{Leo2016, author = {De Oliveira Martins, Leonardo and Mallo, Diego and Posada, David}, title = {A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction}, volume = {65}, number = {3}, pages = {397-416}, year = {2016}, oi = {10.1093/sysbio/syu082}, URL = {http://sysbio.oxfordjournals.org/content/65/3/397.abstract}, eprint = {http://sysbio.oxfordjournals.org/content/65/3/397.full.pdf+html}, journal = {Systematic Biology} } @article{Schwarz1978, author = "Schwarz, Gideon", doi = "10.1214/aos/1176344136", fjournal = "The Annals of Statistics", journal = "Ann. 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Anderson", Title = "Model selection and multimodel inference: a practical information-theoretic approach", Edition = "Second", Publisher = "Springer", Address = "New York", Year = "2002" } @Article{ Lapointe2010, title = "Clanistics: a multi-level perspective for harvesting unrooted gene trees", journal = "Trends in Microbiology", volume = "18", number = "8", pages = "341--347", year = "2010", url = "http://www.sciencedirect.com/science/article/pii/S0966842X10000570", author = "Fran\c{c}ois-Joseph Lapointe and Philippe Lopez and Yan Boucher and Jeremy Koenig and Eric Bapteste" } @article{Vos2003, author = {Vos, R. A.}, title = {Accelerated Likelihood Surface Exploration: The Likelihood Ratchet}, volume = {52}, number = {3}, pages = {368-373}, year = {2003}, doi = {10.1080/10635150390196993}, URL = {http://sysbio.oxfordjournals.org/content/52/3/368.abstract}, journal = {Systematic Biology} } @article{Wilkinson2007, title = "Of clades and clans: terms for phylogenetic relationships in unrooted trees", journal = "Trends in Ecology and Evolution", volume = "22", number = "3", pages = "114--115", year = "2007", url = "http://www.sciencedirect.com/science/article/pii/S016953470700019", author = "Mark Wilkinson and James O. McInerney and Robert P. Hirt and Peter G. Foster and T. Martin Embley" } @article{Warnow2012, title = "Standard maximum likelihood analyses of alignments with gaps can be statistically inconsistent", journal = "PLOS Currents Tree of Life", year = "2012", doi = "10.1371/currents.RRN1308", author = "Tandy Warnow" } @Manual{knitr, title = {knitr: A General-Purpose Package for Dynamic Report Generation in R}, author = {Yihui Xie}, year = {2015}, note = {R package version 1.11}, url = {http://yihui.name/knitr/}, } @Book{Xie2015, title = {Dynamic Documents with {R} and knitr}, author = {Yihui Xie}, publisher = {Chapman and Hall/CRC}, address = {Boca Raton, Florida}, year = {2015}, edition = {2nd}, note = {ISBN 978-1498716963}, url = {http://yihui.name/knitr/}, } @InCollection{Xie2014, booktitle = {Implementing Reproducible Computational Research}, editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng}, title = {knitr: A Comprehensive Tool for Reproducible Research in {R}}, author = {Yihui Xie}, publisher = {Chapman and Hall/CRC}, year = {2014}, note = {ISBN 978-1466561595}, url = {http://www.crcpress.com/product/isbn/9781466561595}, } phangorn/vignettes/Trees.Rnw0000644000175100001440000003253612761653173015712 0ustar hornikusers%\VignetteIndexEntry{Constructing phylogenetic trees} %\VignetteKeywords{Documentation} %\VignettePackage{phangorn} %\VignetteEngine{Sweave} \documentclass[12pt]{article} \usepackage{times} \usepackage{hyperref} \begin{document} \SweaveOpts{concordance=TRUE} \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rmethod}[1]{{\texttt{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \textwidth=6.2in \textheight=8.5in %\parskip=.3cm \oddsidemargin=.1in \evensidemargin=.1in \headheight=-.3in \newcommand{\R}{\textsf{R}} \newcommand{\pml}{\Robject{pml}} \newcommand{\phangorn}{\Rpackage{phangorn}} \newcommand{\ape}{\Rpackage{ape}} \newcommand{\multicore}{\Rpackage{multicore}} \newcommand{\term}[1]{\emph{#1}} \newcommand{\mref}[2]{\htmladdnormallinkfoot{#2}{#1}} % leave comments in the text \SweaveOpts{keep.source=TRUE} % Ross Ihakas extenstion for nicer representation \DefineVerbatimEnvironment{Sinput}{Verbatim} {xleftmargin=2em} \DefineVerbatimEnvironment{Soutput}{Verbatim}{xleftmargin=2em} \DefineVerbatimEnvironment{Scode}{Verbatim}{xleftmargin=2em} \fvset{listparameters={\setlength{\topsep}{0pt}}} \renewenvironment{Schunk}{\vspace{\topsep}}{\vspace{\topsep}} <>= options(width=70) foo <- packageDescription("phangorn") options("show.signif.stars" = FALSE) @ \title{Estimating phylogenetic trees with phangorn} %$ (Version \Sexpr{foo$Version})} \author{\mref{mailto:klaus.schliep@gmail.com}{Klaus P. Schliep}} \maketitle \nocite{Paradis2012} \section{Introduction} These notes should enable the user to estimate phylogenetic trees from alignment data with different methods using the \phangorn{} package \cite{Schliep2011}. Several functions of \phangorn{} are also described in more detail in \cite{Paradis2012}. For more theoretical background on all the methods see e.g. \cite{Felsenstein2004, Yang2006}. This document illustrates some of the \phangorn{} features to estimate phylogenetic trees using different reconstruction methods. Small adaptations to the scripts in section \ref{sec:Appendix1} should enable the user to perform phylogenetic analyses. \section{Getting started} The first thing we have to do is to read in an alignment. Unfortunately there exists many different file formats that alignments can be stored in. The function \Rfunction{read.phyDat} is used to read in an alignment. There are several functions to read in alignments depending on the format of the data set (nexus, phylip, fasta) and the kind of data (amino acid or nucleotides) in the \ape{} package \cite{Paradis2004} and \phangorn{}. The function \Rfunction{read.phyDat} calls these other functions. For the specific parameter settings available look in the help files of the function \Rfunction{read.dna} (for phylip, fasta, clustal format), \Rfunction{read.nexus.data} for nexus files. For amino acid data additional \Rfunction{read.aa} is called. %When using the \Rfunction{read.dna} from \ape{} the parameter the we have to use as.character=TRUE. We start our analysis loading the \phangorn{} package and then reading in an alignment. <>= library(phangorn) primates <- read.phyDat("primates.dna", format="phylip", type="DNA") @ \section{Distance based methods} After reading in the alignment we can build a first tree with distance based methods. The function dist.dna from the ape package computes distances for many DNA substitution models. To use the function dist.dna we have to transform the data to class DNAbin. For amino acids the function \Rfunction{dist.ml} offers common substitution models (for example "WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa" or "mtREV24"). After constructing a distance matrix we reconstruct a rooted tree with UPGMA and alternatively an unrooted tree using Neighbor Joining \cite{Saitou1987,Studier1988}. More distance methods like fastme are available in the \Rpackage{ape} package. <>= dm <- dist.ml(primates) treeUPGMA <- upgma(dm) treeNJ <- NJ(dm) @ We can plot the trees treeUPGMA and treeNJ (figure \ref{fig:NJ}) with the commands: <>= layout(matrix(c(1,2), 2, 1), height=c(1,2)) par(mar = c(0,0,2,0)+ 0.1) plot(treeUPGMA, main="UPGMA") plot(treeNJ, "unrooted", main="NJ") @ \begin{figure} \begin{center} <>= <> @ \end{center} \caption{Rooted UPGMA tree and unrooted NJ tree} \label{fig:NJ} \end{figure} Distance based methods are very fast and we will use the UPGMA and NJ tree as starting trees for the maximum parsimony and maximum likelihood analyses. \section{Parsimony} The function parsimony returns the parsimony score, that is the number of changes which are at least necessary to describe the data for a given tree. We can compare the parsimony score or the two trees we computed so far: <>= parsimony(treeUPGMA, primates) parsimony(treeNJ, primates) @ The function optim.parsimony performs tree rearrangements to find trees with a lower parsimony score. The tree rearrangement implemented are nearest-neighbor interchanges (NNI) and subtree pruning and regrafting (SPR). The later one only works so far with the fitch algorithm. However is also a version of the parsimony ratchet \cite{Nixon1999} implemented, which is likely to find better trees than just doing NNI / SPR rearrangements. <>= treePars <- optim.parsimony(treeUPGMA, primates) treeRatchet <- pratchet(primates, trace = 0) parsimony(c(treePars, treeRatchet), primates) @ For small data sets it is also possible to find all most parsimonious trees using a branch and bound algorithm \cite{Hendy1982}. For data sets with more than 10 taxa this can take a long time and depends strongly on how tree like the data are. <>= (trees <- bab(subset(primates,1:10))) @ \section{Maximum likelihood} The last method we will describe in this vignette is Maximum Likelihood (ML) as introduced by Felsenstein \cite{Felsenstein1981}. We can easily compute the likelihood for a tree given the data <>= fit = pml(treeNJ, data=primates) fit @ The function \Rfunction{pml} returns an object of class \Robject{pml}. This object contains the data, the tree and many different parameters of the model like the likelihood. There are many generic functions for the class Robject{pml} available, which allow the handling of these objects. <>= methods(class="pml") @ The object fit just estimated the likelihood for the tree it got supplied, but the branch length are not optimized for the Jukes-Cantor model yet, which can be done with the function \Rfunction{optim.pml}. <>= fitJC <- optim.pml(fit, TRUE) logLik(fitJC) @ With the default values \Rfunction{pml} will estimate a Jukes-Cantor model. The function \Rfunction{update.pml} allows to change parameters. We will change the model to the GTR + $\Gamma(4)$ + I model and then optimize all the parameters. <>= fitGTR <- update(fit, k=4, inv=0.2) fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE, rearrangement = "NNI", control = pml.control(trace = 0)) fitGTR @ With the control parameters the threshols for the fitting process can be changed. Here we want just to supress output during the fitting process. For larger trees the NNI rearrangements often get stuck in local maxima. We added two stochatic algorithm to improve topology search. The first (set rearrangement="stochastic") performs stochastic rearrangements similar as in \cite{Nguyen2015}, which makes random NNI permuation to the tree, which than gets optimised to escape local optima. The second option (rearrangement="ratchet") perform the likelihood ratchet \cite{Vos2003}. While these algorithms may find better trees they will also take more time. <>= fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE, rearrangement = "stochastic", control = pml.control(trace = 0)) fitGTR @ \subsection{Model selection} We can compare nested models for the JC and GTR + $\Gamma(4)$ + I model using likelihood ratio statistic <>= anova(fitJC, fitGTR) @ with the Shimodaira-Hasegawa \cite{Shimodaira1999} test <>= SH.test(fitGTR, fitJC) @ or with the AIC <>= AIC(fitJC) AIC(fitGTR) AICc(fitGTR) BIC(fitGTR) @ An alternative is to use the function \Rfunction{modelTest} to compare different nucleotide or protein models the AIC, AICc or BIC, similar to popular programs ModelTest and ProtTest \cite{Posada1998, Posada2008, Abascal2005}. <>= load("Trees.RData") @ <>= mt = modelTest(primates) @ The results of \Rfunction{modelTest} is illustrated in table \ref{tab:modelTest} \begin{center} <>= library(xtable) print(xtable(mt, caption="Summary table of modelTest", label="tab:modelTest"), include.rownames=FALSE) @ \end{center} The thresholds for the optimization in \Rfunction{modelTest} are not as strict as for \Rfunction{optim.pml} and no tree rearrangements are performed. As \Rfunction{modelTest} computes and optimizes a lot of models it would be a waste of computer time not to save these results. The results are saved as call together with the optimized trees in an environment and this call can be evaluated to get a "pml" object back to use for further optimization or analysis. <>= env <- attr(mt, "env") ls(envir=env) (fit <- eval(get("HKY+G+I", env), env)) @ At last we may want to apply bootstrap to test how well the edges of the tree are supported: %, results=hide <>= bs = bootstrap.pml(fitJC, bs=100, optNni=TRUE, control = pml.control(trace = 0)) @ %$ Now we can plot the tree with the bootstrap support values on the edges and also look at consensusNet to identify potential conflict. <>= par(mfrow=c(2,1)) par(mar=c(1,1,3,1)) plotBS(midpoint(fitJC$tree), bs, p = 50, type="p") title("a)") cnet <- consensusNet(bs, p=0.2) plot(cnet, "2D", show.edge.label=TRUE) title("b)") @ %$ \begin{figure} \begin{center} <>= <> @ \end{center} \caption{a) Unrooted tree (midpoint rooted) with bootstrap support values. b) ConsensusNet from the bootstrap sample} \label{fig:BS} \end{figure} Several analyses, e.g. \Rfunction{bootstrap} and \Rfunction{modelTest}, can be computationally demanding, but as nowadays most computers have several cores one can distribute the computations using the \multicore{} package. However it is only possible to use this approach if R is running from command line ("X11"), but not using a GUI (for example "Aqua" on Macs) and unfortunately the \multicore{} package does not work at all under Windows. \newpage \section{Appendix: Standard scripts for nucleotide analysis}\label{sec:Appendix1} Here we provide two standard scripts which can be adapted for the most common tasks. Most likely the arguments for \Rfunction{read.phyDat} have to be adapted to accommodate your file format. Both scripts assume that the \multicore{} package works on your platform, see comments above. <>= options(prompt=" ") options(continue=" ") @ <>= library(phangorn) file="myfile" dat = read.phyDat(file) dm = dist.ml(dat, "F81") tree = NJ(dm) # as alternative for a starting tree: tree <- pratchet(dat) # parsimony tree tree <- nnls.phylo(tree, dm) # need edge weights # 1. alternative: quick and dirty: GTR + G fitStart = pml(tree, dat, k=4) fit = optim.pml(fitStart, model="GTR", optGamma=TRUE, rearrangement="stochastic") # 2. alternative: preper with modelTest mt <- modelTest(dat, tree=tree, multicore=TRUE) mt[order(mt$AICc),] # choose best model from the table according to AICc bestmodel <- mt$Model[which.min(mt$AICc)] env = attr(mt, "env") fitStart = eval(get("GTR+G+I", env), env) # or let R search the table fitStart = eval(get(bestmodel, env), env) # equivalent to: fitStart = eval(get("GTR+G+I", env), env) fit = optim.pml(fitStart, rearrangement = "stochastic", optGamma=TRUE, optInv=TRUE, model="GTR") bs = bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE) @ \newpage \section{Appendix 2: Standard scripts for amino acid analysis}%\label{sec:Appendix2} You can specify different several models build in which you can specify, e.g. "WAG", "JTT", "Dayhoff", "LG". Optimizing the rate matrix for amino acids is possible, but would take a long, a very long time and you will need to have a large alignement to estimate all the parameters. So make sure to set optBf=FALSE and optQ=FALSE in the function \Rfunction{optim.pml}, which is also the default. <>= library(phangorn) file="myfile" dat = read.phyDat(file, type = "AA") dm = dist.ml(dat, model="JTT") tree = NJ(dm) # parallel will only work safely from command line # and not at all windows (mt <- modelTest(dat, model=c("JTT", "LG", "WAG"), multicore=TRUE)) # run all available amino acid models (mt <- modelTest(dat, model="all", multicore=TRUE)) fitStart = eval(get(mt$Model[which.min(mt$BIC)], env), env) fitNJ = pml(tree, dat, model="JTT", k=4, inv=.2) fit = optim.pml(fitNJ, rearrangement = "stochastic", optInv=TRUE, optGamma=TRUE) fit bs = bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE) @ \newpage % \cite{Sugiura1978} \bibliographystyle{plain} \bibliography{phangorn} \section{Session Information} The version number of \R{} and packages loaded for generating the vignette were: <>= toLatex(sessionInfo()) @ \end{document} 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It also provides a step-by-step guide for users new to R. *Methodological advancement:* The major advancement in this phangorn update is the introduction of a generic network object with a wide range of related transfer and analysis functions. These new functions provide the first means to directly transfer information amongst a wide range of phylogenetic trees and networks, as well as means to visualise and further analyse this information. This should provide a platform for individuals to easily conduct tree-network comparisons and stimulate further function development by the community. *What next?:* By implementing full network handling compatibility in R, and providing exemplar scripts (such as this) and [support](https://github.com/KlausVigo/phangorn), the scientific community now has an easy means to analyse and compare the results of phylogenetic trees vs. network approaches. We hope this will open a largely unexplored world to the general phylogenetic audience. ### Installing R 1. Download R Select the nearest mirror to your location at https://cran.r-project.org/mirrors.html 2. Select your operating system and download the relevant installation file. 3. Install R following the instructions. ### Installing the phangorn library Open R and run the two lines of code below in the command line. (You will need to select a region from which to download the library) ```{r, eval=FALSE} install.packages("phangorn", dependencies=TRUE) # install latest development version needs devtools install.packages("devtools", dependencies=TRUE) library(devtools) install_github("KlausVigo/phangorn") ``` ### Getting started ```{r} library(phangorn) # load the phangorn library ``` ### Set the working directory This is often a major stumbling block for new R users. You need to specify where in your folder structure you wish to work. i.e, where are the files stored that you wish to input? This is done using the setwd() function, e.g. setwd("C:/TreesNetworks/Example Files"). We now set it to the folder of the phangorn package, which contains the files we want to load for this example. ### Read in the example file datasets: This example files are based on the woodmouse dataset available in the ape library. Ultimately, this dataset is from this study: Michaux, J. R., Magnanou, E., Paradis, E., Nieberding, C. and Libois, R. (2003) Mitochondrial phylogeography of the Woodmouse (*Apodemus sylvaticus*) in the Western Palearctic region. Molecular Ecology, 12, 685-697.) ```{r} ## automatically set the correct working directory for the examples below # setwd(system.file("extdata/trees", package = "phangorn")) # for this vignette we create a path to the files we want to load fdir <- system.file("extdata/trees", package = "phangorn") ## in your case it may look something like this... # setwd("C:/TreesNetworks/Example Files") ##DNA Matrix, maybe not needed woodmouse <- read.phyDat(file.path(fdir, "woodmouse.fasta"),format="fasta") ## RAxML best-known tree with bipartition support (from previous analysis) raxml.tree <- read.tree(file.path(fdir,"RAxML_bipartitions.woodmouse")) ## RAxML bootstrap trees (from previous analysis) raxml.bootstrap <- read.tree(file.path(fdir,"RAxML_bootstrap.woodmouse")) ## MrBayes consensus tree (50% majority rule) (from previous analysis) mrbayes.tree <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.con")) ## MrBayes sample runs 1 and 2 (from previous analysis) run1 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run1.t")) run2 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run2.t")) ## How many trees are in the MrBayes tree sample? run1 run2 ## Combining the two runs and removing 25% burn-in mrbayes.trees <- c(run1[251:1001],run2[251:1001]) ## NeigbourNet Nexus file generated by SplitsTree (from previous analysis) Nnet <- read.nexus.networx(file.path(fdir,"woodmouse.nxs")) ``` All example files read into R. ### Viewing the data ```{r} par(mfrow=c(1,2), mar=c(2,2,2,2)) # Setting plot parameters ### Plotting trees with support values: ## RAxML plot(raxml.tree) nodelabels(raxml.tree$node.label, adj = c(1, 0), frame = "none") ## MrBayes plot(mrbayes.tree) nodelabels(mrbayes.tree$node.label, adj = c(1, 0), frame = "none") par(mfrow=c(1,1)) # Setting plot parameters # NeighbourNet plot.networx(Nnet,"2D") ## alternatively, # plot(Nnet,"2D") ``` ### Interwining trees and network functions ### 1A: Identification of edge bundles (in black) in a neighbour-net (NN) network that correspond to branches (labelled 1-12) in a tree (a maximum likelihood tree, in this case). ```{r} # create a vector of labels for the network corresponding to edges in the tree edge.lab <- createLabel(Nnet, raxml.tree, raxml.tree$edge[,2], "edge") # could be also 1:27 instead of raxml.tree$edge[,2] # Show the correspondingly labelled tree and network in R par(mfrow=c(1,2), mar=c(1,1,1,1)) #plotBS(raxml.tree, rotate.tree = 180) plot(raxml.tree, "u", rotate.tree = 180) edgelabels(raxml.tree$edge[,2],col="blue", frame="none") # find edges that are in the network but not in the tree edge.col <- rep("black", nrow(Nnet$edge)) edge.col[ is.na(edge.lab) ] <- "red" # or a simpler alternative... edge.col <- createLabel(Nnet, raxml.tree, "black", nomatch="red") x <- plot.networx(Nnet, edge.label = edge.lab, show.edge.label = T, "2D", edge.color = edge.col, col.edge.label = "blue") # the above plot function returns an invisible networx object and this object also # contains the colors for the edges. ``` ### 1B: Bootstrap support for all branches (branch labels) in the ML tree mapped on the corresponding edge bundles of the NN network. The edges in the network which are not found as ML tree branches are highlighted in red. ```{r} x <- addConfidences(Nnet,raxml.tree) # find splits that are in the network but not in the tree split.col <- rep("black", length(x$splits)) split.col[ !matchSplits(as.splits(x), as.splits(raxml.tree)) ] <- "red" # simpler alternative... split.col2 <- createLabel(x, raxml.tree, label="black", "split", nomatch="red") # Plotting in R par(mfrow=c(1,1)) out.x <- plot.networx(x,"2D",show.edge.label=TRUE, split.color=split.col, col.edge.label = "blue") ``` Again we can write to SplitsTree for viewing... ```{r} # write.nexus.networx(out.x,"woodmouse.tree.support.nxs") ## or we can also export the splits alone (for usage in software other than SplitsTree) # write.nexus.splits(as.splits(out.x),"woodmouse.splits.support.nxs") ``` ### 1C: Frequencies of bipartitions found in the bootstrap pseudoreplicates mapped on the corresponding edge bundles of the NN network using a threshold of 10% (i.e. any edge is labelled that occurs in at least 100 of the 1000 ML-BS pseudoreplicates). Edge bundles not found in the ML tree are labelled using grey edges. ```{r} y <- addConfidences(Nnet, as.splits(raxml.bootstrap)) edge.col <- createLabel(y, raxml.tree, label="black", "edge", nomatch="grey") y <- plot(y,"2D",show.edge.label=TRUE, edge.color=edge.col) ## Write to SplitsTree for viewing # write.nexus.networx(y,"NN.with.bs.support.nxs") ``` ### Extras... We can also compare the neighborNet with a consensusNet (Holland BR, Huber KT, Moulton V, Lockhart PJ,2004, Using consensus networks to visualize contradictory evidence for species phylogeny. Molecular Biology and Evolution, 21, 1459-1461). Furthermore, we can extract the support values from the consensus network, and place these on to the NeighbourNet (this is similar to the process explained in 1C above). ```{r} cnet <- consensusNet(raxml.bootstrap,prob=0.10) edge.col <- createLabel(cnet, Nnet, label="black", "edge", nomatch="grey") cnet <- plot.networx(cnet, "2D", show.edge.label = TRUE, edge.color=edge.col) edge.col <- createLabel(Nnet, cnet, label="black", "edge", nomatch="grey") z <- plot.networx(Nnet, "2D", show.edge.label = TRUE, edge.color=edge.col) obj <- addConfidences(Nnet,cnet) plot.networx(obj,"2D",show.edge.label=T, edge.color=edge.col, col.edge.label = "blue") ## Write to SplitsTree for viewing # write.nexus.networx(obj,"Nnet.with.ML.Cnet.Bootstrap.nxs") ``` phangorn/vignettes/Ancestral.Rnw0000644000175100001440000001351312761653173016536 0ustar hornikusers%\VignetteIndexEntry{Ancestral Sequence Reconstruction} %\VignetteKeywords{Documentation} %\VignettePackage{phangorn} %\VignetteEngine{Sweave} \documentclass[12pt]{article} \usepackage{times} \usepackage{hyperref} \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rmethod}[1]{{\texttt{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \textwidth=6.2in \textheight=8.5in %\parskip=.3cm \oddsidemargin=.1in \evensidemargin=.1in \headheight=-.3in \newcommand{\R}{\textsf{R}} \newcommand{\pml}{\Robject{pml}} \newcommand{\phangorn}{\Rpackage{phangorn}} \newcommand{\ape}{\Rpackage{ape}} \newcommand{\multicore}{\Rpackage{multicore}} \newcommand{\term}[1]{\emph{#1}} \newcommand{\mref}[2]{\htmladdnormallinkfoot{#2}{#1}} \begin{document} % Ross Ihakas extenstion for nicer representation \DefineVerbatimEnvironment{Sinput}{Verbatim} {xleftmargin=2em} \DefineVerbatimEnvironment{Soutput}{Verbatim}{xleftmargin=2em} \DefineVerbatimEnvironment{Scode}{Verbatim}{xleftmargin=2em} \fvset{listparameters={\setlength{\topsep}{0pt}}} \renewenvironment{Schunk}{\vspace{\topsep}}{\vspace{\topsep}} <>= options(width=70) options("show.signif.stars" = FALSE) foo <- packageDescription("phangorn") @ \title{Ancestral sequence reconstruction with phangorn (Version \Sexpr{foo$Version})} %$ \author{\mref{mailto:klaus.schliep@gmail.com}{Klaus P. Schliep}} \maketitle \nocite{Paradis2006} \section{Introduction} These notes describe the ancestral sequence reconstruction using the \phangorn{} package \cite{Schliep2011}. \phangorn{} provides several methods to estimate ancestral character states with either Maximum Parsimony (MP) or Maximum Likelihood (ML). %For more background on all the methods see e.g. \cite{Felsenstein2004, Yang2006}. \section{Parsimony reconstructions} To reconstruct ancestral sequences we first load some data and reconstruct a tree: <>= library(phangorn) primates = read.phyDat("primates.dna", format = "phylip", type = "DNA") tree = pratchet(primates, trace=0) tree = acctran(tree, primates) parsimony(tree, primates) @ For parsimony analysis of the edge length represent the observed number of changes. Reconstructing ancestral states therefore defines also the edge lengths of a tree. However there can exist several equally parsimonious reconstructions or states can be ambiguous and therefore edge length can differ. %\phangorn{} brakes them equally down. "MPR" reconstructs the ancestral states for each (internal) node as if the tree would be rooted in that node. However the nodes are not independent of each other. If one chooses one state for a specific node, this can restrict the choice of neighbouring nodes (figure \ref{fig:Pars}). The function acctran (accelerated transformation) assigns edge length and internal nodes to the tree \cite{Swofford1987}. <>= anc.acctran = ancestral.pars(tree, primates, "ACCTRAN") anc.mpr = ancestral.pars(tree, primates, "MPR") @ All the ancestral reconstructions for parsimony are based on the fitch algorithm and so far only bifurcating trees are allowed. However trees can get pruned afterwards using the function \Rfunction{multi2di} from \ape{}. <>= tmp <- require(seqLogo) if(tmp) seqLogo( t(subset(anc.mpr, getRoot(tree), 1:20)[[1]]), ic.scale=FALSE) @ \begin{figure} \begin{center} <>= <> @ \caption{Representation for the reconstruction of the first 20 characters for the root node.} \end{center} \end{figure} <>= options(SweaveHooks=list(fig=function() par(mar=c(2.1, 4.1, 2.1, 2.1)))) @ <>= par(mfrow=c(2,1)) plotAnc(tree, anc.mpr, 17) title("MPR") plotAnc(tree, anc.acctran, 17) title("ACCTRAN") @ \begin{figure} \begin{center} <>= <> @ \caption{Ancestral reconstruction for one character for the "MPR" and "ACCTRAN" reconstruction. When nodes contain several colours reconstruction is not unique!}\label{fig:Pars} \end{center} \end{figure} \section{Likelihood reconstructions} \phangorn{} also offers the possibility to estimate ancestral states using a ML. The advantages of ML over parsimony is that the reconstruction accounts for different edge lengths. So far only a marginal construction is implemented (see \cite{Yang2006}). <>= fit = pml(tree, primates) fit = optim.pml(fit, model="F81", control = pml.control(trace=0)) @ We can assign the ancestral states according to the highest likelihood ("ml"): \[ P(x_r = A) = \frac{L(x_r=A)}{\sum_{k \in \{A,C,G,T\}}L(x_r=k)} \] and the highest posterior probability ("bayes") criterion: \[ P(x_r=A) = \frac{\pi_A L(x_r=A)}{\sum_{k \in \{A,C,G,T\}}\pi_k L(x_r=k)}, \] where $L(x_r)$ is the joint probability of states at the tips and the state at the root $x_r$ and $\pi_i$ are the estimated base frequencies of state $i$. Both methods agree if all states (base frequencies) have equal probabilities. <>= anc.ml = ancestral.pml(fit, "ml") anc.bayes = ancestral.pml(fit, "bayes") @ The differences of the two approaches for a specific site (17) are represented in figure\ref{fig:MLB}. <>= par(mfrow=c(2,1)) plotAnc(tree, anc.ml, 17) title("ML") plotAnc(tree, anc.bayes, 17) title("Bayes") @ \begin{figure} \begin{center} <>= <> @ \caption{Ancestral reconstruction for fig.\ref{fig:Pars} the using the maximum likelihood and (empirical) Bayesian reconstruction.}\label{fig:MLB} \end{center} \end{figure} \bibliographystyle{plain} \bibliography{phangorn} \section{Session Information} The version number of \R{} and packages loaded for generating the vignette were: <>= toLatex(sessionInfo()) @ \end{document} phangorn/vignettes/phangorn-specials.Rnw0000644000175100001440000003515312761653173020243 0ustar hornikusers%\VignetteIndexEntry{Advanced features} %\VignetteKeywords{Documentation} %\VignettePackage{phangorn} %\VignetteEngine{Sweave} \documentclass[12pt]{article} % setwd("/home/kschliep/Desktop/phangorn/vignettes") % Sweave("phangorn-specials.Rnw") % tools::texi2dvi("phangorn-specials.tex", pdf=TRUE) \usepackage{times} \usepackage{hyperref} \usepackage{amsmath} \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rmethod}[1]{{\texttt{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \textwidth=6.2in \textheight=8.5in %\parskip=.3cm \oddsidemargin=.1in \evensidemargin=.1in \headheight=-.3in \newcommand{\R}{\textsf{R}} \newcommand{\pml}{\Robject{pml}} \newcommand{\phangorn}{\Rpackage{phangorn}} \newcommand{\ape}{\Rpackage{ape}} \newcommand{\multicore}{\Rpackage{multicore}} \newcommand{\term}[1]{\emph{#1}} \newcommand{\mref}[2]{\htmladdnormallinkfoot{#2}{#1}} \begin{document} \SweaveOpts{concordance=TRUE} % Ross Ihakas extenstion for nicer representation \DefineVerbatimEnvironment{Sinput}{Verbatim} {xleftmargin=2em} \DefineVerbatimEnvironment{Soutput}{Verbatim}{xleftmargin=2em} \DefineVerbatimEnvironment{Scode}{Verbatim}{xleftmargin=2em} \fvset{listparameters={\setlength{\topsep}{0pt}}} \renewenvironment{Schunk}{\vspace{\topsep}}{\vspace{\topsep}} <>= options(width=70) options("show.signif.stars" = FALSE) foo <- packageDescription("phangorn") @ \title{Special features of phangorn (Version \Sexpr{foo$Version})} %$ \author{\mref{mailto:klaus.schliep@gmail.com}{Klaus P. Schliep}} \maketitle \nocite{Paradis2012} \section*{Introduction} This document illustrates some of the \phangorn{} \cite{Schliep2011} specialised features which are useful but maybe not as well-known or just not (yet) described elsewhere. This is mainly interesting for someone who wants to explore different models or set up some simulation studies. We show how to construct data objects for different character states other than nucleotides or amino acids or how to set up different models to estimate transition rate. The vignette \emph{Trees} describes in detail how to estimate phylogenies from nucleotide or amino acids. \section{User defined data formats}\label{sec:USER} To better understand how to define our own data type it is useful to know a bit more about the internal representation of \Robject{phyDat} objects. The internal representation of \Robject{phyDat} object is very similar to \Robject{factor} objects. As an example we will show here several possibilities to define nucleotide data with gaps defined as a fifth state. Ignoring gaps or coding them as ambiguous sites - as it is done in most programs, also in phangorn as default - may be misleading (see Warnow(2012)\cite{Warnow2012}). When the number of gaps is low and the gaps are missing at random coding gaps as separate state may be not important. Let assume we have given a matrix where each row contains a character vector of a taxonomical unit: <>= library(phangorn) data = matrix(c("r","a","y","g","g","a","c","-","c","t","c","g", "a","a","t","g","g","a","t","-","c","t","c","a", "a","a","t","-","g","a","c","c","c","t","?","g"), dimnames = list(c("t1", "t2", "t3"),NULL), nrow=3, byrow=TRUE) data @ Normally we would transform this matrix into an phyDat object and gaps are handled as ambiguous character like "?". <<>>= gapsdata1 = phyDat(data) gapsdata1 @ Now we will define a "USER" defined object and have to supply a vector levels of the character states for the new data, in our case the for nucleotide states and the gap. Additional we can define ambiguous states which can be any of the states. <>= gapsdata2 = phyDat(data, type="USER", levels=c("a","c","g","t","-"), ambiguity = c("?", "n")) gapsdata2 @ This is not yet what we wanted as two sites of our alignment, which contain the ambiguous characters "r" and "y", got deleted. To define ambiguous characters like "r" and "y" explicitly we have to supply a contrast matrix similar to contrasts for factors. <>= contrast = matrix(data = c(1,0,0,0,0, 0,1,0,0,0, 0,0,1,0,0, 0,0,0,1,0, 1,0,1,0,0, 0,1,0,1,0, 0,0,0,0,1, 1,1,1,1,0, 1,1,1,1,1), ncol = 5, byrow = TRUE) dimnames(contrast) = list(c("a","c","g","t","r","y","-","n","?"), c("a", "c", "g", "t", "-")) contrast gapsdata3 = phyDat(data, type="USER", contrast=contrast) gapsdata3 @ Here we defined "n" as a state which can be any nucleotide but not a gap "-" and "?" can be any state including a gap. These data can be used in all functions available in \phangorn{} to compute distance matrices or perform parsimony and maximum likelihood analysis. \section{Markov models of character evolution} To model nucleotide substitutions across the edges of a tree T we can assign a transition matrix. In the case of nucleotides, with four character states, each 4 $\times$ 4 transition matrix has, at most, 12 free parameters. Time-reversible Markov models are used to describe how characters change over time, and use fewer parameters. Time-reversible means that these models need not be directed in time, and the Markov property states that these models depend only on the current state. These models are used in analysis of phylogenies using maximum likelihood and MCMC, computing pairwise distances, as well in simulating sequence evolution. We will now describe the General Time-Reversible (GTR) model \cite{Tavare1986}. The parameters of the GTR model are the equilibrium frequencies $\pi = (\pi_A ,\pi_C ,\pi_G ,\pi_T)$ and a rate matrix $Q$ which has the form \begin{equation}\label{eq:GTR} Q = \begin{pmatrix} \ast & \alpha\pi_C & \beta\pi_G & \gamma\pi_T \\ \alpha\pi_A & \ast & \delta\pi_G & \epsilon\pi_T \\ \beta\pi_A & \delta\pi_C & \ast & \eta\pi_T \\ \gamma\pi_A & \epsilon\pi_C & \eta\pi_G & \ast \\ \end{pmatrix} \end{equation} where we need to assign 6 paramters $\alpha, \dots, \eta$. The elements on the diagonal are chosen so that the rows sum to zero. The Jukes-Cantor (JC) \cite{Jukes1969} model can be derived as special case from the GTR model, for equal equilibrium frequencies $\pi_A = \pi_C = \pi_G = \pi_T = 0.25$ and equal rates set to $\alpha = \beta = \gamma = \delta = \eta$. Table \ref{models} lists all built-in nucleotide models in \phangorn. The transition probabilities which describe the probabilities of change from character $i$ to $j$ in time $t$, are given by the corresponding entries of the matrix exponential \[ P(t) = (p_{ij}(t)) = e^{Qt}, \qquad \sum_j p_{ij}=1 \] where $P(t)$ is the transition matrix spanning a period of time $t$. \section{Estimation of non-standard transition rate matrices} In the last section \ref{sec:USER} we described how to set up user defined data formats. Now we describe how to estimate transition matrices with pml. Again for nucleotide data the most common models can be called directly in the \Rfunction{optim.pml} function (e.g. "JC69", "HKY", "GTR" to name a few). Table \ref{models} lists all the available nucleotide models, which can estimated directly in \Rfunction{optim.pml}. For amino acids several transition matrices are available ("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU", "Blossum62", "Dayhoff\_DCMut" and "JTT-DCMut") or can be estimated with \Rfunction{optim.pml}. For example Mathews et al. (2010) \cite{Mathews2010} used this function to estimate a phytochrome amino acid transition matrix. We will now show how to estimate a rate matrix with different transition ($\alpha$) and transversion ratio ($\beta$) and a fixed rate to the gap state ($\gamma$) - a kind of Kimura two-parameter model (K81) for nucleotide data with gaps as fifth state (see table \ref{gaps}). \begin{table}[htbp] \centering \begin{tabular}{l|lllll} & a & c & g & t & - \\ \hline a & & & & & \\ c & $\beta$ & & & & \\ g & $\alpha$ & $\beta$ & & & \\ t & $\beta$ & $\alpha$ & $\beta$ & & \\ - & $\gamma$ & $\gamma$ & $\gamma$ & $\gamma$ & \\ \end{tabular} \caption{Rate matrix K to optimise. }\label{gaps} \end{table} If we want to fit a non-standard transition rate matrices, we have to tell \Rfunction{optim.pml}, which transitions in Q get the same rate. The parameter vector subs accepts a vector of consecutive integers and at least one element has to be zero (these gets the reference rate of 1). Negative values indicate that there is no direct transition is possible and the rate is set to zero. <<>>= tree = unroot(rtree(3)) fit = pml(tree, gapsdata3) fit = optim.pml(fit, optQ=TRUE, subs=c(1,0,1,2,1,0,2,1,2,2), control=pml.control(trace=0)) fit @ Here are some conventions how the models are estimated: \\ If a model is supplied the base frequencies bf and rate matrix Q are optimised according to the model (nucleotides) or the adequate rate matrix and frequencies are chosen (for amino acids). If optQ=TRUE and neither a model or subs are supplied than a symmetric (optBf=FALSE) or reversible model (optBf=TRUE, i.e. the GTR for nucleotides) is estimated. This can be slow if the there are many character states, e.g. for amino acids. \begin{table}[htbp] \centering \begin{tabular}{|llllr|} \hline model & optQ & optBf & subs & df \\ \hline JC & FALSE & FALSE & $c(0, 0, 0, 0, 0, 0)$ & 0 \\ F81 & FALSE & TRUE & $c(0, 0, 0, 0, 0, 0)$ & 3 \\ K80 & TRUE & FALSE & $c(0, 1, 0, 0, 1, 0)$ & 1 \\ HKY & TRUE & TRUE & $c(0, 1, 0, 0, 1, 0)$ & 4 \\ TrNe & TRUE & FALSE & $c(0, 1, 0, 0, 2, 0)$ & 2 \\ TrN & TRUE & TRUE & $c(0, 1, 0, 0, 2, 0)$ & 5 \\ TPM1 & TRUE & FALSE & $c(0, 1, 2, 2, 1, 0)$ & 2 \\ K81 & TRUE & FALSE & $c(0, 1, 2, 2, 1, 0)$ & 2 \\ TPM1u & TRUE & TRUE & $c(0, 1, 2, 2, 1, 0)$ & 5 \\ TPM2 & TRUE & FALSE & $c(1, 2, 1, 0, 2, 0)$ & 2 \\ TPM2u & TRUE & TRUE & $c(1, 2, 1, 0, 2, 0)$ & 5 \\ TPM3 & TRUE & FALSE & $c(1, 2, 0, 1, 2, 0)$ & 2 \\ TPM3u & TRUE & TRUE & $c(1, 2, 0, 1, 2, 0)$ & 5 \\ TIM1e & TRUE & FALSE & $c(0, 1, 2, 2, 3, 0)$ & 3 \\ TIM1 & TRUE & TRUE & $c(0, 1, 2, 2, 3, 0)$ & 6 \\ TIM2e & TRUE & FALSE & $c(1, 2, 1, 0, 3, 0)$ & 3 \\ TIM2 & TRUE & TRUE & $c(1, 2, 1, 0, 3, 0)$ & 6 \\ TIM3e & TRUE & FALSE & $c(1, 2, 0, 1, 3, 0)$ & 3 \\ TIM3 & TRUE & TRUE & $c(1, 2, 0, 1, 3, 0)$ & 6 \\ TVMe & TRUE & FALSE & $c(1, 2, 3, 4, 2, 0)$ & 4 \\ TVM & TRUE & TRUE & $c(1, 2, 3, 4, 2, 0)$ & 7 \\ SYM & TRUE & FALSE & $c(1, 2, 3, 4, 5, 0)$ & 5 \\ GTR & TRUE & TRUE & $c(1, 2, 3, 4, 5, 0)$ & 8 \\ \hline \end{tabular} \caption{DNA models available in phangorn, how they are defined and number of parameters to estimate. The elements of the vector subs correspond to $\alpha, \dots, \eta$ in equation (\ref{eq:GTR})}\label{models} \end{table} \section{Codon substitution models} A special case of the transition rates are codon models. \phangorn{} now offers the possibility to estimate the $d_N/d_S$ ratio (sometimes called ka/ks), for an overview see \cite{Yang2014}. These functions extend the option to estimates the $d_N/d_S$ ratio for pairwise sequence comparison as it is available through the function \Rfunction{kaks} in \Rpackage{seqinr}. The transition rate between between codon $i$ and $j$ is defined as follows: \begin{eqnarray} q_{ij}=\left\{ \begin{array}{l@{\quad}l} 0 & \textrm{if i and j differ in more than one position} \\ \pi_j & \textrm{for synonymous transversion} \\ \pi_j\kappa & \textrm{for synonymous transition} \\ \pi_j\omega & \textrm{for non-synonymous transversion} \\ \pi_j\omega\kappa & \textrm{for non synonymous transition} \end{array} \right. \nonumber \end{eqnarray} where $\omega$ is the $d_N/d_S$ ratio, $\kappa$ the transition transversion ratio and $\pi_j$ is the the equilibrium frequencies of codon $j$. For $\omega\sim1$ the an amino acid change is neutral, for $\omega < 1$ purifying selection and $\omega > 1$ positive selection. There are four models available: "codon0", where both parameter $\kappa$ and $\omega$ are fixed to 1, "codon1" where both parameters are estimated and "codon2" or "codon3" where $\kappa$ or $\omega$ is fixed to 1. We compute the $d_N/d_S$ for some sequences given a tree using the ML functions \Rfunction{pml} and \Rfunction{optim.pml}. First we have to transform the the nucleotide sequences into codons (so far the algorithms always takes triplets). <>= library(phangorn) primates = read.phyDat("primates.dna", format="phylip", type="DNA") tree <- NJ(dist.ml(primates)) dat <- phyDat(as.character(primates), "CODON") fit <- pml(tree, dat) fit0 <- optim.pml(fit, control = pml.control(trace = 0)) fit1 <- optim.pml(fit, model="codon1", control=pml.control(trace=0)) fit2 <- optim.pml(fit, model="codon2", control=pml.control(trace=0)) fit3 <- optim.pml(fit, model="codon3", control=pml.control(trace=0)) anova(fit0, fit2, fit3, fit1) @ The models described here all assume equal frequencies for each codon (=1/61). One can optimise the codon frequencies setting the option to optBf=TRUE. As the convergence of the 61 parameters the convergence is likely slow set the maximal iterations to a higher value than the default (e.g. control = pml.control(maxit=50)). The "YN98" model \cite{Yang98} is similar to the "codon1", but addional optimises the codon frequencies. \section{Generating trees} \phangorn{} has several functions to generate tree topologies, which may are interesting for simulation studies. \Rfunction{allTrees} computes all possible bifurcating tree topologies either rooted or unrooted for up to 10 taxa. One has to keep in mind that the number of trees is growing exponentially, use \Rfunction(howmanytrees) from \ape{} as a reminder. %<>= %trees = allTrees(5) %@ <>= trees = allTrees(5) par(mfrow=c(3,5), mar=rep(0,4)) for(i in 1:15)plot(trees[[i]], cex=1, type="u") @ \begin{figure} \begin{center} <>= <> @ \end{center} \caption{all (15) unrooted trees with 5 taxa} \label{fig:NJ} \end{figure} \Rfunction{nni} returns a list of all trees which are one nearest neighbor interchange away. <>= trees = nni(trees[[1]]) @ \Rfunction{rNNI} and \Rfunction{rSPR} generate trees which are a defined number of NNI (nearest neighbor interchange) or SPR (subtree pruning and regrafting) away. \bibliographystyle{plain} \bibliography{phangorn} \section{Session Information} The version number of \R{} and packages loaded for generating the vignette were: <>= toLatex(sessionInfo()) @ \end{document} phangorn/vignettes/Networx.Rmd0000644000175100001440000001237213005450715016233 0ustar hornikusers--- title: "Splits and Networx" author: "Klaus Schliep" date: "`r format(Sys.time(), '%B %d, %Y')`" output: rmarkdown::html_vignette bibliography: phangorn.bib vignette: > %\VignetteIndexEntry{Splits and Networx} %\VignetteEngine{knitr::rmarkdown} %\usepackage[utf8]{inputenc} --- This tutorial gives a basic introduction for constructing phylogenetic networks and adding parameters to trees or networx objects using [phangorn](https://cran.r-project.org/package=phangorn) [@Schliep2011] in R. Splits graphs or phylogenetic networks are a useful way to display conflicting data or to summarize different trees. Here, we present two popular networks, consensus networks [@Holland2004] and Neighbor-Net [@Bryant2004]. Trees or networks are often missing either edge weights or edge support values. We show how to improve a tree/networx object by adding support values or estimating edge weights using non-negative Least-Squares (nnls). We first load the phangorn package and a few data sets we use in this vignette. ```{r, eval=TRUE} library(phangorn) data(Laurasiatherian) data(yeast) ``` ## consensusNet A consensusNet [@Holland2004] is a generalization of a consensus tree. Instead of only representing splits (taxon bipartitions) occurring in at least 50% of the trees in a bootstrap or MCMC sample one can use a lower threshold and explore competing splits. Note that, in its basic implementation used here, the consensusNet edge lengths are proportional to the frequence of the corresponding splits in the provided list of trees. The input for `consensusNet` is a list of trees i.e. an object of class `multiPhylo`. ```{r, eval=TRUE} set.seed(1) bs <- bootstrap.phyDat(yeast, FUN = function(x)nj(dist.hamming(x)), bs=100) tree <- nj(dist.hamming(yeast)) par("mar" = rep(1, 4)) tree <- plotBS(tree, bs, "phylogram") cnet <- consensusNet(bs, .3) plot(cnet, "2D", show.edge.label=TRUE) ``` In many cases, `consensusNet` will return more than two incompatible (competing) splits. This cannot be plotted as a planar (2-dimensional) graph. Such as situation requires a n-dimensional graph, where the maximum number of dimensions equals the maximum number of incompatible splits. For example, if we have three alternative incompatible splits: (A,B)|(C,D) vs. (A,C)|(B,D) vs. (A,D)|(B,C), we need a 3-dimensional graph to show all three alternatives. A nice way to get still a good impression of the network is to plot it in 3D. ```{r, eval=FALSE} plot(cnet) # rotate 3d plot play3d(spin3d(axis=c(0,1,0), rpm=6), duration=10) # create animated gif file movie3d(spin3d(axis=c(0,1,0), rpm=6), duration=10) ``` which will result in a spinning graph similar to this ![rotatingNetworx](movie.gif) ## neighborNet The function `neighborNet` implements the popular method of @Bryant2004. The Neighbor-Net algorithm is essentially a 2D-version of the Neighbor joining algorithm. The Neighbour-net is computed in two steps: the first computes a circular ordering of the taxa in the data set; the second step involves the estimation of edge weights using non-negative Least-Squares (nnls). ```{r, eval=TRUE} dm <- dist.hamming(yeast) nnet <- neighborNet(dm) par("mar" = rep(1, 4)) plot(nnet, "2D") ``` The advantage of Neighbor-Net is that it returns a circular split system which can be always displayed in a planar (2D) graph. The plots displayed in `phangorn` may (however) not be planar, but re-plotting can give you a planar graph. This unwanted behavior will be improved in future version. The rendering of the `networx` is done using the the fantastic igraph package [@Csardi2006]. ## Adding support values We can use the generic function `addConfidences` to add (branch) support values from a tree, i.e. an object of class `phylo` to a `networx`, `splits` or `phylo` object. The Neighbor-Net object we computed above provides no support values. We can add the support values from the tree we computed to the splits (edges) shared by both objects. ```{r, eval=TRUE} nnet <- addConfidences(nnet, tree) par("mar" = rep(1, 4)) plot(nnet, "2D", show.edge.label=TRUE) ``` Analogously, we can also add support values to a tree: ```{r, eval=TRUE} tree2 <- rNNI(tree, 2) tree2 <- addConfidences(tree2, tree) # several support values are missing par("mar" = rep(1, 4)) plot(tree2, show.node.label=TRUE) ``` ## Estimating edge weights (nnls) Consensus networks, on the other hand, provide primarily information about support values corresponding to a split, but no information about the actual difference between the taxon bipartitions defined by that split. For example, one may be interested how the alternative support values correspond with the actual genetic distance between the involved taxa. Given a distance matrix, we can estimate edge weights using non-negative Least-Squares, and plot them onto the consensusNet splits graph. ```{r, eval=TRUE} cnet <- nnls.networx(cnet, dm) par("mar" = rep(1, 4)) plot(cnet, "2D", show.edge.label=TRUE) ``` ### Import and export networks, advanced functions for networx objects The functions `read.nexus.networx` and `write.nexus.networx` can read and write nexus files for or from SplitsTree [@Huson2006]. Check-out the new vignette IntertwiningTreesAndNetworks for additional functions, examples, and advanced applications. ## References phangorn/vignettes/movie.gif0000644000175100001440000055210412761653173015744 0ustar hornikusersGIF89aôÿ÷//ï__ÞwwÖGGç¾¾¾ŽŽÎ¦¦ÆÆÆÆÎÎÎÖÖÖÞÞÞçççïïï÷÷÷ÿÿÿ!ù !ÿ NETSCAPE2.0,þ $Ždižhª®lë¾p,Ïtmßx®ï|ïÿÀ pH,ȤrÉl:ŸÐ¨tJ­Z¯Ø¬vËíz¿à°xL.›Ïè´zÍn»ßð¸|N¯Ûïø¼~Ïïûÿ€‚ƒ„…†‡ˆ‰Š‹ŒŽ‘’“”•–—˜™š›œžB¡¡Ÿ›%¦¤—¨§©–«$¯#¢¯¦£­}º( ¨¸¶¯»¼Æ" À ð¶Çƒ½¦À ʬÚÒÓÃ×Å·«ÉÝvåÃÌ Ñ'åæuèÊ×ÎÜöïðtÆ«¿ÀäÐöò9g„àuN‰(°É>'ôn‘WâA­!à€IœèN›¨u..ŒF †:vþ³”„e €k þ)Ã×"bJ-+«‰p à¥¿u Ȥ‘ÑÀÍ.Ôö-Hpíe¨KpÔ‹®‡Œ{© ML 0¨j…"VSs rFQ²e;êl7W™¦>×1¸èb*ܸ { õÀYS` B¶;öoƒsGŠ«ûtéá{U¼uŒÔFƒ°Mó%á—3—¯-X>¼`4ƦOïh à0ÐZ›_@†14vÕ a.FU÷6¨} ái»™qÉQDW$mp®U˜©@hRºu©+á6Avéør¹ ’noñK0>Þ©ÚËÜ·›?ˆ|ç¼=Ô€?‘Ô_ÿ‰þ%ƒ`¯¨ßLáÈ„ZJ'W5SsOT.¡v)˜U”†”1Ôá„×Ð!9'þ0•Š»å¥•‹áÁ£婈Bmëì8FX­½P”Bb‘`c‹U˜$K¶ðá“d|æ$ 6Q)ƇP¥Pš–c|H_QL‚¹%qç}iæ@šWÑ5]®IFXÝ‘$Jr’±äE¤˜§Q6¨àŸf8À˜š„’ña–‰–±hqžña™‘–V6•JŠX¦gÉ©°}Z%š¢Î Lœ¥‚1œ ¤*FŸÀ 骗ΠÆI¶~ái®]„Êë#ë¯P„…*±W¬Ú*²YÀ "³XÔ í¸þNk…³ÃZ›„¯ÚJ,¤ÝJal¸S(K®Øž…´ê:Qm»EÈ›¨é‡Ï}Ê-ÍÕ¦ùrú­½r±g/²”‹«ãú pT$’££–æÚ»J«²F1ŽòfZ/pœcTkÉnVÚV@+oh¢¨ïêPGÞ{-Sé¬h7pÜÜ^%\óÒ ¦Š¹ZhA•¸_1î­%϶=5 ´À-&Z¼ðdZSIäÉ×t5šÑ¶-pl».„Áä5·ŽSä2Ý0ä6BZwéÝTÕ€Eœ „_'wâ7À ® «â ¹ ê,Ë‚:”^>çþ.¬Ú¹ç1ˆîs“§Ð°ê3¼¾‚Ý°Ï@Ï×(^»‘]C}^F“ï.¥áz}-¹ðÓS¸Þ^çŒü •ÎÀò†þ¼ »–ÐøÑ׳ý äíg÷¹ïB‚u’oÂ÷ï«~ö&™¯>ìËÐf¶žÃC ê×ÿ¹üÏÓß Vå¦îÝ)üCži ¦ëÉz $•ðh÷‘î>Ö³`GĄ́v‚×A8+ut a>x¹ê`„çRárd×.H2Ê`!Ý`¸ ã脇ÓrÖúÕ’°àW@œt<€X® ò¡¨4غ< Š?»ÚÍ>Õ1Äq9©þ@î%*ž§ŠV$ã;ã*žA…B^äWoÌ&™g”ên\p2ÓÃ%*D>£×5078®ã‹!³£Í–¶9+x#ÀcSø0Ê°BúÔÐX³` 0Dd#IIBÆJ}艔kJ‘–ÐÈñ ‚(0Ôá”ÐÊþäq€ I®('Ižê†£å57Áò|òBÜ0¤.a÷Ècúq¾dÕî6ƒ~Aq£Ä!³N9^¶äF‹$±:¦¹nZS‘V×­x†ÆjŠÓBêäU4éNunEaÌš§ ¶óÌù³Tͼ?¹!FðÌŠš8¸×%±¨4[i•û ådð‰ m¦Š›óËÁþ+3ªQ8qÔ3S£çGOÀ±àr¤0€›mÚ‰R„Ípl©Jª7¤9O¦4=ZœdˆR•²†˜#ðhO¹¦J ’@wíŠW2¼Â6V±TˆR—G@Ó¼ÀD¤P½Ò¹ˆ±Ãøiˆ*Ýø9 Ÿ¬ï,ŸFwEr8uŽ.Øå¨ ¾ÀuxƒòÜ(W”Åðt®[Ú{R€Ô­^ˆuÁñØZ°1öusZ•êq44˜èü5©­YE϶ÊT­1ý,dƒ$ZûE¶´³û%jyŒÕÆ´®õëick±Ò–°´»­—§[€î¶ñ‚€m;¿½F2·éäè©Ê”É—|˵…^P ‹>/9 "tYPP¦]F ýgíPsÏEŠvÑä]·ûÌî¦õº> ì=ž{ÝÌ΄³ÿ°no÷Ëßþú÷¿°€LàøÀN°‚Ìà;øÁŽ°„'LáF…!ù ,ƒÿ÷//ï__ÞwwÖGGç¾¾¾ŽŽÎÆÆÆÎÎÎÖÖÖÞÞÞçççïïï÷÷÷ÿÿÿþðÉI«½8ëÍ»ÿ`(Ždižhª®lë¾p,Ïtmßx®ï|ïÿÀ pH,ȤrÉl:ŸÐ¨tJ­Z¯Ø¬vËíz¿à°xL.›Ïè´zÍn»ßð¸|N¯Ûïø¼~Ïïûÿ€‚ƒ„…†‡ˆ‰Š‹Œ2”’‰–•—ˆ™™žž‘›o¨–™«©p´® ±¦µq´ ®¸š¿Áv¨ ®¸¥ÉÊmÀÔ® Ç×Õ>Òß.ß–­¯§Ôêãà:Àì,ﻳ¾éí?å +â&3 Ò4uùxìKI›‚ÂÛà ]B&·HIPM±uþ9è2°à¢“Œø~옠=MB¦©Am®4@8¢bI™3{Ý—-ƒrŠd¡<| 8zì÷¯ç3¦Iœõ¤@WG’UAÜ$‰5+²S/S¢’Šóá‡aË ˆíV©Y FjK7CEhrg–È‹Ó@Ë Ú~>yÂfá–}%¼˜1 Ç8öõYyMT½¯üIÖ¶³ó] ?ükúÍÐ8·nC¸ðº!ÒÎþÑtä OI Ým£÷³ßRp€ü,q FÊî­.߈º‡?}—”ÉÜ\•V÷v 4·€òQÅëºi?/ãïzöïÊêƾ‘Üþ‡.c%°H)ù×Cwde ÜW¨O îÀàt÷øò/[!4_…/¤×\p,%X^† î`ŸƒÒLÀ‹+uhbŠ?Œä–aHc~PàŽTÔ¦£"IE‘I%fd+fðã’UH‡kPVÑ£9ViÅk®,EÁ“Z2©M\T†iÅXŽæ™WH'˜lZ)]™qnYXƒu^‘žayjfŸZ40& YŒä%¡U(àŠˆ^!(ŒZ1Ri‘N¡¨ŒV:Wi:ÅHzjÄXCŠš§|šúÄ£ê©ú„¡®:QQ±6Áj¨µÖØe®K\š)¯H Ú*°H€J,—–z¬¨þÒº¬ƒ>K¬Ò1kµCÜŠí»n „¯Þ¡m¸>LJ®àžËª”‘Ó"Š |X¥±ä8'/•’Z/{¸0Е³Ü»d³ûjÂ=8ňï]¦Ž 0 ŒuçNÎ"0”Ô¶@©ÝŠh3z¨jzš©à/lÚà’½<]<ð%à¯ÇCDIÅø&¨éžÌAÄ4+àÏKÙå첑¾r s¿ô¡Ba#£ÎšŽUZJ?ÆôN{¬ÀÊSGM^4®F;Ö(ÓŒ©Ïha7T$²À­jL|¦¶Ð¸ªkÁ£íÖÝñÚzŸPѯˆ¹Bià%$Û݈›`õâ6^‚Ù,9 þŒS´æå"°šÀ÷é˹ïµÀá˜;:¬züùÖh³zÒa]zfLO6ût:qÚîùæØ”6áÂc@§ß œü¡®ÚÝ>oÁ¥Î{ФõQþ›ÜO¾HrŸž ã[¯d '¿üØ[Ÿå :ûû$l¿zô+¤?:œ((çÚ—ÿI΀—ñâð—‚YåíYósÁ•Ç@èO]ü“Ãu©¾…HÞ‚K l8Ñ¥7¿`Ðk%,€°ì‚ÕJO?NXdL[·Ë ´òˆV@mlÎi”$–µ4•êÓ÷‚KUïˆEûn¢ÖÄØLO/\üN C&!|MA2”–Jg?ÕgJ¼£$ÇdíFÔy,f AngŒ‹ƒ^¡Gü¸-"mÓ™#ŸãÄ@q…;$TGP ‡P„¼Í»`Æm˜2`›œñ¯³Ì²ë¤”ÃJ†‹•ä/!!L®²VL•pàa‰-R‰µj]U9G;R1V£|¼æË%¥òÌH 1é©d*«–üúb·Ù(]Ò0d•MʨêµÀ½ØÍçRìêï ò7÷À¼ J»VÝ µ:ÁÓ—p ·TôÀ+@°Ý¼‚MŽ¡<ö cwp‰þHͪˆñÉÃ` zyLÐà0ðĵ -•ƒdð:²”Ñ€ÚN2Þ 6ô¤@É@uÕÒÏâµR  iã]¯}S³ThÍ&P güS–K:Q7­urÎc¯š1¼hÐÖ­Ig+áŽ*åÀ.5¼.þUë;æ©ŠŸ8p‘“qã €ö’¼B±åË6h6Ð4EÔÏ|²~LKB/ê>Tç áß‚×~Ä„GSž^Ú¸÷@ÚÀsp@™?V>(9oæ„d'ÝσÓ2È͈[=Êõp1ÞÖpÜ=É¿x/ª¶vA¾<’ÝÓÙ¼ËfOlíÝ ùn@ê íØUAÜéÇDQ½LþÆÞ=œEO\ù8„bÔ©` ð‘eÕ`F åPcÓƒpYÕ!üùgBU…%h"wèÄy^ÔŽMàiX`„2_&ZÔiøHOJ¤há_15XŒG"i<@špž‚R†Aa Ó¸˜å¦ —Š_~¡¤ »±U&4¦ßšfüx•p–q& ]€ec´9‚^·ñiÆ–m&(ØСh¤ˆ žŒ¢q<|EŠÆn½jéü-¶)Šjú©yŽšPUšÚP•ªJF¨®– š±ŽAe­`Þ…«°î Ƭ¾‚‘gªÁRÔžÅj¡(™É^l³[ í¨þN«Ån­Zk…mÚbQj·WT n¬Ž[E¯æJñlºRÜÊ®‡º¾ ºò6±n½LH‹oÇîÛIJþhhÀJèK0ý®ó×Péùªh¶:ˆg£|»®ÅÉ|Ì4°Ä,ð(¨Á9ôÓšýò¢,™Ķ0ŒÆ£}T¤\ ªq-³PÉÊ)·ñÌ'ûšgÏœ%[ϽÐ@,GÆÈ°ª¶Ñ³ˆ¥àÌH÷r€ÒN¥,K5ó¹›TέÜÕG½±SW= ¶Ê^Ž ÛÔ hœu¦J¯8³ŽQÆꕧ%< ÚÝ«1ЪÄdD`Øug5¦…#+Ø}4{ÝP±þì öv° þðH>çÀŸÓàZ c—^C—€«ºê4ØFìé°Ë0ZÑ(t^» “Å@…šïƒ^ ÄLË$¤.¼ iŠ0wÏ¿‹ðúò,€ AÙ¾Eý c¢ 5Ò­oo¥öAn€¨â>}õƒ”sí»¡ßB¹é—à8 „ÖA÷3Яÿ›±#]ú¬ÿ‰/Lj õîg: O/¢“¢hµ¼æå€?ï«×yx0ÁåñO¿ž¢Â‡z•.O´ †À˜` äÁ ñA!´p_  ßõ¨ Òà†<\Îzè/óql; 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ACCCCCCTCG TCTACGCTTA CCACGTCATC CCTCCCTCTC ACCCCAGCCC AACACCCTTC CACAAATCCT TAATATACGC ACCATAAATA AC ATCCCACCAA ATCACCCTCC ATCAAATCCA CAAATTACAC AACCATTAAC CC ACCCTAACAA TTTATCCCTC CCATAATCCA AAAACTCCAT AAACACAAAT TC AATACTCCAA CTCCCATAAC ACAGCATACA TAAACTCCAT AAGTTTGAAC AC ACAACGCCAA ACCCCCCTCT CATAACTCTA CAAAATACAC AATCACCAAC AC AATACATCAA ACAATTCCCC CCAATACCCA CAAACTGCAT AAGCAAACAG AC AATACATCAA ACAATTCCCC CCAATACCCA CAAACTACAT AAACAAACAA AC AATACACCAA ACAATTTTCT CCAACACCCA CAAACTGTAT AAACAAACAA AC AACATACCAA ACAATTCTCC CTAATATACA CAAACCACGC AAACAAACAA AC AGCACGCCAA GCTCTCTACC ATCAAACGCA CAACTTACAC ATACAGAACC AC AACACCCTAA GCCACCTTCC TCAAAATCCA AAACCCACAC AACCGAAACA AC AACACCTCAA TCCACCTCCC CCCAAATACA CAATTCACAC AAACAATACC AC AACATCTTGA CTCGCCTCTC TCCAAACACA CAATTCACGC AAACAACGCC AC AACACCTTAA CTCACCTTCT CCCAAACGCA CAATTCGCAC ACACAACGCC AC phangorn/README.md0000644000175100001440000000347113005450715013370 0ustar hornikusers[![Build Status](https://travis-ci.org/KlausVigo/phangorn.svg?branch=master)](https://travis-ci.org/KlausVigo/phangorn) [![CRAN Status Badge](http://www.r-pkg.org/badges/version/phangorn)](https://cran.r-project.org/package=phangorn) [![License](http://img.shields.io/badge/license-GPL%20%28%3E=%202%29-brightgreen.svg?style=flat)](http://www.gnu.org/licenses/gpl-2.0.html) [![CRAN Downloads](http://cranlogs.r-pkg.org/badges/phangorn)](https://cran.r-project.org/package=phangorn) [![Research software impact](http://depsy.org/api/package/cran/phangorn/badge.svg)](http://depsy.org/package/r/phangorn) [![codecov.io](https://codecov.io/github/KlausVigo/phangorn/coverage.svg?branch=master)](https://codecov.io/github/KlausVigo/phangorn?branch=master) phangorn ======================================================== phangorn is a package for phylogenetic reconstruction and analysis in the R language. phangorn offers the possibility of reconstructing phylogenies with distance based methods, maximum parsimony or maximum likelihood (ML) and performing Hadamard conjugation. Extending the general ML framework, this package provides the possibility of estimating mixture and partition models. Furthermore, phangorn offers several functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analyses. You can install - the latest released version `install.packages("phangorn")` - the latest development version `devtools::install_github("KlausVigo/phangorn")` You may need to install first the Biostrings package from bioconductor ``` source("https://bioconductor.org/biocLite.R") biocLite("Biostrings") ``` If you use phangorn please cite: Schliep K.P. 2011. phangorn: phylogenetic analysis in R. 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ÁæR—ÿ4žØm£tr  p¯âäVÆ× µUœ]±Þæû0Óz½þü©ˆ‡,uŠØL³áB™ó&úú Chسphangorn/DESCRIPTION0000644000175100001440000000273013020771374013620 0ustar hornikusersPackage: phangorn Title: Phylogenetic Analysis in R Version: 2.1.1 Date: 2016-12-03 Authors@R: c(person("Klaus", "Schliep", email="klaus.schliep@gmail.com", role = c("aut", "cre")), person("Emmanuel", "Paradis", role = c("aut")), person("Leonardo", "de Oliveira Martins", role = c("aut")), person("Alastair", "Potts", role = c("aut")), person("Tim W.", "White", role = c("aut")), person("Cyrill", "Stachniss", role = c("ctb")), person("Michelle", "Kendall", email="m.kendall@imperial.ac.uk", role = c("ctb"))) Maintainer: Klaus Schliep Description: Phylogenetic analysis in R: Estimation of phylogenetic trees and networks using Maximum Likelihood, Maximum Parsimony, distance methods and Hadamard conjugation. Depends: R (>= 3.2.0), ape (>= 4.0) Imports: quadprog, igraph (>= 1.0), Matrix, parallel, methods, utils, stats, graphics, grDevices, fastmatch, magrittr, Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: testthat, seqLogo, seqinr, xtable, flashClust, rgl, knitr, rmarkdown, Biostrings ByteCompile: TRUE License: GPL (>= 2) VignetteBuilder: utils, knitr URL: https://github.com/KlausVigo/phangorn Repository: CRAN NeedsCompilation: yes Packaged: 2016-12-03 22:25:47 UTC; klaus Author: Klaus Schliep [aut, cre], Emmanuel Paradis [aut], Leonardo de Oliveira Martins [aut], Alastair Potts [aut], Tim W. White [aut], Cyrill Stachniss [ctb], Michelle Kendall [ctb] Date/Publication: 2016-12-04 11:42:04 phangorn/man/0000755000175100001440000000000013005450715012657 5ustar hornikusersphangorn/man/Laurasiatherian.Rd0000644000175100001440000000065312761653173016301 0ustar hornikusers\name{Laurasiatherian} \alias{Laurasiatherian} \docType{data} \title{ Laurasiatherian data (AWCMEE)} \description{ Laurasiatherian RNA sequence data } \usage{data(Laurasiatherian)} \source{ Data have been taken from \url{http://www.allanwilsoncentre.ac.nz/} and were converted to R format by \email{klaus.schliep@gmail.com}. } \examples{ data(Laurasiatherian) str(Laurasiatherian) } \keyword{datasets} phangorn/man/cophenetic.networx.Rd0000644000175100001440000000137612761653173017016 0ustar hornikusers\name{cophenetic.networx} \alias{cophenetic.networx} \alias{cophenetic.splits} \title{Pairwise Distances from a Phylogenetic Network} \usage{ \method{cophenetic}{networx}(x) \method{cophenetic}{splits}(x) } \arguments{ \item{x}{an object of class \code{"networx"}.} } \description{ \code{cophenetic.networx} computes the pairwise distances between the pairs of tips from a phylogenetic network using its branch lengths. } \value{ an object of class \code{dist}, names are set according to the tip labels (as given by the element \code{tip.label} of the argument \code{x}). } \author{Klaus Schliep} \seealso{ \code{\link[stats]{cophenetic}} for the generic function, \code{neighborNet} to construct a network from a distance matrix } \keyword{manip} phangorn/man/addConfidences.Rd0000644000175100001440000000372412761653173016060 0ustar hornikusers\name{addConfidences} \alias{addConfidences} \alias{presenceAbsence} \alias{createLabel} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Compare splits and add support values to an object } \description{ Add support values to a \code{splits}, \code{phylo} or \code{networx} object. } \usage{ addConfidences(x, y, ...) presenceAbsence(x, y) createLabel(x, y, label_y, type = "edge", nomatch = NA) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ an object of class \code{splits}, \code{phylo} or \code{networx} } \item{y}{ an object of class \code{splits}, \code{phylo}, \code{multiPhylo} or \code{networx} } \item{...}{ Further arguments passed to or from other methods. } \item{label_y}{ label of y matched on x. Will be usually of length(as.splits(x)). } \item{type}{ should labels returned for edges (in \code{networx}) or splits. } \item{nomatch}{ default value if no match between x and y is found. } } %%\details{ %% ~~ If necessary, more details than the description above ~~ %%} \value{ The object \code{x} with added bootstrap / MCMC support values. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} %%\note{ %% ~~further notes~~ %%} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{as.splits}}, \code{\link{as.networx}}, \code{\link{RF.dist}}, \code{\link{plot.phylo}} } \examples{ data(woodmouse) woodmouse <- phyDat(woodmouse) tmpfile <- normalizePath(system.file("extdata/trees/RAxML_bootstrap.woodmouse", package="phangorn")) boot_trees <- read.tree(tmpfile) dm <- dist.ml(woodmouse) tree <- upgma(dm) nnet <- neighborNet(dm) tree <- addConfidences(tree, boot_trees) nnet <- addConfidences(nnet, boot_trees) plot(tree, show.node.label=TRUE) plot(nnet, "2D", show.edge.label=TRUE) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } %%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line phangorn/man/dist.p.Rd0000644000175100001440000000633113005450715014352 0ustar hornikusers\name{dist.p} \alias{dist.p} %- Also NEED an '\alias' for EACH other topic documented here. \title{Pairwise Polymorphism P-Distances from DNA Sequences} \description{ This function computes a matrix of pairwise uncorrected polymorphism p-distances. Polymorphism p-distances include intra-individual site polymorphisms (2ISPs; e.g. "R") when calculating genetic distances. } \usage{ dist.p(x, cost="polymorphism", ignore.indels=TRUE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{a matrix containing DNA sequences; this must be of class "phyDat" (use as.phyDat to convert from DNAbin objects).} \item{cost}{A cost matrix or "polymorphism" for a predefined one.} \item{ignore.indels}{a logical indicating whether gaps are treated as fifth state or not. Warning, each gap site is treated as a characters, so an an indel that spans a number of base positions would be treated as multiple character states.} } \details{ The polymorphism p-distances (Potts et al. 2014) have been developed to analyse intra-individual variant polymorphism. For example, the widely used ribosomal internal transcribed spacer (ITS) region (e.g. Alvarez and Wendel, 2003) consists of 100's to 1000's of units within array across potentially multiple nucleolus organising regions (Bailey et al., 2003; Goeker and Grimm, 2008). This can give rise to intra-individual site polymorphisms (2ISPs) that can be detected from direct-PCR sequencing or cloning . Clone consensus sequences (see Goeker and Grimm, 2008) can be analysed with this function. } \value{ an object of class \code{dist}. } \references{Alvarez, I., and J. F. Wendel. (2003) Ribosomal ITS sequences and plant phylogenetic inference. \emph{ Molecular Phylogenetics and Evolution}, \bold{29}, 417--434. Bailey, C. D., T. G. Carr, S. A. Harris, and C. E. Hughes. (2003) Characterization of angiosperm nrDNA polymorphism, paralogy, and pseudogenes. \emph{Molecular Phylogenetics and Evolution} \bold{29}, 435--455. Goeker, M., and G. Grimm. (2008) General functions to transform associate data to host data, and their use in phylogenetic inference from sequences with intra-individual variability. \emph{BMC Evolutionary Biology}, \bold{8}:86. Potts, A.J., T.A. Hedderson, and G.W. Grimm. (2014) Constructing phylogenies in the presence of intra-individual site polymorphisms (2ISPs) with a focus on the nuclear ribosomal cistron. \emph{Systematic Biology}, \bold{63}, 1--16 } \author{Klaus Schliep and Alastair Potts} \seealso{ \code{\link[ape]{dist.dna}}, \code{\link[phangorn]{dist.hamming}}} \examples{ data(Laurasiatherian) laura = as.DNAbin(Laurasiatherian) dm <- dist.p(Laurasiatherian, "polymorphism") ######################################################## # Dealing with indel 2ISPs # These can be coded using an "x" in the alignment. Note # that as.character usage in the read.dna() function. ######################################################### cat("3 5", "No305 ATRA-", "No304 ATAYX", "No306 ATAGA", file = "exdna.txt", sep = "\n") (ex.dna <- read.dna("exdna.txt", format = "sequential", as.character=TRUE)) dat= phyDat(ex.dna, "USER", levels=unique(as.vector(ex.dna))) dist.p(dat) } \keyword{ cluster }% at least one, from doc/KEYWORDS phangorn/man/phangorn-package.Rd0000644000175100001440000000303012761653173016362 0ustar hornikusers\name{phangorn-package} \alias{phangorn-package} \alias{phangorn} \docType{package} \title{ Phylogenetic analysis in R } \description{ Phylogenetic analysis in R (Estimation of phylogenetic trees and networks using Maximum Likelihood, Maximum Parsimony, Distance methods & Hadamard conjugation) The complete list of functions can be displayed with \code{library(help = phangorn)}. Further information is available in several vignettes. To show the available vignettes in an HTML browser type \code{browseVignettes("phangorn")}. \tabular{ll}{ \code{Trees} \tab Constructing phylogenetic trees (source, pdf) \cr \code{phangorn-specials} \tab Advanced features (source, pdf) \cr \code{Ancestral} \tab Ancestral sequence reconstruction (source, pdf) \cr \code{Networx} \tab Splits and Networx (source, html) \cr } The first vignette (to display type \code{vignette('Trees')}) gives an introduction in phylogenetic analysis with phangorn. The second vignette (phangorn-special) covers more advanced feature like defining special character spaces and things which fit nowhere else. More information on \bold{phangorn} can be found on \url{http://www.phangorn.org}. } \author{ Klaus Schliep Maintainer: Klaus Schliep } \references{ Schliep K.P. (2011) phangorn: Phylogenetic analysis in R. \emph{Bioinformatics}, 27(4) 592-593 } \keyword{ package } %\seealso{ %~~ Optional links to other man pages, e.g. ~~ %~~ \code{\link[:-package]{}} ~~ %} %\examples{ %~~ simple examples of the most important functions ~~ %} phangorn/man/SOWH.test.Rd0000644000175100001440000000404113005450715014703 0ustar hornikusers\name{SOWH.test} \alias{SOWH.test} \title{Swofford-Olsen-Waddell-Hillis Test} \usage{ SOWH.test(x, n = 100, restricted = list(optNni=FALSE), optNni=TRUE, trace = 1, ...) } \arguments{ \item{x}{an object of class \code{"pml"}.} \item{n}{the number of bootstrap replicates.} \item{restricted}{list of restricted parameter settings.} \item{optNni}{Logical value indicating whether topology gets optimized (NNI).} \item{trace}{Show output during computations.} \item{\dots}{Further arguments passed to \code{"optim.pml"}.} } \description{ This function computes the Swofford--Olsen--Waddell--Hillis (SOWH) test, a parametric bootstrap test. The function is computational very demanding and likely to be very slow. } \details{ \code{SOWH.test} performs a parametric bootstrap test to compare two trees. It makes extensive use \code{simSeq} and \code{optim.pml} and can take quite long. } \value{ an object of class SOWH. That is a list with three elements, one is a matrix containing for each bootstrap replicate the (log-) likelihood of the restricted and unrestricted estimate and two pml objects of the restricted and unrestricted model. } \references{ Goldman, N., Anderson, J. P., and Rodrigo, A. G. (2000) Likelihood -based tests of topologies in phylogenetics. \emph{Systematic Biology} \bold{49} 652-670. Swofford, D.L., Olsen, G.J., Waddell, P.J. and Hillis, D.M. (1996) Phylogenetic Inference in Hillis, D.M., Moritz, C. and Mable, B.K. (Eds.) \emph{Molecular Systematics} (2nd ed.) 407-514, Sunderland, MA: Sinauer } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{pml}}, \code{\link{pmlPart}}, \code{\link{pmlCluster}}, \code{\link{simSeq}}, \code{\link{SH.test}} } \examples{ # in real analysis use larger n, e.g. 500 preferably more \dontrun{ data(Laurasiatherian) dm <- dist.logDet(Laurasiatherian) tree <- NJ(dm) fit <- pml(tree, Laurasiatherian) fit <- optim.pml(fit, TRUE) set.seed(6) tree <- rNNI(fit$tree, 1) fit <- update(fit, tree = tree) (res <- SOWH.test(fit, n=100)) summary(res) } } \keyword{models} phangorn/man/pml.Rd0000644000175100001440000002162512761653173013757 0ustar hornikusers\name{pml} \alias{pml} \alias{optim.pml} \alias{pml.control} \title{Likelihood of a tree.} \description{ \code{pml} computes the likelihood of a phylogenetic tree given a sequence alignment and a model. \code{optim.pml} optimizes the different model parameters. } \usage{ pml(tree, data, bf=NULL, Q=NULL, inv=0, k=1, shape=1, rate=1, model="", ...) optim.pml(object, optNni=FALSE, optBf=FALSE, optQ=FALSE, optInv=FALSE, optGamma=FALSE, optEdge=TRUE, optRate=FALSE, optRooted=FALSE, control = pml.control(epsilon=1e-08, maxit=10, trace=1), model = NULL, rearrangement = ifelse(optNni, "NNI","none"), subs = NULL, ratchet.par = list(iter = 20L, maxit = 100L, prop = 1/3),...) pml.control(epsilon = 1e-08, maxit = 10, trace = 1) } \arguments{ \item{tree}{A phylogenetic \code{tree}, object of class \code{phylo}. } \item{data}{An alignment, object of class \code{phyDat}.} \item{bf}{Base frequencies.} \item{Q}{A vector containing the lower triangular part of the rate matrix.} \item{inv}{Proportion of invariable sites.} \item{k}{Number of intervals of the discrete gamma distribution.} \item{shape}{Shape parameter of the gamma distribution.} \item{rate}{Rate.} \item{model}{allows to choose an amino acid models or nucleotide model, see details.} \item{object}{An object of class \code{pml}.} \item{optNni}{Logical value indicating whether toplogy gets optimized (NNI).} \item{optBf}{Logical value indicating whether base frequencies gets optimized.} \item{optQ}{Logical value indicating whether rate matrix gets optimized.} \item{optInv}{Logical value indicating whether proportion of variable size gets optimized.} \item{optGamma}{Logical value indicating whether gamma rate parameter gets optimized.} \item{optEdge}{Logical value indicating the edge lengths gets optimized.} \item{optRate}{Logical value indicating the overall rate gets optimized.} \item{optRooted}{Logical value indicating if the edge lengths of a rooted tree get optimized.} % \item{optRatchet}{Stochastic tree search.} \item{ratchet.par}{search parameter for stochastic search} \item{rearrangement}{type of tree tree rearrangements to perform, one of "none", "NNI", "stochastic" or "ratchet"} \item{control}{A list of parameters for controlling the fitting process.} \item{subs}{A (integer) vector same length as Q to specify the optimization of Q} \item{\dots}{Further arguments passed to or from other methods.} \item{epsilon}{Stop criterion for optimisation (see details).} \item{maxit}{Maximum number of iterations (see details).} \item{trace}{Show output during optimization (see details).} } \details{ The topology search uses a nearest neighbor interchange (NNI) and the implementation is similar to phyML. The option model in pml is only used for amino acid models. The option model defines the nucleotide model which is getting optimised, all models which are included in modeltest can be chosen. Setting this option (e.g. "K81" or "GTR") overrules options optBf and optQ. Here is a overview how to estimate different phylogenetic models with \code{pml}: \tabular{lll}{ model \tab optBf \tab optQ \cr Jukes-Cantor \tab FALSE \tab FALSE \cr F81 \tab TRUE \tab FALSE \cr symmetric \tab FALSE \tab TRUE \cr GTR \tab TRUE \tab TRUE } Via model in optim.pml the following nucleotide models can be specified: JC, F81, K80, HKY, TrNe, TrN, TPM1, K81, TPM1u, TPM2, TPM2u, TPM3, TPM3u, TIM1e, TIM1, TIM2e, TIM2, TIM3e, TIM3, TVMe, TVM, SYM and GTR. These models are specified as in Posada (2008). So far 17 amino acid models are supported ("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU", "Blossum62", "Dayhoff_DCMut" and "JTT_DCMut") and additionally rate matrices and amino acid frequencies can be supplied. It is also possible to estimate codon models (e.g. YN98), for details see also the chapter in vignette("phangorn-specials"). If the option 'optRooted' is set to TRUE than the edge lengths of rooted tree are optimized. The tree has to be rooted and by now ultrametric! Optimising rooted trees is generally much slower. \code{pml.control} controls the fitting process. \code{epsilon} and \code{maxit} are only defined for the most outer loop, this affects \code{pmlCluster}, \code{pmlPart} and \code{pmlMix}. \code{epsilon} is defined as (logLik(k)-logLik(k+1))/logLik(k+1), this seems to be a good heuristics which works reasonably for small and large trees or alignments. If \code{trace} is set to zero than no out put is shown, if functions are called internally than the trace is decreased by one, so a higher of trace produces more feedback. If \code{rearrangement} is set to \code{stochastic} a stochastic search algorithm similar to Nguyen et al. (2015). and for \code{ratchet} the likelihood ratchet as in Vos (2003). This should helps often to find better tree topologies, especially for larger trees. } \value{ \code{pml} or \code{optim.pml} return a list of class \code{pml}, some are useful for further computations like \item{tree}{the phylogenetic tree.} \item{data}{the alignment.} \item{logLik}{Log-likelihood of the tree.} \item{siteLik}{Site log-likelihoods.} \item{weight}{Weight of the site patterns.} } \references{ Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. \emph{Journal of Molecular Evolution}, \bold{17}, 368--376. Felsenstein, J. (2004). \emph{Inferring Phylogenies}. Sinauer Associates, Sunderland. Yang, Z. (2006). \emph{Computational Molecular evolution}. Oxford University Press, Oxford. Adachi, J., P. J. Waddell, W. Martin, and M. Hasegawa (2000) Plastid genome phylogeny and a model of amino acid substitution for proteins encoded by chloroplast DNA. \emph{Journal of Molecular Evolution}, \bold{50}, 348--358 Rota-Stabelli, O., Z. Yang, and M. Telford. (2009) MtZoa: a general mitochondrial amino acid substitutions model for animal evolutionary studies. \emph{Mol. Phyl. Evol}, \bold{52(1)}, 268--72 Whelan, S. and Goldman, N. (2001) A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. \emph{Molecular Biology and Evolution}, \bold{18}, 691--699 Le, S.Q. and Gascuel, O. (2008) LG: An Improved, General Amino-Acid Replacement Matrix \emph{Molecular Biology and Evolution}, \bold{25(7)}, 1307--1320 Yang, Z., R. Nielsen, and M. Hasegawa (1998) Models of amino acid substitution and applications to Mitochondrial protein evolution. \emph{Molecular Biology and Evolution}, \bold{15}, 1600--1611 Abascal, F., D. Posada, and R. Zardoya (2007) MtArt: A new Model of amino acid replacement for Arthropoda. \emph{Molecular Biology and Evolution}, \bold{24}, 1--5 Kosiol, C, and Goldman, N (2005) Different versions of the Dayhoff rate matrix - \emph{Molecular Biology and Evolution}, \bold{22}, 193--199 L.-T. Nguyen, H.A. Schmidt, A. von Haeseler, and B.Q. Minh (2015) IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. \emph{Molecular Biology and Evolution}, \bold{32}, 268--274. Vos, R. A. (2003) Accelerated Likelihood Surface Exploration: The Likelihood Ratchet. \emph{Systematic Biology}, \bold{52(3)}, 368--373 Yang, Z., and R. Nielsen (1998) Synonymous and nonsynonymous rate variation in nuclear genes of mammals. \emph{Journal of Molecular Evolution}, \bold{46}, 409-418. Lewis, P.O. (2001) A likelihood approach to estimating phylogeny from discrete morphological character data. \emph{Systematic Biology} \bold{50}, 913--925. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{bootstrap.pml}}, \code{\link{modelTest}}, \code{\link{pmlPart}}, \code{\link{pmlMix}}, \code{\link{plot.phylo}}, \code{\link{SH.test}}, \code{\link{ancestral.pml}} } % \note{For small trees the likelihood seems to be very similar to Paup* or PhyML.} \examples{ example(NJ) # Jukes-Cantor (starting tree from NJ) fitJC <- pml(tree, Laurasiatherian) # optimize edge length parameter fitJC <- optim.pml(fitJC) fitJC \dontrun{ # search for a better tree using NNI rearrangements fitJC <- optim.pml(fitJC, optNni=TRUE) fitJC plot(fitJC$tree) # JC + Gamma + I - model fitJC_GI <- update(fitJC, k=4, inv=.2) # optimize shape parameter + proportion of invariant sites fitJC_GI <- optim.pml(fitJC_GI, optGamma=TRUE, optInv=TRUE) # GTR + Gamma + I - model fitGTR <- optim.pml(fitJC_GI, rearrangement = "stochastic", optGamma=TRUE, optInv=TRUE, model="GTR") } # 2-state data (RY-coded) dat <- acgt2ry(Laurasiatherian) fit2ST <- pml(tree, dat) fit2ST <- optim.pml(fit2ST,optNni=TRUE) fit2ST # show some of the methods available for class pml methods(class="pml") } \keyword{ cluster }% at least one, from doc/KEYWORDS phangorn/man/neighborNet.Rd0000644000175100001440000000261012761653173015424 0ustar hornikusers\name{neighborNet} \alias{neighborNet} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Computes a neighborNet from a distance matrix } \description{ Computes a neighborNet, i.e. an object of class \code{networx} from a distance matrix. } \usage{ neighborNet(x, ord = NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{a distance matrix.} \item{ord}{a circular ordering.} } \details{ \code{neighborNet} is still experimental. The cyclic ordering sometimes differ from the SplitsTree implementation, the \emph{ord} argument can be used to enforce a certain circular ordering. } \value{ \code{neighborNet} returns an object of class networx. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} %% ~Make other sections like Warning with \section{Warning }{....} ~ \references{ Bryant, D. & Moulton, V. (2004) Neighbor-Net: An Agglomerative Method for the Construction of Phylogenetic Networks. \emph{Molecular Biology and Evolution}, 2004, \bold{21}, 255-265 } \seealso{ \code{\link{splitsNetwork}}, \code{\link{consensusNet}}, \code{\link{plot.networx}}, \code{\link{lento}}, \code{\link{cophenetic.networx}}, \code{\link{distanceHadamard}} } \examples{ data(yeast) dm <- dist.ml(yeast) nnet <- neighborNet(dm) plot(nnet, "2D") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ hplot } phangorn/man/midpoint.Rd0000644000175100001440000000323413005450715014773 0ustar hornikusers\name{midpoint} \alias{midpoint} \alias{pruneTree} \alias{getRoot} %- Also NEED an '\alias' for EACH other topic documented here. \title{Tree manipulation} \description{ \code{midpoint} performs midpoint rooting of a tree. \code{pruneTree} produces a consensus tree. } \usage{ midpoint(tree, node.labels = "support") pruneTree(tree, ..., FUN = ">=") getRoot(tree) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{tree}{an object of class \code{phylo} } \item{FUN}{a function evaluated on the nodelabels, result must be logical.} \item{node.labels}{are nodel labels 'support' values, 'label' or should be 'deleted'} \item{\dots}{further arguments, passed to other methods. } } \details{ \code{pruneTree} prunes back a tree and produces a consensus tree, for trees already containing nodelabels. It assumes that nodelabels are numerical or character that allows conversion to numerical, it uses as.numeric(as.character(tree$node.labels)) to convert them. \code{midpoint} so far does not transform node.labels properly. } \value{ \code{pruneTree} and \code{midpoint} a tree. \code{getRoot} returns the root node. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{ \code{\link[ape]{consensus}}, \code{\link[ape]{root}}, \code{\link[ape]{di2multi}} } \examples{ tree = unroot(rtree(10)) tree$node.label = c("", round(runif(tree$Nnode-1), 3)) tree2 = midpoint(tree) tree3 = pruneTree(tree, .5) par(mfrow = c(3,1)) plot(tree, show.node.label=TRUE) plot(tree2, show.node.label=TRUE) plot(tree3, show.node.label=TRUE) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } phangorn/man/splitsNetwork.Rd0000644000175100001440000000360212761653173016052 0ustar hornikusers\name{splitsNetwork} \alias{splitsNetwork} \title{Phylogenetic Network} \description{ \code{splitsNetwork} estimates weights for a splits graph from a distance matrix. } \usage{ splitsNetwork(dm, splits=NULL, gamma=.1, lambda=1e-6, weight=NULL) } \arguments{ \item{dm}{A distance matrix.} \item{splits}{a splits object, containing all splits to consider, otherwise all possible splits are used} \item{gamma}{penalty value for the L1 constraint.} \item{lambda}{penalty value for the L2 constraint.} \item{weight}{a vector of weights.} } \details{ \code{splitsNetwork} fits non-negative least-squares phylogenetic networks using L1 (LASSO), L2(ridge regression) constraints. The function minimizes the penalized least squares \deqn{\beta = min \sum(dm - X\beta)^2 + \lambda \|\beta \|^2_2 }{% beta = sum(dm - X*beta)^2 + lambda |beta|^2_2 } with respect to \deqn{\|\beta \|_1 <= \gamma, \beta >= 0}{% |beta|_1 = gamma, beta >= 0} where X is a design matrix constructed with \code{designSplits}. External edges are fitted without L1 or L2 constraints. } \value{ \code{splitsNetwork} returns a splits object with a matrix added. The first column contains the indices of the splits, the second column an unconstrained fit without penalty terms and the third column the constrained fit. } \references{ Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics K. P. Schliep (2009). Some Applications of statistical phylogenetics (PhD Thesis) } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{\code{\link[phangorn]{distanceHadamard}}, \code{\link[phangorn]{designTree}} \code{\link[phangorn]{consensusNet}}, \code{\link[phangorn]{plot.networx}} } \examples{ data(yeast) dm = dist.ml(yeast) fit = splitsNetwork(dm) net = as.networx(fit) plot(net, "2D") write.nexus.splits(fit) } \keyword{ cluster }% at least one, from doc/KEYWORDS phangorn/man/phyDat.Rd0000644000175100001440000001262013005450715014400 0ustar hornikusers\name{phyDat} \alias{phyDat} \alias{as.AAbin.phyDat} \alias{as.phyDat.DNAbin} \alias{as.phyDat.alignment} \alias{as.phyDat.character} \alias{as.phyDat.data.frame} \alias{as.phyDat.factor} \alias{as.phyDat.matrix} \alias{as.phyDat.MultipleAlignment} \alias{as.MultipleAlignment} \alias{as.MultipleAlignment.phyDat} \alias{as.data.frame.phyDat} \alias{as.character.phyDat} \alias{as.DNAbin.phyDat} \alias{read.phyDat} \alias{write.phyDat} \alias{allSitePattern} \alias{as.phyDat} \alias{subset.phyDat} \alias{acgt2ry} \alias{baseFreq} \alias{cbind.phyDat} \alias{c.phyDat} \alias{unique.phyDat} \alias{removeUndeterminedSites} \alias{phyDat2alignment} \alias{phyDat2MultipleAlignment} \alias{image.phyDat} \alias{dna2codon} \alias{codon2dna} \title{Conversion among Sequence Formats} \description{ These functions transform several DNA formats into the \code{phyDat} format. \code{allSitePattern} generates an alignment of all possible site patterns. } \usage{ phyDat(data, type = "DNA", levels = NULL, return.index=TRUE, ...) read.phyDat(file, format="phylip", type="DNA", ...) write.phyDat(x, file, format="phylip", colsep = "", nbcol=-1, ...) \method{as.phyDat}{DNAbin}(x, ...) \method{as.phyDat}{alignment}(x, type="DNA", ...) \method{as.phyDat}{character}(x, ...) \method{as.phyDat}{data.frame}(x, ...) \method{as.phyDat}{factor}(x, ...) \method{as.phyDat}{matrix}(x, ...) \method{as.phyDat}{MultipleAlignment}(x, ...) %\method{as.AAbin}{phyDat}(x, ...) \method{as.character}{phyDat}(x, allLevels = TRUE, ...) \method{as.data.frame}{phyDat}(x, ...) \method{as.DNAbin}{phyDat}(x, ...) \method{subset}{phyDat}(x, subset, select, site.pattern = TRUE, ...) \method{unique}{phyDat}(x, incomparables = FALSE, identical = TRUE, ...) phyDat2alignment(x) allSitePattern(n, levels=c("a","c","g","t"), names=NULL) acgt2ry(obj) baseFreq(obj, freq=FALSE, all=FALSE, drop.unused.levels=FALSE) dna2codon(x) codon2dna(x) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{data}{An object containing sequences.} \item{x}{An object containing sequences.} \item{type}{Type of sequences ("DNA", "AA", "CODON" or "USER").} \item{levels}{Level attributes.} \item{return.index}{If TRUE returns a index of the site patterns.} \item{file}{A file name.} \item{format}{File format of the sequence alignment (see details). Several popular formats are supported: "phylip", "interleaved", "sequential", "clustal", "fasta" or "nexus", or any unambiguous abbreviation of these. } \item{colsep}{a character used to separate the columns (a single space by default).} \item{nbcol}{a numeric specifying the number of columns per row (-1 by default); may be negative implying that the nucleotides are printed on a single line.} \item{n}{Number of sequences.} \item{names}{Names of sequences.} \item{subset}{a subset of taxa.} \item{select}{a subset of characters.} \item{site.pattern}{select site pattern or sites.} \item{allLevels}{return original data.} \item{obj}{as object of class phyDat} \item{freq}{logical, if 'TRUE', frequencies or counts are returned otherwise proportions} \item{all}{all a logical; if all = TRUE, all counts of bases, ambiguous codes, missing data, and alignment gaps are returned as defined in the contrast.} \item{drop.unused.levels}{logical, drop unused levels} \item{incomparables}{for compatibility with unique.} \item{identical}{if TRUE (default) sequences have to be identical, if FALSE sequences are considered duplicates if distance between sequences is zero (happens frequently with ambiguous sites).} \item{...}{further arguments passed to or from other methods.} } \details{ If \code{type} "USER" a vector has to be give to \code{levels}. For example c("a", "c", "g", "t", "-") would create a data object that can be used in phylogenetic analysis with gaps as fifth state. There is a more detailed example for specifying "USER" defined data formats in the vignette "phangorn-specials". \code{allSitePattern} returns all possible site patterns and can be useful in simulation studies. For further details see the vignette phangorn-specials. \code{write.phyDat} calls the function write.dna or write.nexus.data and \code{read.phyDat} calls the function \code{read.dna}, \code{read.aa} or \code{read.nexus.data} see for more details over there. You may import data directly with \code{\link[ape]{read.dna}} or \code{\link[ape]{read.nexus.data}} and convert the data to class phyDat. The generic function \code{c} can be used to to combine sequences and \code{unique} to get all unique sequences or unique haplotypes. \code{acgt2ry} converts a \code{phyDat} object of nucleotides into an binary ry-coded dataset. } \value{ The functions return an object of class \code{phyDat}. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{DNAbin}}, \code{\link{as.DNAbin}}, \code{\link{read.dna}}, \code{\link{read.aa}}, \code{\link{read.nexus.data}} and the chapter 1 in the \code{vignette("phangorn-specials", package="phangorn")} and the example of \code{\link{pmlMix}} for the use of \code{allSitePattern} } \examples{ data(Laurasiatherian) class(Laurasiatherian) Laurasiatherian baseFreq(Laurasiatherian) baseFreq(Laurasiatherian, all=TRUE) subset(Laurasiatherian, subset=1:5) # transform into old ape format LauraChar <- as.character(Laurasiatherian) # and back Laura <- phyDat(LauraChar, return.index=TRUE) all.equal(Laurasiatherian, Laura) allSitePattern(5) } \keyword{cluster} phangorn/man/bab.Rd0000644000175100001440000000431612761653173013711 0ustar hornikusers\name{bab} \alias{bab} \alias{BranchAndBound} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Branch and bound for finding all most parsimonious trees } \description{ \code{bab} finds all most parsimonious trees. } \usage{ bab(data, tree = NULL, trace = 1, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{data}{ an object of class phyDat. } \item{tree}{ a phylogenetic tree an object of class phylo, otherwise a pratchet search is performed. } \item{trace}{ defines how much information is printed during optimisation. } \item{\dots}{ Further arguments passed to or from other methods } } \details{ This implementation is very slow and depending on the data may take very long time. In the worst case all (2n-5)!! possible trees have to be examined. For 10 species there are already 2027025 tip-labelled unrooted trees. It only uses some basic strategies to find a lower and upper bounds similar to penny from phylip. It uses a very basic heuristic approach of MinMax Squeeze (Holland et al. 2005) to improve the lower bound. On the positive side \code{bab} is not like many other implementations restricted to binary or nucleotide data. } \value{ \code{bab} returns all most parsimonious trees in an object of class \code{multiPhylo}. } \references{ Hendy, M.D. and Penny D. (1982) Branch and bound algorithms to determine minimal evolutionary trees. \emph{Math. Biosc.} \bold{59}, 277-290 Holland, B.R., Huber, K.T. Penny, D. and Moulton, V. (2005) The MinMax Squeeze: Guaranteeing a Minimal Tree for Population Data, \emph{Molecular Biology and Evolution}, \bold{22}, 235--242 White, W.T. and Holland, B.R. (2011) Faster exact maximum parsimony search with XMP. \emph{Bioinformatics}, \bold{27(10)},1359--1367 } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} based on work on Liam Revell } \seealso{ \code{\link{pratchet}}, \code{\link{dfactorial}} } \examples{ data(yeast) dfactorial(11) # choose only the first two genes gene12 <- subset(yeast, , 1:3158, site.pattern=FALSE) trees <- bab(gene12) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line phangorn/man/designTree.Rd0000644000175100001440000000405112761653173015252 0ustar hornikusers\name{designTree} \alias{designTree} \alias{designSplits} \alias{nnls.tree} \alias{nnls.phylo} \alias{nnls.splits} \alias{nnls.networx} %- Also NEED an '\alias' for EACH other topic documented here. \title{Compute a design matrix or non-negative LS } \description{ \code{nnls.tree} estimates the branch length using non-negative least squares given a tree and a distance matrix. \code{designTree} and \code{designSplits} compute design matrices for the estimation of edge length of (phylogenetic) trees using linear models. For larger trees a sparse design matrix can save a lot of memory. %\code{designTree} also computes a contrast matrix if the method is "rooted". } \usage{ designTree(tree, method = "unrooted", sparse=FALSE, ...) designSplits(x, splits = "all", ...) nnls.tree(dm, tree, rooted=FALSE, trace=1) nnls.phylo(x, dm, rooted=FALSE, trace=0) nnls.splits(x, dm, trace = 0) nnls.networx(x, dm) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{tree}{an object of class \code{phylo} } \item{method}{design matrix for an "unrooted" or "rooted" ultrametric tree.} \item{sparse}{return a sparse design matrix.} \item{x}{number of taxa. } \item{splits}{one of "all", "star".} \item{dm}{a distance matrix.} \item{rooted}{compute a "rooted" or "unrooted" tree.} \item{trace}{defines how much information is printed during optimisation.} \item{\dots}{further arguments, passed to other methods.} } \value{ \code{nnls.tree} return a tree, i.e. an object of class \code{phylo}. \code{designTree} and \code{designSplits} a matrix, possibly sparse. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{ \code{\link[ape]{fastme}}, \code{\link[phangorn]{distanceHadamard}}, \code{\link[phangorn]{splitsNetwork}}, \code{\link[phangorn]{upgma}} } \examples{ example(NJ) dm <- as.matrix(dm) y <- dm[lower.tri(dm)] X <- designTree(tree) lm(y~X-1) # avoids negative edge weights tree2 = nnls.tree(dm, tree) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } phangorn/man/densiTree.Rd0000644000175100001440000000424013017130351015062 0ustar hornikusers\name{densiTree} \alias{densiTree} \title{ Plots a densiTree. } \description{ An R function to plot trees similar to those produced by DensiTree. } \usage{ densiTree(x, type = "cladogram", alpha = 1/length(x), consensus = NULL, optim = FALSE, scaleX = FALSE, col = 1, width = 1, cex = 0.8, ...) } \arguments{ \item{x}{ an object of class \code{multiPhylo}. } \item{type}{ a character string specifying the type of phylogeny, so far "cladogram" (default) or "phylogram" (the default) are supported. } \item{alpha}{ parameter for semi-transparent colors. } \item{consensus}{ A tree which is used to define the order of the tip labels. } \item{optim}{ not yet used. } \item{scaleX}{ scale trees to have identical heights. } \item{col}{ edge color. } \item{width}{ edge width. } \item{cex}{ a numeric value giving the factor scaling of the tip labels. } \item{\dots}{ further arguments to be passed to plot. } } \details{ If no consensus tree is provided \code{densiTree} computes a rooted mrp.supertree as a backbone. This should avoid too many unnecessary crossings of edges. Trees should be rooted, other wise the output may not make sense. } \references{ densiTree is inspired from the great \href{www.cs.auckland.ac.nz/~remco/DensiTree}{DensiTree} program of Remco Bouckaert. Remco R. Bouckaert (2010) DensiTree: making sense of sets of phylogenetic trees \emph{Bioinformatics}, \bold{26 (10)}, 1372-1373. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{ \code{\link{plot.phylo}}, \code{\link{plot.networx}} } \examples{ data(Laurasiatherian) set.seed(1) bs <- bootstrap.phyDat(Laurasiatherian, FUN = function(x)upgma(dist.hamming(x)), bs=25) # cladogram nice to show topological differences densiTree(bs, optim=TRUE, type="cladogram", col="blue") densiTree(bs, optim=TRUE, type="phylogram", col="green") \dontrun{ # phylograms are nice to show different age estimates require(PhyloOrchard) data(BinindaEmondsEtAl2007) BinindaEmondsEtAl2007 <- .compressTipLabel(BinindaEmondsEtAl2007) densiTree(BinindaEmondsEtAl2007, type="phylogram", col="red") } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{plot} phangorn/man/plot.networx.Rd0000644000175100001440000001116313005450715015633 0ustar hornikusers\name{plot.networx} \alias{plot.networx} \alias{as.networx} \alias{as.networx.splits} \alias{as.networx.phylo} \alias{write.nexus.networx} \alias{read.nexus.networx} %\alias{reorder.networx} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Phylogenetic networks } \description{ \code{as.networx} convert \code{splits} objects into a \code{networx} object. \code{plot.networx} plot phylogenetic network or split graphs. } \usage{ as.networx(x, ...) \method{as.networx}{splits}(x, planar = FALSE, coord = c("none", "2D", "3D"), ...) \method{plot}{networx}(x, type="3D", use.edge.length = TRUE, show.tip.label=TRUE, show.edge.label=FALSE, edge.label = NULL, show.node.label=FALSE, node.label = NULL, show.nodes=FALSE, tip.color="black", edge.color="black", edge.width=3, edge.lty=1, split.color=NULL, split.width=NULL, split.lty=NULL, font = 3, cex = par("cex"), cex.node.label=cex, cex.edge.label=cex, col.node.label = tip.color, col.edge.label = tip.color, font.node.label = font, font.edge.label = font, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ an object of class \code{"splits"} (as.networx) or \code{"networx"} (plot) } \item{planar}{ logical whether to produce a planar graph from only cyclic splits (may excludes splits). } \item{coord}{add coordinates of the nodes, allows to reproduce the plot.} \item{type}{ "3D" to plot using rgl or "2D" in the normal device. } \item{use.edge.length}{ a logical indicating whether to use the edge weights of the network to draw the branches (the default) or not. } \item{show.tip.label}{ a logical indicating whether to show the tip labels on the graph (defaults to \code{TRUE}, i.e. the labels are shown). } \item{show.edge.label}{ a logical indicating whether to show the tip labels on the graph. } \item{edge.label}{ an additional vector of edge labels (normally not needed). } \item{show.node.label}{ a logical indicating whether to show the node labels (see example). } \item{node.label}{ an additional vector of node labels (normally not needed). } \item{show.nodes}{ a logical indicating whether to show the nodes (see example). } \item{tip.color}{ the colors used for the tip labels. } \item{edge.color}{ the colors used to draw edges. } \item{edge.width}{ the width used to draw edges. } \item{edge.lty}{ a vector of line types. } \item{split.color}{ the colors used to draw edges. } \item{split.width}{ the width used to draw edges. } \item{split.lty}{ a vector of line types. } \item{font}{ an integer specifying the type of font for the labels: 1 (plain text), 2 (bold), 3 (italic, the default), or 4 (bold italic). } \item{cex}{ a numeric value giving the factor scaling of the labels.} \item{cex.node.label}{ a numeric value giving the factor scaling of the node labels.} \item{cex.edge.label}{ a numeric value giving the factor scaling of the edge labels.} \item{col.node.label}{ the colors used for the node labels. } \item{col.edge.label}{ the colors used for the edge labels. } \item{font.node.label}{ the font used for the node labels. } \item{font.edge.label}{ the font used for the edge labels. } \item{\dots}{ Further arguments passed to or from other methods. } } \details{ A \code{networx} object hold the information for a phylogenetic network and extends the \code{phylo} object. Therefore some generic function for \code{phylo} objects will also work for \code{networx} objects. The argument planar = FALSE will create a planar split graph based on a cyclic ordering. These objects can be nicely plotted in "2D". So far not all parameters behave the same on the the rgl "3D" and basic graphic "2D" device. Often it is easier (and safer) to supply vectors of graphical parameters for splits (e.g. splits.color). These overwrite values edge.color. } \note{ The internal representation is likely to change. } \references{ Dress, A.W.M. and Huson, D.H. (2004) Constructing Splits Graphs \emph{IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)}, \bold{1(3)}, 109--115 } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{ \code{\link{consensusNet}}, \code{\link{neighborNet}}, \code{\link{splitsNetwork}}, \code{\link{hadamard}}, \code{\link{distanceHadamard}}, \code{\link{layout_with_kk}}, \code{\link[ape]{evonet}}, \code{\link[ape]{as.igraph}}, \code{\link{densiTree}} } \examples{ set.seed(1) tree1 = rtree(20, rooted=FALSE) sp = as.splits(rNNI(tree1, n=10)) net = as.networx(sp) plot(net, "2D") \dontrun{ # also see example in consensusNet example(consensusNet) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{plot} phangorn/man/nni.Rd0000644000175100001440000000242612761653173013751 0ustar hornikusers\name{nni} \alias{nni} \alias{rNNI} \alias{rSPR} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Tree rearrangements.} \description{ \code{nni} returns a list of all trees which are one nearest neighbor interchange away. \code{rNNI} and \code{rSPR} are two methods which simulate random trees which are a specified number of rearrangement apart from the input tree. Both methods assume that the input tree is bifurcating. These methods may be useful in simulation studies. } \usage{ nni(tree) rSPR(tree, moves=1, n=length(moves), k=NULL) rNNI(tree, moves=1, n=length(moves)) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{tree}{A phylogenetic \code{tree}, object of class \code{phylo}.} \item{moves}{Number of tree rearrangements to be transformed on a tree. Can be a vector} \item{n}{Number of trees to be simulated.} \item{k}{If defined just SPR of distance k are performed.} } \value{ an object of class multiPhylo. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{\code{\link{allTrees}}, \code{\link{SPR.dist}}} \examples{ tree = unroot(rtree(20)) trees1 <- nni(tree) trees2 <- rSPR(tree, 2, 10) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } phangorn/man/hadamard.Rd0000644000175100001440000000464312761653173014731 0ustar hornikusers\name{hadamard} \alias{hadamard} \alias{fhm} \alias{h4st} \alias{h2st} \title{Hadamard Matrices and Fast Hadamard Multiplication} \description{ A collection of functions to perform Hadamard conjugation. %Hv of a Hadamard matrix H with a vector v using fast Hadamard multiplication. } \usage{ hadamard(x) fhm(v) h2st(obj, eps=0.001) h4st(obj, levels = c("a","c","g","t")) } \arguments{ \item{x}{ a vector of length \eqn{2^n}, where n is an integer. } \item{v}{ a vector of length \eqn{2^n}, where n is an integer. } \item{obj}{ a data.frame or character matrix, typical a sequence alignment.} \item{eps}{Threshold value for splits.} \item{levels}{ levels of the sequences.} } \details{ \code{h2st} and \code{h4st} perform Hadamard conjugation for 2-state (binary, RY-coded) or 4-state (DNA/RNA) data. \code{write.nexus.splits} writes splits returned from \code{h2st} or \code{\link[phangorn]{distanceHadamard}} to a nexus file, which can be processed by Spectronet or Splitstree. } \value{ \code{hadamard} returns a Hadamard matrix. \code{fhm} returns the fast Hadamard multiplication. } \references{Hendy, M.D. (1989). The relationship between simple evolutionary tree models and observable sequence data. \emph{Systematic Zoology}, \bold{38} 310--321. Hendy, M. D. and Penny, D. (1993). Spectral Analysis of Phylogenetic Data. \emph{Journal of Classification}, \bold{10}, 5--24. Hendy, M. D. (2005). Hadamard conjugation: an analytical tool for phylogenetics. In O. Gascuel, editor, \emph{Mathematics of evolution and phylogeny}, Oxford University Press, Oxford Waddell P. J. (1995). Statistical methods of phylogenetic analysis: Including hadamard conjugation, LogDet transforms, and maximum likelihood. \emph{PhD thesis}. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{\code{\link{distanceHadamard}}, \code{\link{lento}}, \code{\link{plot.networx}}} \examples{ H <- hadamard(3) v <- 1:8 H%*%v fhm(v) data(yeast) # RY-coding dat_ry <- acgt2ry(yeast) fit2 <- h2st(dat_ry) lento(fit2) # write.nexus.splits(fit2, file = "test.nxs") # read this file into Spectronet or Splitstree to show the network \dontrun{ dat = as.character(yeast) dat4 = phyDat(dat, type="USER", levels=c("a","c", "g", "t"), ambiguity=NULL) fit4 = h4st(dat4) par(mfrow=c(3,1)) lento(fit4[[1]], main="Transversion") lento(fit4[[2]], main="Transition 1") lento(fit4[[3]], main="Transition 2") } } \keyword{ cluster }% at least one, from doc/KEYWORDS phangorn/man/dist.hamming.Rd0000644000175100001440000000540612761653173015550 0ustar hornikusers\name{dist.hamming} \alias{dist.hamming} \alias{dist.logDet} \alias{dist.ml} \alias{readDist} \alias{writeDist} %- Also NEED an '\alias' for EACH other topic documented here. \title{Pairwise Distances from Sequences} \description{ \code{dist.hamming}, \code{dist.ml} and \code{dist.logDet} compute pairwise distances for an object of class \code{phyDat}. \code{dist.ml} uses DNA / AA sequences to compute distances under different substitution models. } \usage{ dist.hamming(x, ratio = TRUE, exclude="none") dist.logDet(x) dist.ml(x, model="JC69", exclude="none", bf=NULL, Q=NULL, k = 1L, shape = 1, ...) readDist(file) writeDist(dm, file="") } \arguments{ \item{x}{An object of class \code{phyDat}} \item{ratio}{Compute uncorrected ('p') distance or character difference.} \item{model}{One of "JC69", "F81" or one of 17 amino acid models see details.} \item{exclude}{One of "none", "all", "pairwise" indicating whether to delete the sites with missing data (or ambiguous states). The default is handle missing data as in pml.} \item{bf}{A vector of base frequencies.} \item{Q}{A vector containing the lower triangular part of the rate matrix.} \item{k}{Number of intervals of the discrete gamma distribution.} \item{shape}{Shape parameter of the gamma distribution.} \item{\dots}{Further arguments passed to or from other methods.} \item{file}{A file name.} \item{dm}{A \code{dist} object.} } \value{ an object of class \code{dist} } \details{ So far 17 amino acid models are supported ("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU", "Blossum62", "Dayhoff_DCMut" and "JTT_DCMut") and additional rate matrices and frequencies can be supplied. The "F81" model uses empirical base frequencies, the "JC69" equal base frequencies. This is even the case if the data are not nucleotides. } \references{ Lockhart, P. J., Steel, M. A., Hendy, M. D. and Penny, D. (1994) Recovering evolutionary trees under a more realistic model of sequence evolution. \emph{Molecular Biology and Evolution}, \bold{11}, 605--602. Jukes TH and Cantor CR (1969). \emph{Evolution of Protein Molecules}. New York: Academic Press. 21--132. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{For more distance methods for nucleotide data see \code{\link[ape]{dist.dna}} and \code{\link{dist.p}} for pairwise polymorphism p-distances} \examples{ data(Laurasiatherian) dm1 <- dist.hamming(Laurasiatherian) tree1 <- NJ(dm1) dm2 <- dist.logDet(Laurasiatherian) tree2 <- NJ(dm2) treedist(tree1,tree2) # JC model dm3 <- dist.ml(Laurasiatherian) tree3 <- NJ(dm3) treedist(tree1,tree3) # F81 + Gamma dm4 <- dist.ml(Laurasiatherian, model="F81", k=4, shape=.4) tree4 <- NJ(dm4) treedist(tree1,tree4) treedist(tree3,tree4) } \keyword{ cluster } phangorn/man/allTrees.Rd0000644000175100001440000000161412761653173014736 0ustar hornikusers\name{allTrees} \alias{allTrees} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Compute all trees topologies.} \description{ \code{allTrees} computes all tree topologies for rooted or unrooted trees with up to 10 tips. \code{allTrees} returns bifurcating trees. } \usage{ allTrees(n, rooted = FALSE, tip.label = NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{n}{Number of tips (<=10).} \item{rooted}{Rooted or unrooted trees (default: rooted). } \item{tip.label}{Tip labels.} } \value{ an object of class \code{multiPhylo}. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{\code{\link[ape]{rtree}}, \code{\link{nni}}} \examples{ trees <- allTrees(5) par(mfrow = c(3,5)) for(i in 1:15)plot(trees[[i]]) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } phangorn/man/parsimony.Rd0000644000175100001440000000725513005450715015200 0ustar hornikusers\name{parsimony} \alias{parsimony} \alias{optim.parsimony} \alias{sankoff} \alias{fitch} \alias{PNJ} \title{Parsimony tree.} \alias{CI} \alias{RI} \alias{pratchet} \alias{random.addition} \alias{acctran} \description{ \code{parsimony} returns the parsimony score of a tree using either the sankoff or the fitch algorithm. \code{optim.parsimony} tries to find the maximum parsimony tree using either Nearest Neighbor Interchange (NNI) rearrangements or sub tree pruning and regrafting (SPR). \code{pratchet} implements the parsimony ratchet (Nixon, 1999) and is the preferred way to search for the best tree. \code{random.addition} can be used to produce starting trees. \code{CI} and \code{RI} computes the consistency and retention index. } \usage{ parsimony(tree, data, method="fitch", ...) optim.parsimony(tree, data, method="fitch", cost=NULL, trace=1, rearrangements="SPR", ...) pratchet(data, start=NULL, method="fitch", maxit=1000, k=10, trace=1, all=FALSE, rearrangements="SPR", perturbation="ratchet", ...) fitch(tree, data, site = "pscore") sankoff(tree, data, cost = NULL, site = "pscore") random.addition(data, method="fitch") CI(tree, data, cost = NULL, sitewise=FALSE) RI(tree, data, cost = NULL, sitewise=FALSE) acctran(tree, data) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{data}{A object of class phyDat containing sequences.} \item{tree}{ tree to start the nni search from.} \item{method}{one of 'fitch' or 'sankoff'.} \item{cost}{A cost matrix for the transitions between two states.} \item{site}{return either 'pscore' or 'site' wise parsimony scores.} \item{trace}{defines how much information is printed during optimisation.} \item{rearrangements}{SPR or NNI rearrangements.} \item{start}{a starting tree can be supplied.} \item{maxit}{maximum number of iterations in the ratchet.} \item{k}{number of rounds ratchet is stopped, when there is no improvement.} \item{all}{return all equally good trees or just one of them.} \item{perturbation}{whether using a ratchet or stochastic (nni) for shuffling the tree.} \item{...}{Further arguments passed to or from other methods (e.g. model="sankoff" and cost matrix).} \item{sitewise}{return CI/RI for alignment or sitewise} } \value{ \code{parsimony} returns the maximum parsimony score (pscore). \code{optim.parsimony} returns a tree after NNI rearrangements. \code{pratchet} returns a tree or list of trees containing the best tree(s) found during the search. \code{acctran} returns a tree with edge length according to the ACCTRAN criterion. } \details{ The "SPR" rearrangements are so far only available for the "fitch" method, "sankoff" only uses "NNI". The "fitch" algorithm only works correct for binary trees. } \references{ Felsenstein, J. (2004). \emph{Inferring Phylogenies}. Sinauer Associates, Sunderland. Nixon, K. (1999) The Parsimony Ratchet, a New Method for Rapid Parsimony Analysis. \emph{Cladistics} \bold{15}, 407-414 } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{\code{\link{bab}}, \code{\link{ancestral.pml}}, \code{\link{nni}}, \code{\link{NJ}}, \code{\link{pml}}, \code{\link{getClans}} ,\code{\link{ancestral.pars}}, \code{\link{bootstrap.pml}}} \examples{ set.seed(3) data(Laurasiatherian) dm = dist.hamming(Laurasiatherian) tree = NJ(dm) parsimony(tree, Laurasiatherian) treeRA <- random.addition(Laurasiatherian) treeNNI <- optim.parsimony(tree, Laurasiatherian) treeRatchet <- pratchet(Laurasiatherian, start=tree, maxit=100, k=5) # assign edge length treeRatchet <- acctran(treeRatchet, Laurasiatherian) plot(midpoint(treeRatchet)) add.scale.bar(0,0, length=100) parsimony(c(tree,treeNNI, treeRatchet), Laurasiatherian) } \keyword{cluster} phangorn/man/delta.score.Rd0000644000175100001440000000257512761653173015375 0ustar hornikusers\name{delta.score} \alias{delta.score} \title{ Computes the \eqn{\delta} score } \description{ Computes the treelikeness } \usage{ delta.score(x, arg = "mean", ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ an object of class \code{phyDat} } \item{arg}{ Specifies the return value, one of "all", "mean" or "sd" } \item{...}{ further arguments passed through \code{dist.hamming} } } %\details{ %% ~~ If necessary, more details than the description above ~~ %} \value{ A vector containing the \eqn{\delta} scores. } \references{ BR Holland, KT Huber, A Dress, V Moulton (2002) \eqn{\delta} Plots: a tool for analyzing phylogenetic distance data Russell D. Gray, David Bryant, Simon J. Greenhill (2010) On the shape and fabric of human history \emph{Molecular Biology and Evolution}, \bold{19(12)} 2051--2059 Russell D. Gray, David Bryant, Simon J. Greenhill (2010) On the shape and fabric of human history \emph{Phil. Trans. R. Soc. B}, \bold{365} 3923--3933; DOI: 10.1098/rstb.2010.0162 } \author{ Alastair Potts and Klaus Schliep } %\note{ %% ~~further notes~~ %} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{dist.hamming}} } \examples{ data(yeast) hist(delta.score(yeast, "all")) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } phangorn/man/distanceHadamard.Rd0000644000175100001440000000175712761653173016407 0ustar hornikusers\name{distanceHadamard} \alias{distanceHadamard} \title{Distance Hadamard} \description{ Distance Hadamard produces spectra of splits from a distance matrix. } \usage{ distanceHadamard(dm, eps=0.001) } \arguments{ \item{dm}{A distance matrix.} \item{eps}{Threshold value for splits.} } \value{ \code{distanceHadamard} returns a matrix. The first column contains the distance spectra, the second one the edge-spectra. If eps is positive an object of with all splits greater eps is returned. } \references{Hendy, M. D. and Penny, D. (1993). Spectral Analysis of Phylogenetic Data. \emph{Journal of Classification}, \bold{10}, 5-24. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}, Tim White} \seealso{ \code{\link{hadamard}}, \code{\link{lento}}, \code{\link{plot.networx}}, \code{\link{neighborNet}} } \examples{ data(yeast) dm = dist.hamming(yeast) dm = as.matrix(dm) fit = distanceHadamard(dm) lento(fit) plot(as.networx(fit), "2D") } \keyword{ cluster }% at least one, from doc/KEYWORDS phangorn/man/consensusNet.Rd0000644000175100001440000000407512761653173015656 0ustar hornikusers\name{consensusNet} \alias{consensusNet} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Computes a consensusNetwork from a list of trees Computes a \code{networx} object from a collection of splits. } \description{ Computes a consensusNetwork, i.e. an object of class \code{networx} from a list of trees, i.e. an class of class \code{multiPhylo}. Computes a \code{networx} object from a collection of splits. } \usage{ consensusNet(obj, prob=.3, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{obj}{An object of class multiPhylo.} \item{prob}{the proportion a split has to be present in all trees to be represented in the network.} \item{\dots}{Further arguments passed to or from other methods.} } \value{ \code{consensusNet} returns an object of class networx. This is just an intermediate to plot phylogenetic networks with igraph. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} %% ~Make other sections like Warning with \section{Warning }{....} ~ \references{ Holland B.R., Huber K.T., Moulton V., Lockhart P.J. (2004) Using consensus networks to visualize contradictory evidence for species phylogeny. \emph{Molecular Biology and Evolution}, \bold{21}, 1459--61 } \seealso{ \code{\link{splitsNetwork}}, \code{\link{neighborNet}}, \code{\link{lento}}, \code{\link{distanceHadamard}}, \code{\link{plot.networx}}, \code{\link{maxCladeCred}} } \examples{ data(Laurasiatherian) set.seed(1) bs <- bootstrap.phyDat(Laurasiatherian, FUN = function(x)nj(dist.hamming(x)), bs=50) class(bs) <- 'multiPhylo' cnet = consensusNet(bs, .3) plot(cnet, "2D") \dontrun{ library(rgl) open3d() plot(cnet, show.tip.label=FALSE, show.nodes=TRUE) plot(cnet, type = "2D", show.edge.label=TRUE) tmpfile <- normalizePath(system.file("extdata/trees/RAxML_bootstrap.woodmouse", package="phangorn")) trees <- read.tree(tmpfile) cnet_woodmouse = consensusNet(trees, .3) plot(cnet_woodmouse, type = "2D", show.edge.label=TRUE) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ hplot } phangorn/man/superTree.Rd0000644000175100001440000000530013005450715015122 0ustar hornikusers\name{superTree} \alias{superTree} \alias{coalSpeciesTree} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Super Tree and Species Tree methods } \description{ These function \code{superTree} allows the estimation of a supertree from a set of trees using either Matrix representation parsimony, Robinson-Foulds or SPR as criterion. \code{coalSpeciesTree} estimates species trees and can multiple individuals per species.} \usage{ superTree(tree, method = "MRP", rooted=FALSE, trace=0, ...) coalSpeciesTree(tree, X, sTree = NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{tree}{ an object of class \code{multiPhylo} } \item{method}{ An argument defining which algorithm is used to optimize the tree. Possible are "MRP", "NNI", and "SPR". } \item{rooted}{ should the resulting supertrees be rooted. } \item{X}{ A \code{phyDat} object to define which individual belongs to which species. } \item{trace}{ defines how much information is printed during optimization. } \item{sTree}{ A species tree which forces the topology. } \item{\dots}{ further arguments passed to or from other methods. } } \details{ The function \code{superTree} extends the function mrp.supertree from Liam Revells, with artificial adding an outgroup on the root of the trees. This allows to root the supertree afterwards. The functions is internally used in DensiTree. The implementation for the RF- and SPR-supertree are very basic so far and assume that all trees share the same set of taxa. \code{coalSpeciesTree} estimates a single linkage tree as suggested by Liu et al. (2010) from the element wise minima of the cophenetic matrices of the gene trees. It extends \code{speciesTree} in ape as it allows that have several individuals per gene tree. } \value{ The function returns an object of class \code{phylo}. } \references{ Liu, L., Yu, L. and Pearl, D. K. (2010) Maximum tree: a consistent estimator of the species tree. \emph{Journal of Mathematical Biology}, \bold{60}, 95--106. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} Liam Revell Emmanuel Paradies } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{mrp.supertree}, \code{\link{speciesTree}}, \code{\link{densiTree}}, \code{\link{RF.dist}}, \code{\link{SPR.dist}} } \examples{ data(Laurasiatherian) set.seed(1) bs <- bootstrap.phyDat(Laurasiatherian, FUN = function(x)upgma(dist.hamming(x)), bs=50) class(bs) <- 'multiPhylo' mrp_st <- superTree(bs, rooted=TRUE) plot(superTree(mrp_st)) \dontrun{ rf_st <- superTree(bs, method = "RF") spr_st <- superTree(bs, method = "SPR") } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{cluster} phangorn/man/upgma.Rd0000644000175100001440000000172212761653173014274 0ustar hornikusers\name{upgma} \alias{upgma} \alias{wpgma} \title{ UPGMA and WPGMA } \description{ UPGMA and WPGMA clustering. Just a wrapper function around \code{\link[stats]{hclust}}. } \usage{ upgma(D, method = "average", ...) wpgma(D, method = "mcquitty", ...) } \arguments{ \item{D}{A distance matrix.} \item{method}{The agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid". The default is "average".} \item{\dots}{Further arguments passed to or from other methods.} } \value{ A phylogenetic tree of class \code{phylo}. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{hclust}}, \code{\link{dist.hamming}}, \code{\link{NJ}}, \code{\link{as.phylo}}, \code{\link{fastme}}, \code{\link{nnls.tree}} } \examples{ data(Laurasiatherian) dm = dist.ml(Laurasiatherian) tree = upgma(dm) plot(tree) } \keyword{cluster} phangorn/man/SH.test.Rd0000644000175100001440000000247412761653173014460 0ustar hornikusers\name{SH.test} \alias{SH.test} \title{Shimodaira-Hasegawa Test} \usage{ SH.test(..., B = 10000, data=NULL) } \arguments{ \item{...}{either a series of objects of class \code{"pml"} separated by commas, a list containing such objects or an object of class \code{"pmlPart"}.} \item{B}{the number of bootstrap replicates.} \item{data}{an object of class \code{"phyDat"}.} } \description{ This function computes the Shimodaira--Hasegawa test for a set of trees. } \value{ a numeric vector with the P-value associated with each tree given in \code{...}. } \references{ Shimodaira, H. and Hasegawa, M. (1999) Multiple comparisons of log-likelihoods with applications to phylogenetic inference. \emph{Molecular Biology and Evolution}, \bold{16}, 1114--1116. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{pml}}, \code{\link{pmlPart}}, \code{\link{pmlCluster}}, \code{\link{SOWH.test}} } \examples{ data(Laurasiatherian) dm <- dist.logDet(Laurasiatherian) tree1 <- NJ(dm) tree2 <- unroot(upgma(dm)) fit1 <- pml(tree1, Laurasiatherian) fit2 <- pml(tree2, Laurasiatherian) fit1 <- optim.pml(fit1) # optimize edge weights fit2 <- optim.pml(fit2) SH.test(fit1, fit2, B=500) # in real analysis use larger B, e.g. 10000 \dontrun{ example(pmlPart) SH.test(sp, B=1000) } } \keyword{models} phangorn/man/lento.Rd0000644000175100001440000000304712761653173014306 0ustar hornikusers\name{lento} \alias{lento} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Lento plot } \description{ The lento plot represents support and conflict of splits/bipartitions. } \usage{ lento(obj, xlim = NULL, ylim = NULL, main = "Lento plot", sub = NULL, xlab = NULL, ylab = NULL, bipart=TRUE, trivial=FALSE, col = rgb(0, 0, 0, 0.5),...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{obj}{an object of class phylo, multiPhylo or splits} \item{xlim}{graphical parameter} \item{ylim}{graphical parameter} \item{main}{graphical parameter} \item{sub}{graphical parameter} \item{xlab}{graphical parameter} \item{ylab}{graphical parameter} \item{bipart}{plot bipartition information.} \item{trivial}{logical, whether to present trivial splits (default is FALSE).} \item{col}{color for the splits / bipartition.} \item{\dots}{Further arguments passed to or from other methods.} } \value{ lento returns a plot. } \references{ Lento, G.M., Hickson, R.E., Chambers G.K., and Penny, D. (1995) Use of spectral analysis to test hypotheses on the origin of pinninpeds. \emph{Molecular Biology and Evolution}, \bold{12}, 28-52. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{as.splits}, \link{hadamard}} } \examples{ data(yeast) yeast.ry = acgt2ry(yeast) splits.h = h2st(yeast.ry) lento(splits.h, trivial=TRUE) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } \keyword{ plot }% __ONLY ONE__ keyword per line phangorn/man/pmlMix.Rd0000644000175100001440000000604112761653173014430 0ustar hornikusers\name{pmlMix} \alias{pmlMix} \alias{pmlPen} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Phylogenetic mixture model } \description{ Phylogenetic mixture model. } \usage{ pmlMix(formula, fit, m=2, omega=rep(1/m, m), control=pml.control(epsilon=1e-08, maxit=20, trace=1),...) } \arguments{ \item{formula}{ a formula object (see details).} \item{fit}{ an object of class \code{pml}. } \item{m}{ number of mixtures. } \item{omega}{ mixing weights.} \item{control}{A list of parameters for controlling the fitting process.} \item{\dots}{ Further arguments passed to or from other methods. } } \details{ The \code{formula} object allows to specify which parameter get optimized. The formula is generally of the form \code{edge + bf + Q ~ rate + shape + \dots}, on the left side are the parameters which get optimized over all mixtures, on the right the parameter which are optimized specific to each mixture. The parameters available are \code{"nni", "bf", "Q", "inv", "shape", "edge", "rate"}. Each parameters can be used only once in the formula. \code{"rate"} and \code{"nni"} are only available for the right side of the formula. On the other hand parameters for invariable sites are only allowed on the left-hand side. The convergence of the algorithm is very slow and is likely that the algorithm can get stuck in local optima. } \value{ \code{pmlMix} returns a list with elements \item{logLik}{log-likelihood of the fit} \item{omega}{mixing weights.} \item{fits}{fits for the final mixtures.} } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{pml}},\code{\link{pmlPart}},\code{\link{pmlCluster}} } \examples{ \dontrun{ X <- allSitePattern(5) tree <- read.tree(text = "((t1:0.3,t2:0.3):0.1,(t3:0.3,t4:0.3):0.1,t5:0.5);") fit <- pml(tree,X, k=4) weights <- 1000*exp(fit$site) attr(X, "weight") <- weights fit1 <- update(fit, data=X, k=1) fit2 <- update(fit, data=X) (fitMixture <- pmlMix(edge~rate, fit1 , m=4)) (fit2 <- optim.pml(fit2, optGamma=TRUE)) data(Laurasiatherian) dm <- dist.logDet(Laurasiatherian) tree <- NJ(dm) fit=pml(tree, Laurasiatherian) fit = optim.pml(fit) fit2 <- update(fit, k=4) fit2 <- optim.pml(fit2, optGamma=TRUE) fitMix = pmlMix(edge ~ rate, fit, m=4) fitMix # # simulation of mixture models # \dontrun{ X <- allSitePattern(5) tree1 <- read.tree(text = "((t1:0.1,t2:0.5):0.1,(t3:0.1,t4:0.5):0.1,t5:0.5);") tree2 <- read.tree(text = "((t1:0.5,t2:0.1):0.1,(t3:0.5,t4:0.1):0.1,t5:0.5);") tree1 <- unroot(tree1) tree2 <- unroot(tree2) fit1 <- pml(tree1,X) fit2 <- pml(tree2,X) weights <- 2000*exp(fit1$site) + 1000*exp(fit2$site) attr(X, "weight") <- weights fit1 <- pml(tree1, X) fit2 <- optim.pml(fit1) logLik(fit2) AIC(fit2, k=log(3000)) fitMixEdge = pmlMix( ~ edge, fit1, m=2) logLik(fitMixEdge) AIC(fitMixEdge, k=log(3000)) fit.p <- pmlPen(fitMixEdge, .25) logLik(fit.p) AIC(fit.p, k=log(3000)) } } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } phangorn/man/ancestral.pml.Rd0000644000175100001440000000537012761653173015731 0ustar hornikusers\name{ancestral.pml} \alias{ancestral.pml} \alias{ancestral.pars} \alias{pace} \alias{plotAnc} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Ancestral character reconstruction. } \description{ Marginal reconstruction of the ancestral character states. } \usage{ ancestral.pml(object, type = c("marginal", "ml", "bayes")) ancestral.pars(tree, data, type = c("MPR", "ACCTRAN"), cost = NULL) pace(tree, data, type = c("MPR", "ACCTRAN"), cost = NULL) plotAnc(tree, data, i, col=NULL, cex.pie=par("cex"), pos="bottomright", ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{an object of class pml} % \item{type}{either "ml" or "bayes"} \item{tree}{a tree, i.e. an object of class pml} \item{data}{an object of class phyDat} \item{type}{method used to assign characters to internal nodes, see details.} \item{i}{plots the i-th character of the \code{data}.} \item{col}{a vector containing the colors for all possible states.} \item{cex.pie}{a numeric defining the size of the pie graphs} \item{pos}{a character string defining the position of the legend} \item{cost}{A cost matrix for the transitions between two states.} \item{\dots}{Further arguments passed to or from other methods.} } % \item{eps}{a small value to prevent rounding errors} \details{ The argument "type" defines the criterion to assign the internal nodes. For \code{ancestral.pml} so far "ml" and (empirical) "bayes" and for \code{ancestral.pars} "MPR" and "ACCTRAN" are possible. With parsimony reconstruction one has to keep in mind that there will be often no unique solution. For further details see vignette("Ancestral"). } \value{ %A matrix containing the the estimates character states. An object of class "phyDat", containing the ancestral states of all nodes. } \references{ Felsenstein, J. (2004). \emph{Inferring Phylogenies}. Sinauer Associates, Sunderland. Swofford, D.L., Maddison, W.P. (1987) Reconstructing ancestral character states under Wagner parsimony. \emph{Math. Biosci.} \bold{87}: 199--229 Yang, Z. (2006). \emph{Computational Molecular evolution}. Oxford University Press, Oxford. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{pml}}, \code{\link{parsimony}}, \code{\link[ape]{ace}}, \code{\link[ape]{root}} } \examples{ example(NJ) fit = pml(tree, Laurasiatherian) anc.ml = ancestral.pml(fit, type = "ml") anc.p = ancestral.pars(tree, Laurasiatherian) \dontrun{ require(seqLogo) seqLogo( t(subset(anc.ml, 48, 1:20)[[1]]), ic.scale=FALSE) seqLogo( t(subset(anc.p, 48, 1:20)[[1]]), ic.scale=FALSE) } plotAnc(tree, anc.ml, 1) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ ~kwd1 } \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line phangorn/man/dfactorial.Rd0000644000175100001440000000125512761653173015274 0ustar hornikusers\name{dfactorial} \alias{dfactorial} \alias{ldfactorial} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Arithmetic Operators } \description{ double factorial function } \usage{ dfactorial(x) ldfactorial(x) } \arguments{ \item{x}{ a numeric scalar or vector } } \value{ \code{dfactorial(x)} returns the double factorial, that is \eqn{x\!\! = 1 * 3 * 5 * \ldots * x } and \code{ldfactorial(x)} is the natural logarithm of it. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{ \code{\link[base:Special]{factorial}}, \code{\link[ape]{howmanytrees}} } \examples{ dfactorial(1:10) } \keyword{ classif }% at least one, from doc/KEYWORDS phangorn/man/treedist.Rd0000644000175100001440000001217713020070304014766 0ustar hornikusers\name{treedist} \alias{treedist} \alias{RF.dist} \alias{wRF.dist} \alias{KF.dist} \alias{path.dist} \alias{sprdist} \alias{SPR.dist} %\alias{print.treedist} \title{ Distances between trees } \description{ \code{treedist} computes different tree distance methods and \code{RF.dist} the Robinson-Foulds or symmetric distance. } \usage{ treedist(tree1, tree2, check.labels = TRUE) sprdist(tree1, tree2) RF.dist(tree1, tree2=NULL, normalize=FALSE, check.labels=TRUE, rooted=FALSE) wRF.dist(tree1, tree2=NULL, normalize=FALSE, check.labels=TRUE, rooted=FALSE) KF.dist(tree1, tree2=NULL, check.labels=TRUE, rooted=FALSE) SPR.dist(tree1, tree2=NULL) path.dist(tree1, tree2=NULL, check.labels=TRUE, use.weight=FALSE) } \arguments{ \item{tree1}{ A phylogenetic tree (class \code{phylo}) or vector of trees (an object of class \code{multiPhylo}). See details } \item{tree2}{ A phylogenetic tree. } \item{normalize}{compute normalized RF-distance, see details. } \item{check.labels}{compares labels of the trees.} \item{rooted}{take bipartitions for rooted trees into account, default is unrooting the trees.} \item{use.weight}{use edge.length argument or just count number of edges on the path (default)} } \value{ \code{treedist} returns a vector containing the following tree distance methods \item{symmetric.difference}{symmetric.difference or Robinson-Foulds distance} \item{branch.score.difference}{branch.score.difference} \item{path.difference}{path.difference} \item{weighted.path.difference}{weighted.path.difference} } \details{ The Robinson-Foulds distance between two trees \eqn{T_1} and \eqn{T_2} with \eqn{n} tips is defined as (following the notation Steel & Penny 1993): \deqn{d(T_1, T_2) = i(T_1) + i(T_2) - 2v_s(T_1, T_2)} where \eqn{i(T_1)} denotes the number of internal edges and \eqn{v_s(T_1, T_2)} denotes the number of internal splits shared by the two trees. The normalized Robinson-Foulds distance is derived by dividing \eqn{d(T_1, T_2)} by the maximal possible distance \eqn{i(T_1) + i(T_2)}. If both trees are unrooted and binary this value is \eqn{2n-6}. % The weighted Robinson-Foulds distance makes use of edge weights. Let \eqn{E_1} and \eqn{E_2} the set of edges of \eqn{d(T_1, T_2)} and \eqn{w(e), e \in E_1} the weight of an edge in \eqn{E_1} %\deqn{d(T_1, T_2) = \sum_{e \in E \setminus E_1}w(e) + \sum_{e \in E_1\cap E_2}|w(e_1) - w(e_2) + \sum_{e \in E\setminus E_2}w(e) } % where \eqn{E = E_1\cup E_2} %The Kuhner-Felsenstein difference (Kuhner & Felsenstein 1994) is closely related with the weighted Robinson-Foulds distance. %Instead of the absolute values between edges are squared % 0-norm RF % 1-norm wRF % 2-norm KF \code{RF.dist} returns the Robinson-Foulds distance (Robinson & Foulds 1981) between either 2 trees or computes a matrix of all pairwise distances if a \code{multiPhylo} object is given. The Robinson-Foulds distance only depends on the toplogy of the trees. If edge weights should be considered \code{wRF.dist} calculates the weighted RF distance (Robinson & Foulds 1981). and \code{KF.dist} calculates the branch score distance (Kuhner & Felsenstein 1994). \code{path.dist} computes the path difference metric as described in Steel and Penny 1993). \code{sprdist} computes the approximate SPR distance (Oliveira Martins et al. 2008, de Oliveira Martins 2016). For large number of trees the distance functions can use a lot of memory! % The function used internally is 2 * (nt - m) where nt is the number of tips and % m is the number of shared bipartitions. When there are multifurcations the % distance is therefore increasing!! This may be different to other implementations! } \references{ de Oliveira Martins L., Leal E., Kishino H. (2008) \emph{Phylogenetic Detection of Recombination with a Bayesian Prior on the Distance between Trees}. PLoS ONE \bold{3(7)}. e2651. doi: 10.1371/journal.pone.0002651 de Oliveira Martins L., Mallo D., Posada D. (2016) \emph{A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction}. Syst. Biol. \bold{65(3)}: 397-416, doi:10.1093/sysbio/syu082 Steel M. A. and Penny P. (1993) \emph{Distributions of tree comparison metrics - some new results}, Syst. Biol., \bold{42(2)}, 126--141 Kuhner, M. K. and Felsenstein, J. (1994) \emph{A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates}, Molecular Biology and Evolution, \bold{11(3)}, 459--468 D.F. Robinson and L.R. Foulds (1981) \emph{Comparison of phylogenetic trees}, Mathematical Biosciences, \bold{53(1)}, 131--147 D.F. Robinson and L.R. Foulds (1979) Comparison of weighted labelled trees. In Horadam, A. F. and Wallis, W. D. (Eds.), \emph{Combinatorial Mathematics VI: Proceedings of the Sixth Australian Conference on Combinatorial Mathematics, Armidale, Australia}, 119--126 } \author{ Klaus P. Schliep \email{klaus.schliep@gmail.com}, Leonardo de Oliveira Martins } \seealso{\code{\link[ape]{dist.topo}}, \code{\link{nni}}, \code{\link{superTree}}} \examples{ tree1 <- rtree(100, rooted=FALSE) tree2 <- rSPR(tree1, 3) RF.dist(tree1, tree2) treedist(tree1, tree2) sprdist(tree1, tree2) trees <- rSPR(tree1, 1:5) SPR.dist(tree1, trees) } \keyword{ classif }% at least one, from doc/KEYWORDS phangorn/man/modelTest.Rd0000644000175100001440000000622413005450715015112 0ustar hornikusers\name{modelTest} \alias{modelTest} \alias{AICc} \title{ ModelTest } \description{ Comparison of different nucleotide or amino acid substitution models } \usage{ modelTest(object, tree=NULL, model = c("JC", "F81", "K80", "HKY", "SYM", "GTR"), G = TRUE, I = TRUE, FREQ = FALSE, k = 4, control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1), multicore = FALSE, mc.cores = NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{an object of class phyDat or pml} \item{tree}{a phylogenetic tree.} \item{model}{a vector containing the substitution models to compare with each other or "all" to test all available models} \item{G}{logical, TRUE (default) if (discrete) Gamma model should be tested} \item{I}{logical, TRUE (default) if invariant sites should be tested} \item{FREQ}{logical, FALSE (default) if TRUE amino acid frequencies will be estimated.} \item{k}{number of rate classes} \item{control}{A list of parameters for controlling the fitting process.} \item{multicore}{logical, whether models should estimated in parallel.} \item{mc.cores}{The number of cores to use, i.e. at most how many child processes will be run simultaneously. Must be at least one, and parallelization requires at least two cores.} } \details{ \code{modelTest} estimates all the specified models for a given tree and data. When the mclapply is available, the computations are done in parallel. \code{modelTest} runs each model in one thread. This is may not work within a GUI interface and will not work under Windows. } \value{ A data.frame containing the log-likelihood, number of estimated parameters, AIC, AICc and BIC all tested models. The data.frame has an attributes "env" which is an environment which contains all the trees, the data and the calls to allow get the estimated models, e.g. as a starting point for further analysis (see example). } \references{ Burnham, K. P. and Anderson, D. R (2002) \emph{Model selection and multimodel inference: a practical information-theoretic approach}. 2nd ed. Springer, New York Posada, D. and Crandall, K.A. (1998) MODELTEST: testing the model of DNA substitution. \emph{Bioinformatics} \bold{14(9)}: 817-818 Posada, D. (2008) jModelTest: Phylogenetic Model Averaging. \emph{Molecular Biology and Evolution} \bold{25}: 1253-1256 Darriba D., Taboada G.L., Doallo R and Posada D. (2011) ProtTest 3: fast selection of best-fit models of protein evolution. . \emph{Bioinformatics} \bold{27}: 1164-1165 } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{pml}}, \code{\link{anova}}, \code{\link[stats]{AIC}} } \examples{ \dontrun{ example(NJ) (mT <- modelTest(Laurasiatherian, tree)) # some R magic env = attr(mT, "env") ls(env=env) (F81 <- get("F81+G", env)) # a call eval(F81, env=env) data(chloroplast) (mTAA <- modelTest(chloroplast, model=c("JTT", "WAG", "LG"))) # test all available amino acid models (mTAA_all <- modelTest(chloroplast, model="all", multicore=TRUE, mc.cores=2)) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{cluster} phangorn/man/simSeq.Rd0000644000175100001440000000545513014656617014431 0ustar hornikusers\name{simSeq} \alias{simSeq} \alias{simSeq.phylo} \alias{simSeq.pml} \title{ Simulate sequences. } \description{ Simulate sequences for a given evolutionary tree. } \usage{ simSeq(x, ...) \method{simSeq}{phylo}(x, l=1000, Q=NULL, bf=NULL, rootseq=NULL, type="DNA", model="", levels=NULL, rate=1, ancestral=FALSE, ...) \method{simSeq}{pml}(x, ancestral = FALSE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ a phylogenetic tree \code{tree}, i.e. an object of class \code{phylo} or and object of class \code{pml}. } \item{l}{ length of the sequence to simulate. } \item{Q}{ the rate matrix. } \item{bf}{ base frequencies. } \item{rootseq}{a vector of length l containing the root sequence, other root sequence is randomly generated.} \item{type}{Type of sequences ("DNA", "AA" or "USER").} \item{model}{Amino acid models: one of "WAG", "JTT", "Dayhoff" or "LG"} \item{levels}{ \code{levels} takes a character vector of the different bases, default is for nucleotide sequences, only used when type = "USER".} \item{rate}{mutation rate or scaler for the edge length, a numerical value greater than zero. } \item{ancestral}{Return ancestral sequences?} \item{\dots}{Further arguments passed to or from other methods.} } \details{ \code{simSeq} is now a generic function to simulate sequence alignments. It is quite flexible and allows to generate DNA, RNA, amino acids or binary sequences. It is possible to give a \code{pml} object as input simSeq return a \code{phyDat} from these model. There is also a more low level version, which lacks rate variation, but one can combine different alignments having their own rate (see example). The rate parameter acts like a scaler for the edge lengths. } \value{ \code{simSeq} returns an object of class phyDat. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{phyDat}}, \code{\link{pml}}, \code{\link{SOWH.test}} } \examples{ \dontrun{ data(Laurasiatherian) tree = nj(dist.ml(Laurasiatherian)) fit = pml(tree, Laurasiatherian, k=4) fit = optim.pml(fit, optNni=TRUE, model="GTR", optGamma=TRUE) data = simSeq(fit) } tree = rtree(5) plot(tree) nodelabels() # Example for simple DNA alignment data = simSeq(tree, l = 10, type="DNA", bf=c(.1,.2,.3,.4), Q=1:6) as.character(data) # Example to simulate discrete Gamma rate variation rates = discrete.gamma(1,4) data1 = simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[1]) data2 = simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[2]) data3 = simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[3]) data4 = simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[4]) data <- c(data1,data2, data3, data4) write.phyDat(data, file="temp.dat", format="sequential",nbcol = -1, colsep = "") unlink("temp.dat") } \keyword{ cluster }% at least one, from doc/KEYWORDS phangorn/man/bootstrap.pml.Rd0000644000175100001440000001022412761653173015764 0ustar hornikusers\name{bootstrap.pml} %\Rdversion{1.1} \alias{bootstrap.pml} \alias{bootstrap.phyDat} \alias{plotBS} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Bootstrap } \description{ \code{bootstrap.pml} performs (non-parametric) bootstrap analysis and \code{bootstrap.phyDat} produces a list of bootstrapped data sets. \code{plotBS} plots a phylogenetic tree with the with the bootstrap values assigned to the (internal) edges.} \usage{ bootstrap.pml(x, bs = 100, trees = TRUE, multicore=FALSE, mc.cores = NULL, ...) bootstrap.phyDat(x, FUN, bs = 100, multicore=FALSE, mc.cores = NULL, jumble = TRUE, ...) plotBS(tree, BStrees, type="unrooted", bs.col="black", bs.adj=NULL, p=50, frame=none, ...) } \arguments{ \item{x}{ an object of class \code{pml} or \code{phyDat}. } \item{bs}{ number of bootstrap samples. } \item{trees}{ return trees only (default) or whole \code{pml} objects. } % \item{multicore}{ %logical, if TRUE analysis is performed in parallel (see details). %} \item{multicore}{logical, whether models should estimated in parallel.} \item{mc.cores}{ The number of cores to use during bootstrap. Only supported on UNIX-alike systems. } \item{jumble}{logical, jumble the order of the sequences.} \item{\dots}{ further parameters used by \code{optim.pml} or \code{plot.phylo}. } \item{FUN}{ the function to estimate the trees. } \item{tree}{ The tree on which edges the bootstrap values are plotted. } \item{BStrees}{ a list of trees (object of class "multiPhylo"). } \item{type}{ the type of tree to plot, so far "cladogram", "phylogram" and "unrooted" are supported. } \item{bs.col}{ color of bootstrap support labels. } \item{bs.adj}{ one or two numeric values specifying the horizontal and vertical justification of the bootstrap labels. } \item{p}{ only plot support values higher than this percentage number (default is 80). } \item{frame}{ a character string specifying the kind of frame to be printed around the bootstrap values. This must be one of "none" (the default), "rect" or "circle". } } \details{ It is possible that the bootstrap is performed in parallel, with help of the multicore package. Unfortunately the multicore package does not work under windows or with GUI interfaces ("aqua" on a mac). However it will speed up nicely from the command line ("X11"). } \value{ \code{bootstrap.pml} returns an object of class \code{multi.phylo} or a list where each element is an object of class \code{pml}. \code{plotBS} returns silently a tree, i.e. an object of class \code{phylo} with the bootstrap values as node labels. The argument \code{BStrees} is optional and if not supplied the tree with labels supplied in the \code{node.label} slot. } \references{ Felsenstein J. (1985) Confidence limits on phylogenies. An approach using the bootstrap. \emph{Evolution} \bold{39}, 783--791 Penny D. and Hendy M.D. (1985) Testing methods evolutionary tree construction. \emph{Cladistics} \bold{1}, 266--278 Penny D. and Hendy M.D. (1986) Estimating the reliability of evolutionary trees. \emph{Molecular Biology and Evolution} \bold{3}, 403--417 } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{ \code{\link{optim.pml}}, \code{\link{pml}}, \code{\link{plot.phylo}}, \code{\link{nodelabels}},\code{\link{consensusNet}} and \code{\link{SOWH.test}} for parametric bootstrap } \examples{ \dontrun{ data(Laurasiatherian) dm <- dist.logDet(Laurasiatherian) tree <- NJ(dm) # NJ set.seed(123) NJtrees <- bootstrap.phyDat(Laurasiatherian, FUN=function(x)NJ(dist.logDet(x)), bs=100) treeNJ <- plotBS(tree, NJtrees, "phylogram") # Maximum likelihood fit <- pml(tree, Laurasiatherian) fit <- optim.pml(fit, rearrangements="NNI") set.seed(123) bs <- bootstrap.pml(fit, bs=100, optNni=TRUE) treeBS <- plotBS(fit$tree,bs) # Maximum parsimony treeMP <- pratchet(Laurasiatherian) treeMP <- acctran(treeMP, Laurasiatherian) set.seed(123) BStrees <- bootstrap.phyDat(Laurasiatherian, pratchet, bs = 100) treeMP <- plotBS(treeMP, BStrees, "phylogram") add.scale.bar() # export tree with bootstrap values as node labels # write.tree(treeBS) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{cluster} phangorn/man/yeast.Rd0000644000175100001440000000070312761653173014306 0ustar hornikusers\name{yeast} \alias{yeast} \docType{data} \title{ Yeast alignment (Rokas et al.) } \description{ Alignment of 106 genes of 8 different species of yeast.} \usage{data(yeast)} \references{ Rokas, A., Williams, B. L., King, N., and Carroll, S. B. (2003) Genome-scale approaches to resolving incongruence in molecular phylogenies. \emph{Nature}, \bold{425}(6960): 798--804 } \examples{ data(yeast) str(yeast) } \keyword{datasets} phangorn/man/NJ.Rd0000644000175100001440000000244612761653173013476 0ustar hornikusers\name{NJ} \alias{NJ} \alias{UNJ} %- Also NEED an '\alias' for EACH other topic documented here. \title{Neighbor-Joining} \description{ This function performs the neighbor-joining tree estimation of Saitou and Nei (1987). UNJ is the unweighted version from Gascuel (1997). } \usage{ NJ(x) UNJ(x) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{A distance matrix.} } \value{ an object of class \code{"phylo"}. } \references{Saitou, N. and Nei, M. (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. \emph{Molecular Biology and Evolution}, \bold{4}, 406--425. Studier, J. A and Keppler, K. J. (1988) A Note on the Neighbor-Joining Algorithm of Saitou and Nei. \emph{Molecular Biology and Evolution}, \bold{6}, 729--731. Gascuel, O. (1997) Concerning the NJ algorithm and its unweighted version, UNJ. in Birkin et. al. \emph{Mathematical Hierarchies and Biology}, 149--170.} \author{Klaus P. Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link[ape]{nj}}, \code{\link[ape]{dist.dna}}, \code{\link[phangorn]{dist.hamming}}, \code{\link[phangorn]{upgma}}, \code{\link[ape]{fastme}}} \examples{ data(Laurasiatherian) dm <- dist.ml(Laurasiatherian) tree <- NJ(dm) plot(tree) } \keyword{ cluster }% at least one, from doc/KEYWORDS phangorn/man/as.splits.Rd0000644000175100001440000000617013005450715015072 0ustar hornikusers\name{as.splits} \alias{as.splits} \alias{as.prop.part.splits} \alias{as.splits.phylo} \alias{as.splits.multiPhylo} \alias{as.splits.networx} \alias{as.matrix.splits} \alias{as.Matrix} \alias{as.Matrix.splits} \alias{c.splits} \alias{distinct.splits} \alias{print.splits} \alias{write.splits} \alias{allSplits} \alias{allCircularSplits} \alias{compatible} \alias{write.nexus.splits} \alias{read.nexus.splits} \alias{as.phylo.splits} \alias{countCycles} \alias{addTrivialSplits} \alias{matchSplits} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Splits representation of graphs and trees. } \description{ \code{as.splits} produces a list of splits or bipartitions. } \usage{ as.splits(x, ...) \method{as.splits}{phylo}(x, ...) \method{as.splits}{multiPhylo}(x, ...) \method{print}{splits}(x, maxp = getOption("max.print"), zero.print = ".", one.print = "|", ...) \method{as.prop.part}{splits}(x, ...) compatible(obj) allSplits(k, labels = NULL) allCircularSplits(k, labels = NULL) write.nexus.splits(obj, file = "", weights=NULL, taxa=TRUE, append=FALSE) read.nexus.splits(file) addTrivialSplits(obj) distinct.splits(...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{An object of class phylo or multiPhylo.} \item{maxp}{integer, default from \code{options(max.print)}, influences how many entries of large matrices are printed at all.} \item{zero.print}{character which should be printed for zeros.} \item{one.print}{character which should be printed for ones.} \item{\dots}{Further arguments passed to or from other methods.} \item{obj}{an object of class splits.} \item{k}{number of taxa.} \item{labels}{names of taxa.} \item{file}{ a file name.} \item{weights}{ Edge weights.} \item{taxa}{logical. If TRUE a taxa block is added} \item{append}{logical. If TRUE the nexus blocks will be added to a file.} } \value{ \code{as.splits} returns an object of class splits, which is mainly a list of splits and some attributes. Often a \code{splits} object will contain attributes \code{confidences} for bootstrap or Bayesian support values and \code{weight} storing edge weights. \code{read.nexus.splits} and \code{write.nexus.splits} allow to exchange a \code{splits} object with other software like Splitstree. \code{compatible} return a lower triangular matrix where an 1 indicates that two splits are incompatible. } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \note{ The internal representation is likely to change. \code{read.nexus.splits} reads in the splits block of a nexus file. It assumes that different co-variables are tab delimited and the bipartition are separated with white-space. Comments in square brackets are ignored. } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{prop.part}}, \code{\link{lento}}, \code{\link{distanceHadamard}}, \code{\link{as.networx}} } %% as.phylo.splits \examples{ (sp <- as.splits(rtree(5))) write.nexus.splits(sp) spl <- allCircularSplits(5) plot(as.networx(spl), "2D") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } phangorn/man/pml.fit.Rd0000644000175100001440000000500713017134011014510 0ustar hornikusers\name{pml.fit} \alias{pml.fit} \alias{edQt} \alias{pml.init} \alias{pml.free} \alias{discrete.gamma} \alias{lli} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Internal maximum likelihood functions. } \description{ These functions are internally used for the likelihood computations in \code{pml} or \code{optim.pml}. } \usage{ pml.fit(tree, data, bf=rep(1/length(levels), length(levels)), shape=1, k=1, Q=rep(1, length(levels)*(length(levels)-1)/2), levels=attr(data, "levels"), inv=0, rate=1, g=NULL, w=NULL, eig=NULL, INV=NULL, ll.0=NULL, llMix=NULL, wMix=0, ..., site=FALSE) pml.init(data, k) pml.free() edQt(Q = c(1, 1, 1, 1, 1, 1), bf = c(0.25, 0.25, 0.25, 0.25)) lli(data, tree, ...) discrete.gamma(alpha, k) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{tree}{A phylogenetic \code{tree}, object of class \code{phylo}. } \item{data}{An alignment, object of class \code{phyDat}.} \item{bf}{Base frequencies.} \item{shape}{Shape parameter of the gamma distribution.} \item{alpha}{Shape parameter of the gamma distribution.} \item{k}{Number of intervals of the discrete gamma distribution.} \item{Q}{A vector containing the lower triangular part of the rate matrix.} \item{levels}{The alphabet used e.g. c("a", "c", "g", "t") for DNA} \item{inv}{Proportion of invariable sites.} \item{rate}{Rate.} \item{g}{vector of quantiles (default is NULL)} \item{w}{vector of probabilities (default is NULL)} \item{eig}{Eigenvalue decomposition of Q} \item{INV}{Sparse representation of invariant sites} \item{ll.0}{default is NULL} \item{llMix}{default is NULL} \item{wMix}{default is NULL} \item{\dots}{Further arguments passed to or from other methods.} \item{site}{return the log-likelihood or vector of sitewise likelihood values} } \details{ These functions are exported to be used in different packages so far only in the package coalescentMCMC, but are not intended for end user. Most of the functions call C code and are far less forgiving if the import is not what they expect than \code{pml}. } \value{ \code{pml.fit} returns the log-likelihood. } \references{ Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. \emph{Journal of Molecular Evolution}, \bold{17}, 368--376. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{ \code{\link{pml}, \link{pmlPart}, \link{pmlMix}} } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } phangorn/man/chloroplast.Rd0000644000175100001440000000071212761653173015513 0ustar hornikusers\name{chloroplast} \alias{chloroplast} \docType{data} \title{ Chloroplast alignment } \description{ Amino acid alignment of 15 genes of 19 different chloroplast.} \usage{data(yeast)} %\references{ %Nisbet R.E.R, Schliep K., Steel M.A., Knapp M. Howe C.J. and Lockhart P.J. (2009) %The slop of rocks and clocks %\emph{Potato growers weekly}, \bold{425}(6960): 798--804 %} \examples{ data(chloroplast) chloroplast } \keyword{datasets} phangorn/man/maxCladeCred.Rd0000644000175100001440000000426213005450715015466 0ustar hornikusers\name{maxCladeCred} \alias{maxCladeCred} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Maximum clade credibility tree } \description{ \code{maxCladeCred} computes the maximum clade credibility tree from a sample of trees. } \usage{ maxCladeCred(x, tree = TRUE, part = NULL, rooted = TRUE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ \code{x} is an object of class \code{multiPhylo} or \code{phylo} } \item{tree}{ logical indicating whether return the tree with the clade credibility (default) or the clade credibility score for all trees. } \item{rooted}{ logical, if FALSE the tree with highest maximum bipartition credibility is returned. } \item{part}{a list of partitions as returned by \code{prop.part}} } \details{ So far just the best tree is returned. No annotations or transformations of edge length are performed. If a list of partition is provided then the clade credibility is computed for the trees in x. } \value{ a tree (an object of class \code{phylo}) with the highest clade credibility or a numeric vector of clade credibilities for each tree. } %%\references{ %% ~put references to the literature/web site here ~ %%} \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{consensus}}, \code{\link{consensusNet}}, \code{\link{prop.part}} } \examples{ data(Laurasiatherian) set.seed(42) bs <- bootstrap.phyDat(Laurasiatherian, FUN = function(x)upgma(dist.hamming(x)), bs=100) class(bs) <- 'multiPhylo' strict_consensus <- consensus(bs) majority_consensus <- consensus(bs, p=.5) max_clade_cred <- maxCladeCred(bs) par(mfrow = c(1,3), mar = c(1,4,1,1)) plot(strict_consensus, main="Strict consensus tree") plot(majority_consensus, main="Majority consensus tree") plot(max_clade_cred, main="Maximum clade credibility tree") # compute clade credibility for trees given a prop.part object pp <- prop.part(bs) tree <- rNNI(bs[[1]], 20) maxCladeCred(c(tree, bs[[1]]), tree=FALSE, part = pp) # first value likely be -Inf } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } phangorn/man/Ancestors.Rd0000644000175100001440000000325512761653173015127 0ustar hornikusers\name{Ancestors} \alias{Ancestors} \alias{Children} \alias{Descendants} \alias{Siblings} \alias{mrca.phylo} %\alias{mrca} \title{tree utility function} \description{ Functions for describing relationships among phylogenetic nodes. } \usage{ Ancestors(x, node, type=c("all","parent")) Children(x, node) Siblings(x, node, include.self=FALSE) Descendants(x, node, type=c("tips","children","all")) mrca.phylo(x, node=NULL) } %mrca(x) \arguments{ \item{x}{a tree (a phylo object).} \item{node}{an integer or a vector of integers corresponding to a node ID} \item{type}{specify whether to return just direct children / parents or all } \item{include.self}{whether to include self in list of siblings} } \details{ These functions are inspired by \code{treewalk} in phylobase package, but work on the S3 \code{phylo} objects. The nodes are the indices as given in edge matrix of an phylo object. From taxon labels these indices can be easily derived matching against the \code{tip.label} argument of an phylo object, see example below. All the functions allow \code{node} to be either a scalar or vector. \code{mrca} is a faster version of the mrca in ape, in phangorn only because of dependencies. } \value{ a vector or a list containing the indices of the nodes. } \seealso{\code{treewalk}, \code{\link[ape]{phylo}}, \code{\link[ape]{nodelabels}} } \examples{ tree = rtree(10) plot(tree, show.tip.label = FALSE) nodelabels() tiplabels() Ancestors(tree, 1:3, "all") Children(tree, 11) Descendants(tree, 11, "tips") Siblings(tree, 3) mrca.phylo(tree, 1:3) mrca.phylo(tree, match(c("t1", "t2", "t3"), tree$tip)) mrca.phylo(tree) # same as mrca(tree), but faster for large trees } \keyword{misc} phangorn/man/cladePar.Rd0000644000175100001440000000240612761653173014676 0ustar hornikusers\name{cladePar} \alias{cladePar} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Utility function to plot.phylo } \description{ cladePar can help you coloring (choosing edge width/type) of clades. } \usage{ cladePar(tree, node, edge.color = "red", tip.color = edge.color, edge.width = 1, edge.lty = 1, x = NULL, plot = FALSE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{tree}{ an object of class phylo. } \item{node}{ the node which is the common ancestor of the clade. } \item{edge.color}{ see plot.phylo. } \item{tip.color}{ see plot.phylo. } \item{edge.width}{ see plot.phylo. } \item{edge.lty}{ see plot.phylo. } \item{x}{ the result of a previous call to cladeInfo. } \item{plot}{ logical, if TRUE the tree is plotted. } \item{\dots}{ Further arguments passed to or from other methods. } } \value{ A list containing the information about the edges and tips. } \author{ Klaus Schliep \email{klaus.schliep@gmail.com} } \seealso{ \code{\link{plot.phylo}} } \examples{ tree = rtree(10) plot(tree) nodelabels() x = cladePar(tree, 12) cladePar(tree, 18, "blue", "blue", x=x, plot=TRUE) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{plot} phangorn/man/getClans.Rd0000644000175100001440000001426412761653173014730 0ustar hornikusers\name{getClans} \alias{getClans} \alias{getClips} \alias{getSlices} \alias{getDiversity} \alias{diversity} \title{ Clans, slices and clips } \description{ Functions for clanistics to compute clans, slices, clips for unrooted trees and functions to quantify the fragmentation of trees. } \usage{ getClans(tree) getClips(tree, all=TRUE) getSlices(tree) getDiversity(tree, x, norm=TRUE, var.names = NULL, labels="new") diversity(tree, X) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{tree}{An object of class phylo or multiPhylo (getDiversity).} \item{all}{A logical, return all or just the largest clip. } \item{x}{An object of class phyDat. } \item{norm}{A logical, return Equitability Index (default) or Shannon Diversity. } \item{var.names}{A vector of variable names. } \item{labels}{see details. } \item{X}{a data.frame} } \details{ Every split in an unrooted tree defines two complementary clans. Thus for an unrooted binary tree with \eqn{n} leaves there are \eqn{2n - 3} edges, and therefore \eqn{4n - 6} clans (including \eqn{n} trivial clans containing only one leave). Slices are defined by a pair of splits or tripartitions, which are not clans. The number of distinguishable slices for a binary tree with \eqn{n} tips is \eqn{2n^2 - 10n + 12}. %A clip is a different type of partition as it is defined by evolutionary or cophenetic distance and not by the topology. Namely clips are groups of leaves for which the maximum pairwise distance is smaller than threshold. %For a better separation we additionally demand that the maximum pairwise distance within a clip is lower than the distance between any member of the clip and any other tip. A clip is a different type of partition, defining groups of leaves that are related in terms of evolutionary distances and not only topology. Namely, clips are groups of leaves for which all pairwise path-length distances are smaller than a given threshold value (Lapointe et al. 2010). There exists different numbers of clips for different thresholds, the largest (and trivial) one being the whole tree. There is always a clip containing only the two leaves with the smallest pairwise distance. Clans, slices and clips can be used to characterize how well a vector of categorial characters (natives/intruders) fit on a tree. We will follow the definitions of Lapointe et al.(2010). A complete clan is a clan that contains all leaves of a given state/color, but can also contain leaves of another state/color. A clan is homogeneous if it only contains leaves of one state/color. \code{getDiversity} computes either the \cr Shannon Diversity: \eqn{H = -\sum_{i=1}^{k}(N_i/N) log(N_i/N), N=\sum_{i=1}^{k} N_i}{H = -sum(N_i/N) * log(N_i/N), N=sum(N_i)} \cr or the \cr Equitability Index: \eqn{E = H / log(N)} \cr where \eqn{N_i} are the sizes of the \eqn{k} largest homogeneous clans of intruders. If the categories of the data can be separated by an edge of the tree then the E-value will be zero, and maximum equitability (E=1) is reached if all intruders are in separate clans. getDiversity computes these Intruder indices for the whole tree, complete clans and complete slices. Additionally the parsimony scores (p-scores) are reported. The p-score indicates if the leaves contain only one color (p-score=0), if the the leaves can be separated by a single split (perfect clan, p-score=1) or by a pair of splits (perfect slice, p-score=2). So far only 2 states are supported (native, intruder), however it is also possible to recode several states into the native or intruder state using contrasts, for details see section 2 in vignette("phangorn-specials"). Furthermore unknown character states are coded as ambiguous character, which can act either as native or intruder minimizing the number of clans or changes (in parsimony analysis) needed to describe a tree for given data. Set attribute labels to "old" for analysis as in Schliep et al. (2010) or to "new" for names which are more intuitive. \code{diversity} returns a data.frame with the parsimony score for each tree and each levels of the variables in \code{X}. \code{X} has to be a \code{data.frame} where each column is a factor and the rownames of \code{X} correspond to the tips of the trees. %TODO See also vignette("Clanistic"). } \value{ getClans, getSlices and getClips return a matrix of partitions, a matrix of ones and zeros where rows correspond to a clan, slice or clip and columns to tips. A one indicates that a tip belongs to a certain partition. \cr getDiversity returns a list with tree object, the first is a data.frame of the equitability index or Shannon divergence and parsimony scores (p-score) for all trees and variables. The data.frame has two attributes, the first is a splits object to identify the taxa of each tree and the second is a splits object containing all partitions that perfectly fit. } \references{ Lapointe, F.-J., Lopez, P., Boucher, Y., Koenig, J., Bapteste, E. (2010) Clanistics: a multi-level perspective for harvesting unrooted gene trees. \emph{Trends in Microbiology} 18: 341-347 Wilkinson, M., McInerney, J.O., Hirt, R.P., Foster, P.G., Embley, T.M. (2007) Of clades and clans: terms for phylogenetic relationships in unrooted trees. \emph{Trends in Ecology and Evolution} 22: 114-115 Schliep, K., Lopez, P., Lapointe F.-J., Bapteste E. (2011) Harvesting Evolutionary Signals in a Forest of Prokaryotic Gene Trees, \emph{Molecular Biology and Evolution} 28(4): 1393-1405 } \author{ Klaus Schliep \email{klaus.schliep@snv.jussieu.fr} Francois-Joseph Lapointe \email{francois-joseph.lapointe@umontreal.ca} } \seealso{ \code{\link{parsimony}}, Consistency index \code{\link{CI}}, Retention index \code{\link{RI}}, \code{\link{phyDat}} } \examples{ set.seed(111) tree = rtree(10) getClans(tree) getClips(tree, all=TRUE) getSlices(tree) set.seed(123) trees = rmtree(10, 20) X = matrix(sample(c("red", "blue", "violet"), 100, TRUE, c(.5,.4, .1)), ncol=5, dimnames=list(paste('t',1:20, sep=""), paste('Var',1:5, sep="_"))) x = phyDat(X, type = "USER", levels = c("red", "blue"), ambiguity="violet") plot(trees[[1]], "u", tip.color = X[trees[[1]]$tip,1]) # intruders are blue (divTab <- getDiversity(trees, x, var.names=colnames(X))) summary(divTab) } \keyword{ cluster } phangorn/man/pmlCluster.Rd0000644000175100001440000000550412761653173015317 0ustar hornikusers\name{pmlCluster} \alias{pmlCluster} %\alias{pmlCluster2} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Stochastic Partitioning } \description{ Stochastic Partitioning of genes into p cluster. } \usage{ pmlCluster(formula, fit, weight, p=1:5, part=NULL, nrep = 10, control=pml.control(epsilon=1e-8, maxit=10, trace=1),...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ a formula object (see details).} \item{fit}{ an object of class \code{pml}. } \item{weight}{ \code{weight} is matrix of frequency of site patterns for all genes. } \item{p}{ number of clusters. } \item{part}{ starting partition, otherwise a random partition is generated. } \item{nrep}{ number of replicates for each p. } \item{control}{A list of parameters for controlling the fitting process.} \item{\dots}{ Further arguments passed to or from other methods. } } \details{ The \code{formula} object allows to specify which parameter get optimized. The formula is generally of the form \code{edge + bf + Q ~ rate + shape + \dots}, on the left side are the parameters which get optimized over all cluster, on the right the parameter which are optimized specific to each cluster. The parameters available are \code{"nni", "bf", "Q", "inv", "shape", "edge", "rate"}. Each parameter can be used only once in the formula. There are also some restriction on the combinations how parameters can get used. \code{"rate"} is only available for the right side. When \code{"rate"} is specified on the left hand side \code{"edge"} has to be specified (on either side), if \code{"rate"} is specified on the right hand side it follows directly that \code{edge} is too. } \value{ \code{pmlCluster} returns a list with elements \item{logLik}{log-likelihood of the fit} \item{trees}{a list of all trees during the optimization.} \item{fits}{fits for the final partitions} } \references{ K. P. Schliep (2009). Some Applications of statistical phylogenetics (PhD Thesis) Lanfear, R., Calcott, B., Ho, S.Y.W. and Guindon, S. (2012) PartitionFinder: Combined Selection of Partitioning Schemes and Substitution Models for Phylogenetic Analyses. \emph{Molecular Biology and Evolution}, \bold{29(6)}, 1695-1701 } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{pml}},\code{\link{pmlPart}},\code{\link{pmlMix}},\code{\link{SH.test}} } \examples{ \dontrun{ data(yeast) dm <- dist.logDet(yeast) tree <- NJ(dm) fit=pml(tree,yeast) fit = optim.pml(fit) weight=xtabs(~ index+genes,attr(yeast, "index")) set.seed(1) sp <- pmlCluster(edge~rate, fit, weight, p=1:4) sp SH.test(sp) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ cluster } %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line phangorn/man/read.aa.Rd0000644000175100001440000000322712761653173014460 0ustar hornikusers\name{read.aa} \alias{read.aa} \title{Read Amino Acid Sequences in a File} \usage{ read.aa(file, format = "interleaved", skip = 0, nlines = 0, comment.char = "#", seq.names = NULL) } \arguments{ \item{file}{a file name specified by either a variable of mode character, or a double-quoted string.} \item{format}{a character string specifying the format of the DNA sequences. Three choices are possible: \code{"interleaved"}, \code{"sequential"}, or \code{"fasta"}, or any unambiguous abbreviation of these.} \item{skip}{the number of lines of the input file to skip before beginning to read data.} \item{nlines}{the number of lines to be read (by default the file is read until its end).} \item{comment.char}{a single character, the remaining of the line after this character is ignored.} \item{seq.names}{the names to give to each sequence; by default the names read in the file are used.} } \description{ This function reads amino acid sequences in a file, and returns a matrix list of DNA sequences with the names of the taxa read in the file as row names.} \value{ a matrix of amino acid sequences. } \references{ % Anonymous. FASTA format description. % \url{http://www.ncbi.nlm.nih.gov/BLAST/fasta.html} Felsenstein, J. (1993) Phylip (Phylogeny Inference Package) version 3.5c. Department of Genetics, University of Washington. \url{http://evolution.genetics.washington.edu/phylip/phylip.html} } \seealso{ \code{\link[ape]{read.dna}}, \code{\link[ape]{read.GenBank}}, \code{\link[phangorn]{phyDat}}, \code{\link[seqinr]{read.alignment}} } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \keyword{IO} phangorn/man/pmlPart.Rd0000644000175100001440000000514612761653173014606 0ustar hornikusers\name{pmlPart} \alias{pmlPart} \alias{pmlPart2multiPhylo} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Partition model. } \description{ Model to estimate phylogenies for partitioned data. } \usage{ pmlPart(formula, object, control = pml.control(epsilon=1e-8, maxit=10, trace=1), model=NULL, rooted=FALSE, ...) pmlPart2multiPhylo(x) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ a formula object (see details).} \item{object}{ an object of class \code{pml} or a list of objects of class \code{pml} . } \item{control}{A list of parameters for controlling the fitting process.} \item{model}{A vector containing the models containing a model for each partition.} \item{rooted}{Are the gene trees rooted (ultrametric) or unrooted.} \item{\dots}{Further arguments passed to or from other methods. } \item{x}{an object of class \code{pmlPart} } } \details{ The \code{formula} object allows to specify which parameter get optimized. The formula is generally of the form \code{edge + bf + Q ~ rate + shape + \dots}, on the left side are the parameters which get optimized over all partitions, on the right the parameter which are optimized specific to each partition. The parameters available are \code{"nni", "bf", "Q", "inv", "shape", "edge", "rate"}. Each parameters can be used only once in the formula. \code{"rate"} and \code{"nni"} are only available for the right side of the formula. For partitions with different edge weights, but same topology, \code{pmlPen} can try to find more parsimonious models (see example). \code{pmlPart2multiPhylo} is a convenience function to extract the trees out of a \code{pmlPart} object. } \value{ \code{kcluster} returns a list with elements \item{logLik}{log-likelihood of the fit} \item{trees}{a list of all trees during the optimization.} \item{object}{an object of class \code{"pml"} or \code{"pmlPart"}} } %\references{ ~put references to the literature/web site here ~ } \author{Klaus Schliep \email{klaus.schliep@gmail.com}} \seealso{ \code{\link{pml}},\code{\link{pmlCluster}},\code{\link{pmlMix}},\code{\link{SH.test}} } \examples{ data(yeast) dm <- dist.logDet(yeast) tree <- NJ(dm) fit <- pml(tree,yeast) fits <- optim.pml(fit) weight=xtabs(~ index+genes,attr(yeast, "index"))[,1:10] sp <- pmlPart(edge ~ rate + inv, fits, weight=weight) sp \dontrun{ sp2 <- pmlPart(~ edge + inv, fits, weight=weight) sp2 AIC(sp2) sp3 <- pmlPen(sp2, lambda = 2) AIC(sp3) } } \keyword{ cluster } %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line