lsmeans/0000755000175100001440000000000013014014030011714 5ustar hornikuserslsmeans/inst/0000755000175100001440000000000013013732335012707 5ustar hornikuserslsmeans/inst/CITATION0000644000175100001440000000120513013732335014042 0ustar hornikusersbibentry(bibtype = "Article", title = "Least-Squares Means: The {R} Package {lsmeans}", author = person(given = c("Russell", "V."), family = "Lenth"), journal = "Journal of Statistical Software", year = "2016", volume = "69", number = "1", pages = "1--33", doi = "10.18637/jss.v069.i01", header = "To cite lsmeans in publications use:", textVersion = paste("Russell V. Lenth (2016).", "Least-Squares Means: The R Package lsmeans.", "Journal of Statistical Software, 69(1), 1-33.", "doi:10.18637/jss.v069.i01") ) lsmeans/inst/NEWS0000644000175100001440000005703613013732335013421 0ustar hornikusersChangelog for 'lsmeans' package 2.25 Now supports models with just the intercept Added support for lsmeans(model, ~1) or lsmeans(model, "1") so that you may obtain the average over all factors. Corrected bug in lme support when data are subsetted Updated and clarified license info to make it more compatible with licenses of packages that depend on this one 2.24 lsm.options now returns the options visibly if no arguments are provided S4 class definitions modernized to use `slots` instead of `representation` 'ref.grid' gets a new 'transform' argument that can be used instead of following it with a call to 'regrid' lstrends now correctly handles response transformations when constructed with transform = "response" lsmeans with a 'trend' argument can now have character 'specs', and is generally more robust to user errors. contrast() now handles log and logit models somewhat differently: - inverse transformations are labeled as ratios whenever all the linear functions sum to 0 (previously just pairwise comparisons) - contrasts of logits are now annotated as being on the "log odds ratio" scale ref.grid and related functions previously did not detect response transformations when model call had no argument named "formula" (e.g., nlme::lme(fixed, random, ...) didn't work) Now can handle not only models where covariates are made into factors, but ones where factors are made into covariates, e.g., y ~ factor(x) + unclass(trt) New option 'lmer.df' and corresponding 'mode' argument for 'merMod' objects, allows user to specify Satterthwaite (new default), K-R, or asymptotic degrees of freedom. CAUTION: This may alter the results obtained by default with 'merMod' objects. Use 'lsm.options(lmer.df = "ken")' if you want the same behavior as before Added a 'df' argument to 'ref.grid' to make it more user-friendly Fixed bug in recover.data.call with incorrect call to 'complete.cases' This fix could change the reference values for covariates from past results - especially for lme models or where 'data' is used Addition to "extending" vignette illustrating how the 'rsm' package supports 'lsmeans' Misc. minor bug fixes and code improvements 2.23-5 More graceful handling of mu.hat <= 0 in regrid(., "log") Added 'predict.type' argument to 'regrid' Bug fixes related to the case where all estimates are NA Addition of 'reversed' argument to 'cld' Bug fixes for na.action handling. Results may now differ somewhat from past versions, especially if you have covariates Fixed bug in 'lstrends' when there is a response transformation Fixed new bug that popped up when tbl_df objects are used Under-the-radar support for Satterthwaite method from lmerTest. To try it: `lsm.options(disable.satterth = FALSE)` 2.23 Added xtable support (actually uses xtableList) Corrected bugs in rbind.ref.grid that caused error when rank-deficient, and incorrectly reported 'avgd.over' information In summary.ref.grid with type = "response", ensured CIs are never reversed and SEs are always nonnegative Fixed a bug in reverse-transforming estimates and CIs when the a linear predictor is invalid (e.g., a negative sqrt) Greatly increased transformation capabilities via 'make.tran' Made messages more explicit about which transformation is/was used Renamed 'adjustSigma' to 'sigmaAdjust' in 'lme' support to avoid a conflict with 'adjust' Improvements to lsm() which previously did not pass arguments like 'at' and 'by' to ref.grid Added glht.list methods: coef, confint, plot, vcov Added "log" capability to 'regrid' 'using-lsmeans' vignette updated to include more information on 'xtable', 'make.tran', and 'regrid' 'using-lsmeans' now also has a comprehensive index Added .Last.ref.grid feature and lsm.option "save.ref.grid" Expanded options to include null, side, and delta 2.22 If confidence intervals are requested and 'adjust' doesn't make sense (e.g., "fdr"), we now default to "bonferroni" instead of "none", and don't override the adjustment made for tests. 'update.ref.grid' now also allows directly replacing slots. A new 'interaction' argument is added to 'contrast', thus facilitating generation of interaction contrasts. Improvements to 'test' with 'joint = TRUE' Better error message in cases where no estimates are produced. Basic support for 'betareg' objects CITATION file added to reflect related JSS article. 2.21-1 Changed R dependency to >= 3.2; needed for rbind support 2.21 Changed implementation of pbkrtest.limit - now based on number of rows of design matrix. Added support for "hurdle" and zeroinfl" classes (pscl package) In 'regrid', dffun now implements a containment method Additional annotation to clarify when results are on the linear-predictor scale. Added courtesy wrappers pmmeans, pmtreans, pmmip, ... for those who prefer "predicted marginal means" to "least-squares means". Output is re-labeled as well. Export a few hidden fcns of possible use to package developers Updates to "extending" vignette Miscellaneous bug fixes, including repair to d.f. method from `regrid` 2.20-23 Added 'get.lsm.option()', works like 'getOption()' but with defaults obtained from 'lsmeans::defaults' Added pbkrtest.limit option -- to limit object size of 'lmerMod' objects for which the K-R method is used, to prevent excessive time/memory Fixed bug that occurs with models where the response is not discernible from the call. In plot.lsmobj, int.adjust now defaults to object's 'adjust' setting (as it should have originally) 2.20-2 Fixed error in df for joint tests with aovlist objects (but existing ref.grid objects need to be rebuilt for it to work) Patched problem with rbind with R < 3.2 2.20 Added 'rbind' function for combining ref.grids into one family Added "[.ref.grid" method for subsetting a reference grid Efficiency/robustness improvements to recover.data for certain models Removed 'mixed' support -- afex >= 0.14 has its own lsmeans support Added support for 'nnet::multinom' objects Returned table from 'lsmip' now includes a "lattice" attribute; also an additional 'plotit' argument to control its display Changed DESCRIPTION to produce a clearer citation 2.19 Added optional `params` argument for `ref.grid` and related functions - use it to specify variables in the model formula that are not predictors Added optional `vcov.` argument so user can override the default vcov() with a function or specified matrix Now supports model formulas containing `$` and `[[]]` (and related exported function `All.vars`) Revised support for survreg, coxph, coxme -- strata and cluster no longer in reference grid. Previous coxme support was bad for other reasons as well Added as.glht method for 'lsm.list', similar to other methods for this class In 'contrast', when 'method' is a list, names are kept as-is whereas previously there was an implicit 'make.names' call Imports of globals from default packages per a change in CRAN checking requirements. 2.18 In DESCRIPTION, moved all Enhances packages to Suggests; Enhancements can't be tested and cause headaches Fixed bug in lstrends - 'data' argument was not used correctly Fixed bug in cld where fatal error occurs when type = "response" Added temporary workaround for 'maov' and 'manova' objects (error in stats coef.maov now fixed in R-devel) Re-enabled existing code for 'mer' support (lme4.0) -- previously had trouble with > 1 Additional_repositories Made "dunnettx"-adjusted critical values more robust Coding improvements for adjusted p values and critical values Added 'consec.lsmc' and 'mean_chg.lsmc' to available contrasts Added support for the CARBayes package 2.17 Removed unneeded import of 'Matrix' (could have done this in 2.12 when I stopped using my own adaptation of 'pbkrtest' code) Bug fix for gls, lme, nlme objects with 'weights' argument Allows for character predictors in models as well as factors Exported selected S3 methods for lsm.basis and recover.data for use by other packages Added 'post.beta' slot in 'ref.grid' and 'lsmobj' classes; if not NA, holds sample from posterior of fixed effects. Also added 'as.mcmc' function to return posterior sample of lsmeans when 'post.beta' has data Added support for 'MCMCglmm' and some 'mcmc' objects produced by the 'MCMCpack' package 2.16 Added support for 'nlme' objects (for parameters in 'fixed') (and associated additional vignette example) Degrees of freedom now produced with lme and nlme objects (uses containment-like method) Addition of 'name' argument in 'contrast' Now built with byte-compiler enabled Bug fix in lstrends when there is a multivariate response Corrected error in annotation for Tukey-adjusted P values Other minor bug fixes and documentation improvements 2.15 Clarified annotations for Tukey and Scheffe-adjusted summaries Moved estimability code to a separate 'estmability' package Basic support for model objects in rms package Explicit non-support for gam (previously ran due to inheritance, but produced the wrong results!) 2.14 Model weights are now used instead of frequencies when weights are not "equal" Fixed error in 'lstrends' when 'specs' is two-sided formula Added more/better references for LS means Yet more enhancements for for clm, clmm, polr... "mean.class" mode, uses response name for "prob" mode "scale" mode does lsmeans for estimated log-scale Enhancements to 'weights' argument in 'lsmeans': - May now provide a matrix - it cycles through its rows - weights = "show.levels" shows the factor comb's averaged Added 'reverse' option to 'pairs' Added 'type' argument in 'ref.grid' for convenience Fixed some errors/omissions in documentation of 'update' One-sided 'tail' spec now applies to CIs as well as tests New adjustment methods added: "mvt" provides exact one-step methods like Tukey, Dunnett "dunnettx" is approximate and only available for 2-sided cases Added contrast fcns "tukey" and "dunnett" - these are pseudonyms for "pairwise" and "trt.vs.ctrl" resp. More adjustments are available for confidence intervals Tries harder to use only appropriate adjust methods New 'estType' attribute for 'update' related to the above 'cld' accommodates when 'multcompView' isn't installed, thus can still pass checks without that package Removed .old.lsmeans and associated vignette 2.13 Provides for equivalence, noninferiority, etc. tests via 'side' and 'delta' arguments in 'summary' or 'test'. Also for non-zero null values via 'null' Added support for ordinal package objects 'clm', 'clmm' Option 'mode' added for 'polr' objects, defaulting to "latent". NOTE - NEED TO SET 'mode = "linear.predictor" TO REPRODUCE PAST RESULTS FOR 'polr' OBJECTS. nlme and lme4 moved to 'Enhances' field for consistency ### Added limited support for mer objects (lme4.0 package) ### I dropped this because it messes-up the CRAN checks Added 'adjustSigma' option for 'lme' objects (works like in 'summary.lme') Fixed error in handling offsets for 'polr' objects Fixed error in omitting modeled covariates when there was a multivariate response. Fixed bug in 'contrast' that failed to update 'pri.vars' which, e.g., creates an error in a subsequent plot Fixed bug in plot - it had ignored xlab when horizontal = FALSE In lsmip with type = "response", the vertical axis is now labeled with misc$inv.lbl when available Removed frequencies and offsets from row labels in 'as.glht' Fixed 'as.glht' so df may be omitted or set to NA for asymptotics Renamed the 'parm' argument in 'test' to 'null' Removed superfluous "P values are asymptotic" messages. Revision to function producing estimates, SEs, and df Added provision to specify 'estHook' and 'vcovHook' in 'misc' slot to allow for custom routines for estimates and covariances. '.is.estble' is renamed 'is.estble' -- exported and documented. The 'by' variable for 'test' now also works when 'joint = TRUE' 2.12 *** Developers supporting lsmeans please note: *** *** Change to lsm.basis prototype: Added a "..." argument *** Removed own patch for df from pbkrtest in favor of its Lb_ddf. This means you must have pbkrtest 0.4-1 or newer Added 'plot.lsmobj' method to display confidence intervals These plots also optionally can display comparison arrows that attempt to display significance of pairwise comparisons according to their degree of overlap Added 'joint' argument to 'test' Fixed a bug in vcov Modified 'auto.noise' so 'side' has levels 'L' and 'R' rather than 'R' and 'L' (so not in reverse position in plots!) Added support for: gee, geeglm, and geese objects (gee, geepack); glmmadmb objects (glmmADMB); mixed objects (afex) Added 'lsmobj' function to construct an lsmobj from summary statistics Overhauled 'lsmeans-package' help page to provide an overview with links to key functions, and integrated details of model-specific implementation in a new 'models' help page 2.11 Added vcov method for ref.grid Added 'weights' argument for lsmeans - may be numeric, or the string "equal", "proportional", "outer", or "cells" Enhanced 'cov.reduce' argument in 'ref.grid' to allow formulas, useful when you think covariates depend on other variables. Also, when cov.reduce is a list, it may now include anything -- function, logical, or formula Additional dataset 'feedlot' Now 'cld' does not break when there are non-estimable lsmeans Fixed situation where one error message masks another more informative one Added possibility of 'summary=' spec for lsm.options, which sets defaults for summary, predict, and lsmip Improvements/updates to vignettes Minor coding/efficiency improvements Removed 'df' arg for ref.grid, stupidly added in 2.10 (we can set df as part of 'options' arg) 2.10 Support for 'aovlist' objects (does intra-block analysis) To use, avoid contr.treatment; use contr.sum or other that sums to 0 Added 'contrast', 'test', 'pairs', 'confint', 'cld' methods for lsm.list, with 'which' argument defaulting to 1 Added 'disable.pbkrtest' option (via lsm.options) if you don't want bias correction and K-R degrees of freedom with lmerMod objects Added 'df' argument to 'ref.grid' constructor, also 'summary' and 'update'. 2.05 Corrected error in Tukey-adjusted confidence intervals New dataset 'oranges' Completely revised, improved vignette 'using-lsmeans' New vignette 'extending lsmeans' New 'mult.name' argument in 'ref.grid' New 'lsm.options' function and associated support for setting display defaults for various objects Improved estimability checking and ability to set the tolerance via lsmeans.options(estble.tol = ...) Added estimability support for merMod, now that lme4 supports rank-deficient models Miscellaneous small improvements to code and datasets 2.00-5 Additional message showing what factors have been averaged over Now adds offsets to predictions when model contains offset() terms (but not for an 'offset' argument) New 'update' method for "ref.grid" objects Broader support for transformations (see ?update.ref.grid) Changed all S3 methods for "lsmobj", except 'show', to S3 methods for the "ref.grid" class, since they all work just fine for this class as well. Added an example for 'lstrends' Removed unneeded misc$ylevs element in ref.grid objects New way to bypass unneeded package-loading from pbkrtest Added an example for lstrends as there wasn't one Fixed minor bug in summary() - a try() call should have been with silent=TRUE 2.00-4 Reverted to code that just loads pbkrtest and its required (but unneeded) packages when pbkrtest is needed 2.00-3 Slight change in use of attach() in lsm.basis.merMod 2.00-2 Added some support for adjusted confidence limits Minor bug fix to make recover.data work with more gls models Fewer unneeded packages are loaded 2.00-1 Fixed a minor formatting issue in a vignette 2.00-0 This is a major redesign of the package. Single function 'lsmeans' is replaced by methods for different types of models and specifications, and returns an 'lsmobj' rather than a 'data.frame'. New functions 'ref.grid', 'contrast', 'lstrends', and new 'summary', 'confint', 'test', 'glht', 'cld' methods for 'ref.grid' and 'lsmobj' objects. See the vignette "lsmeans-changes" for more details. This version is easily extensible to various types of model objects, and several more are supported such as survreg, coxph, polr. There is also full support for na.actions, and for back-transforming results when a response is transformed or a link function is used. 1.10-4 Fixed bug in cld spec that caused multiple letter columns 1.10-3 Added cld capability Fix to lsmip so factor levels are not re-ordered Added new dataset: 'auto.noise' Fixed problem in documentation link to another package 1.10-2 Added simple support for models with a multivariate response Bug fix for case when only one contrast is specified Examples that use lme4 package are made conditional per CRAN requirements since lme4 version 1.0-4 and later depend on a non-portable package Dependency modifications to meet CRAN requirements. Includes adding multcomp as required package, since we enhance glht 1.10-01 Now works again for 'gls' objects 1.10-00 Added contrast families for effects and "deleted effects" Added "scheffe" option for 'adjust' argument Added lsmip function (interaction plots) Fixed Tukey-adjusted P values for d.f. near 2. (Note: ptukey does not work when there are less than 2 d.f.) Allow covariates in 'specs' formula (also allows covariates to have multiple levels and be part of the reference grid that is averaged over in determining LS means) Allow cov.reduce to be logical - mostly to implement a 'unique' option No d.f. or adjusted covariance matrix for GLMMs (questionable validity, plus pbkrtest won't play along) Now checks lengths of contrast coef's for conformity Fixed a bug in estimability checking (occurred when qr.R does not return a square matrix) Efficiency improvements. Now requires 'plyr' package 1.06-06 - June 13, 2013 Added support for lmerMod objects (gitHub version of lme4) 1.06-05 - February 13, 2013 Fixed a bug whereby unused levels of factors can create havoc. 1.06-00 - January 14, 2013 Added a 'trend' argument for estimating and contrasting fitted trends rather than lsmeans 1.05-01 - December 5, 2012 Fixed a bug that occurs when model contains expressions like (a + b + c)^2 or other expressions that expand to interaction terms 1.05-00 - November 2, 2012 Added an 'lf' boolean argument to return the matrices of linear functions without evaluating them Added a function 'lsm' and corresponding method for 'glht'; thus, now we can use lsm() much like mcp() Changed examples with glht to integer df -- due to a recent change in mvtnorm glhargs$df is coerced to an integer before passing, but in lsm() it is up to the user to pass appropriate arguments to glht 1.00-00 - October 19, 2012 Added support for factors in 'at' argument (can use it to restrict results) Added columns with factor levels to lsmeans table - for possible use in plots Extended trt.vs.ctrl.lsmc to accommodate more than one reference level (Thanks to Florent Duyme, ARVALIS - Institut du v?g?tal, for suggesting this) Refinements to print methods, including new class 'data.frame.lsm' to display results the way I want them If 'glhargs' is used, those elements are now of class 'summary.glht'. We can still use those results in the same ways as before Added support for adjustment methods in 'p.adjust' (stats) Included a couple of sample datasets Various small bug fixes (e.g. subsets bug) 0.99-80 - September 19, 2012 Now finds degrees of freedom when appropriate for all but lme objects (For mer objects, get df only if 'pbkrtest' installed) lsmeans tables now show confidence intervals instead of t tests and contrast output shows p values (both assuming df are available) Added a 'conf' argument for confidence level in lsmean intervals Added a 'glhargs' argument; if provided, lsmeans passes its contrasts to the 'glht' function in the 'multcomp' package Added an 'adjust' argument for popular single-step multiplicity corrections (uncorrected, Tukey, Bonferroni, and Sidak) Contrast-generating functions now produce an 'adjust' attribute which provides a default multiplicity-adjustment method The 'check.cells' argument is now deprecated. Instead, we do an actual check of estimability in rank-deficient cases (currently can happen only with 'lm' objects). This check is not optional Renamed the vignette to avoid confusion with the regular documentation Returned object is now assigned class "lsm" which has its own print method. 0.99 - August 27, 2012 Now can handle predictors coerced to 'factor' or 'ordered' Now can handle predictors deleted due to rank deficiency Now can handle models with matrices among the predictors (uses 'cov.reduce' on each column; 'at' not supported for matrix covariate) Added support for gls objects (and documented that it works also for glm and glmer) Added a check for empty cells in the design (relative to the model). This (by default) forces a NA to be outputted for any lsmeans that are corrupted by rank deficiencies Generates a warning if specified factor(s) interact with other predictors Better and more comprehensive vignette Added a whole lot of comments to the code (you won't see these, but be glad, as it greatly aids maintainability) Added error messages for anticipatable user errors Numerous bug fixes and corrections to documentation Still no d.f. or P values. Those could take awhile. 0.90 - August 14, 2012 First release, still needs some refinements and bullet-proofinglsmeans/inst/doc/0000755000175100001440000000000013013732364013456 5ustar hornikuserslsmeans/inst/doc/extending.pdf0000644000175100001440000104151413013732364016144 0ustar hornikusers%PDF-1.5 % 1 0 obj << /S /GoTo /D (section.1) >> endobj 4 0 obj (Introduction) endobj 5 0 obj << /S /GoTo /D (section.2) >> endobj 8 0 obj (Data example) endobj 9 0 obj << /S /GoTo /D (section.3) >> endobj 12 0 obj (Supporting rlm) endobj 13 0 obj << /S /GoTo /D (section.4) >> endobj 16 0 obj (Supporting lqs objects) endobj 17 0 obj << /S /GoTo /D (subsection.4.1) >> endobj 20 0 obj (The recover.data method) endobj 21 0 obj << /S /GoTo /D (subsection.4.2) >> endobj 24 0 obj (The lsm.basis method) endobj 25 0 obj << /S /GoTo /D (subsection.4.3) >> endobj 28 0 obj (Dissecting lsm.basis.lqs) endobj 29 0 obj << /S /GoTo /D (section.5) >> endobj 32 0 obj (Hook functions) endobj 33 0 obj << /S /GoTo /D (section.6) >> endobj 36 0 obj (Exported methods) endobj 37 0 obj << /S /GoTo /D (section.7) >> endobj 40 0 obj (Support for rsm objects) endobj 41 0 obj << /S /GoTo /D (subsection.7.1) >> endobj 44 0 obj (The recover.data method) endobj 45 0 obj << /S /GoTo /D (subsection.7.2) >> endobj 48 0 obj (The lsm.basis method) endobj 49 0 obj << /S /GoTo /D (subsection.7.3) >> endobj 52 0 obj (Exporting the methods) endobj 53 0 obj << /S /GoTo /D (subsection.7.4) >> endobj 56 0 obj (A demonstration) endobj 57 0 obj << /S /GoTo /D (section.8) >> endobj 60 0 obj (Conclusions) endobj 61 0 obj << /S /GoTo /D [62 0 R /Fit] >> endobj 63 0 obj << /Length 439 >> stream concordance:extending.tex:extending.rnw:1 40 1 1 5 1 1 1 0 7 1 1 2 4 0 1 2 5 1 1 2 1 0 1 3 5 0 1 2 3 1 1 2 1 0 1 1 1 2 1 1 18 0 1 2 3 1 1 2 4 0 1 2 2 1 1 2 14 0 2 2 4 0 2 2 1 0 1 1 12 0 1 2 11 1 1 2 8 0 1 2 4 1 1 2 19 0 1 2 1 1 1 12 14 0 1 2 1 1 1 2 19 0 1 2 8 1 1 2 4 0 1 2 18 1 1 2 1 0 1 1 5 0 1 1 6 0 1 2 4 1 1 2 7 0 1 2 14 1 1 25 27 0 1 2 9 1 1 25 27 0 1 2 6 1 1 7 9 0 1 2 7 1 1 2 1 0 1 1 3 0 1 2 1 3 17 0 2 2 11 0 1 2 1 3 17 0 1 2 6 1 endstream endobj 66 0 obj << /Length 2693 /Filter /FlateDecode >> stream xڭn Cjp30 < 'GHTdzK[|V 9D 57JTiU~h,2}~J/w?|^rWW&FP& rhC6Q*kBEa1Qxbvat=?(uz b4& m3[Ԍ/GxBifL?Bf([2.2]|](m84 3<!DGZ#HT5yeN#~@O$Vq@.ᱱ3ptaȅ+:M<ˋjL4uVurb@ͳZEEȤ ZP$08Ûϼ@@FnT~8GBDN@!Ӷ8{#ЃɬO/. 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Lenth % % % \documentclass[11pt]{article} \usepackage[margin=1in]{geometry} \usepackage{mathpazo} \usepackage{fancyvrb} \usepackage{natbib} \usepackage{hyperref} \let\dq=" \DefineShortVerb{\"} \def\pkg#1{\textsf{#1}} \def\lsm{\pkg{lsmeans}} \def\code{\texttt} \def\proglang{\textsf} % double-quoted text \def\dqt#1{\code{\dq{}#1\dq{}}} % The objects I want to talk about \def\rg{\dqt{ref.grid}} \def\lsmo{\dqt{lsmobj}} \def\R{\proglang{R}} \def\SAS{\proglang{SAS}} \def\Fig#1{Figure~\ref{#1}} \def\bottomfraction{.5} \title{Extending \lsm{}} \author{Russell V.~Lenth} %\VignetteIndexEntry{Extending lsmeans} %\VignetteDepends{lsmeans} %\VignetteKeywords{least-squares means} %\VignettePackage{lsmeans} % Initialization <>= options(show.signif.stars=FALSE, prompt="R> ", continue=" ") set.seed(271828) @ \begin{document} \SweaveOpts{concordance=TRUE} \maketitle{} \section{Introduction} Suppose you want to use \lsm{} for some type of model that it doesn't (yet) support. Or, suppose you have developed a new package with a fancy model-fitting function, and you'd like it to work with \lsm{}. What can you do? Well, there is hope because \lsm{} is designed to be extended. The first thing to do is to look at the help page for extending the package: <>= help("extending-lsmeans", package="lsmeans") @ It gives details about the fact that you need to write two S3 methods, "recover.data" and "lsm.basis", for the class of object that your model-fitting function returns. The "recover.data" method is needed to recreate the dataset so that the reference grid can be identified. The "lsm.basis" method then determines the linear functions needed to evaluate each point in the reference grid and to obtain associated information---such as the variance-covariance matrix---needed to do estimation and testing. This vignette presents an example where suitable methods are developed, and discusses a few issues that arise. \section{Data example} The \pkg{MASS} package contains various functions that do robust or outlier-resistant model fitting. We will cobble together some \lsm{} support for these. But first, let's create a suitable dataset (a simulated two-factor experiment) for testing.\footnote{I unapologetically use \code{=} as the assignment operator. It is good enough for C and Java, and supported by R.} <<>>= fake = expand.grid(rep = 1:5, A = c("a1","a2"), B = c("b1","b2","b3")) fake$y = c(11.46,12.93,11.87,11.01,11.92,17.80,13.41,13.96,14.27,15.82, 23.14,23.75,-2.09,28.43,23.01,24.11,25.51,24.11,23.95,30.37, 17.75,18.28,17.82,18.52,16.33,20.58,20.55,20.77,21.21,20.10) @ The $y$ values were generated using predetermined means and Cauchy-distributed errors. There are some serious outliers in these data. \section{Supporting \code{rlm}} The \pkg{MASS} package provides an "rlm" function that fits robust-regression models using $M$~estimation. We'll fit a model using the default settings for all tuning parameters: <<>>= library(MASS) fake.rlm = rlm(y ~ A * B, data = fake) library(lsmeans) lsmeans(fake.rlm, ~B | A) @ The first lesson to learn about extending \lsm{} is that sometimes, it already works! It works here because "rlm" objects inherit from "lm", which is supported by the \lsm{} package, and "rlm" objects aren't enough different to create any problems. \section{Supporting \code{lqs} objects} The \pkg{MASS} resistant-regression functions "lqs", "lmsreg", and "ltsreg" are another story, however. They create "lqs" objects that are not extensions of any other class, and have other issues, including not even having a "vcov" method. So for these, we really do need to write new methods for "lqs" objects. First, let's fit a model. <<>>= fake.lts = ltsreg(y ~ A * B, data = fake) @ \subsection{The \code{recover.data} method} It is usually an easy matter to write a "recover.data" method. Look at the one for "lm" objects: <<>>= lsmeans:::recover.data.lm @ Note that all it does is obtain the "call" component and call the method for class \dqt{call}, with additional arguments for its "terms" component and "na.action". It happens that we can access these attributes in exactly the same way as for "lm" objects; so, \ldots <<>>= recover.data.lqs = lsmeans:::recover.data.lm @ Let's test it: <<>>= rec.fake = recover.data(fake.lts) head(rec.fake) @ Our recovered data excludes the response variable "y" (owing to the "delete.response" call), and this is fine. \paragraph{Special arguments} By the way, there are two special arguments "data" and "params" that may be handed to "recover.data" via "ref.grid" or "lsmeans" or a related function; and you may need to provide for if you don't use the "recover.data.call" function. The "data" argument is needed to cover a desperate situation that occurs with certain kinds of models where the underlying data information is not saved with the object---e.g., models that are fitted by iteratively modifying the data. In those cases, the only way to recover the data is to for the user to give it explicitly, and "recover.data" just adds a few needed attributes to it. The "params" argument is needed when the model formula refers to variables besides predictors. For example, a model may include a spline term, and the knots are saved in the user's environment as a vector and referred to in the call to fit the model. In trying to recover the data, we try to construct a data frame containing all the variables present on the right-hand side of the model, but if some of those are scalars or of different lengths than the number of observations, an error occurs. So you need to exclude any names in "params" when reconstructing the data. \paragraph{Error handling} If you check for any error conditions in "recover.data", simply have it return a character string with the desired message, rather than invoking "stop". This provides a cleaner exit. The reason is that whenever "recover.data" throws an error, an informative message suggesting that "data" or "params" be provided is displayed. But a character return value is tested for and throws a different error with your string as the message. \subsection{The \code{lsm.basis} method} The "lsm.basis" method has four required arguments: <<>>= args(lsmeans:::lsm.basis.lm) @ These are, respectively, the model object, its "terms" component (at least for the right-hand side of the model), a "list" of levels of the factors, and the grid of predictor combinations that specify the reference grid. The function must obtain six things and return them in a named "list". They are the matrix "X" of linear functions for each point in the reference grid, the regression coefficients "bhat"; the variance-covariance matrix "V"; a matrix "nbasis" for non-estimable functions; a function "dffun(k,dfargs)" for computing degrees of freedom for the linear function "sum(k*bhat)"; and a list "dfargs" of arguments to pass to "dffun". To write your own "lsm.basis" function, examining some of the existing methods can help; but the best resource is the "predict" method for the object in question, looking carefully to see what it does to predict values for a new set of predictors (e.g., "newdata" in "predict.lm"). Following this advice, let's take a look at it: <<>>= MASS:::predict.lqs @ \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=left} Based on this, here is a listing of an "lsm.basis" method for "lqs" objects: <<>>= lsm.basis.lqs = function(object, trms, xlev, grid, ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) bhat = coef(object) Xmat = model.matrix(trms, data=object$model) V = rev(object$scale)[1]^2 * solve(t(Xmat) %*% Xmat) nbasis = matrix(NA) dfargs = list(df = nrow(Xmat) - ncol(Xmat)) dffun = function(k, dfargs) dfargs$df list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs) } @ \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=none} Before explaining it, let's verify that it works: <<>>= lsmeans(fake.lts, ~ B | A) @ Hooray! Note the results are comparable to those we had for "fake.rlm", albeit the standard errors are quite a bit smaller. (In fact, the SEs could be misleading; a better method for estimating covariances should probably be implemented, but that is beyond the scope of this vignette.) \subsection{Dissecting \code{lsm.basis.lqs}} Let's go through the listing of this method, by line numbers. \begin{itemize} \item[2--3:] Construct the linear functions, "X". This is a pretty standard standard two-step process: First obtain a model frame, "m", for the grid of predictors, then pass it as data to "model.data" to create the associated design matrix. As promised, this code is essentially identical to what you find in "predict.lqs". \item[4:] Obtain the coefficients, "bhat". Most model objects have a "coef" method. \item[5--6:] Obtain the covariance matrix, "V", of "bhat". In many models, this can be obtained using the object's "vcov" method. But not in this case. Instead, I cobbled one together using what it would be for ordinary regression: $\hat\sigma^2(\mathbf{X}'\mathbf{X})^{-1}$, where $\mathbf{X}$ is the design matrix for the whole dataset (not the reference grid). Here, $\hat\sigma$ is obtained using the last element of the "scale" element of the object (depending on the method, there are one or two scale estimates). This probably under-estimates the variances and distorts the covariances, because robust estimators have some efficiency loss. \item[7:] Compute the basis for non-estimable functions. This applies only when there is a possibility of rank deficiency in the model, and "lqs" methods cannot handle that. All linear functions are estimable, and we signal that by setting "nbasis" equal to a $1\times1$ matrix of "NA". If rank deficiency were possible, the \pkg{estimability} package (which is required by \lsm{}) provides a "nonest.basis" function that makes this fairly painless---I would have coded: <>= nbasis = estimability::nonest.basis(Xmat) @ On the other hand, if rank-deficient cases are not possible, set "nbasis" equal to "all.estble", a constant in the \pkg{estimability} package. There is a subtlety you need to know regarding estimability. Suppose the model is rank-deficient, so that the design matrix $\mathbf{X}$ has $p$ columns but rank $r>= form = ~ data$x + data[[5]] base::all.vars(form) lsmeans::.all.vars(form) @ The "retain" argument may be used to specify regular expressions for patterns to retain as parts of variable names. \item[\code{.diag(x, nrow, ncol)}] The base "diag" function has a booby trap whereby, for example, "diag(57.6)" returns a $57\times 57$ identity matrix rather than a $1\times1$ matrix with $57.6$ as its only element. But "lsmeans::.diag(57.6)" will return the latter. The function works identically to "diag" except for the identity-matrix trap. \item[\code{.aovlist.dffun(k, dfargs)}] This function is exported because it is needed for computing degrees of freedom for models fitted using "aov", but it may be useful for other cases where Satterthwaite degrees-of-freedom calculations are needed. It requires the "dfargs" slot to contain analogous contents. \item[\code{.get.offset(terms, grid)}] If "terms" is a model formula containing an "offset" call, this is will compute that offset in the context of "grid" (a "data.frame"). <<>>= .get.offset(terms(~ speed + offset(.03*breaks)), head(warpbreaks)) @ \item[\code{.my.vcov(object, ...)}] In a call to "ref.grid", "lsmeans", etc., the user may use "vcov." to specify an alternative function or matrix to use as the covariance matrix of the fixed-effects coefficients. This function supports that feature. Calling ".my.vcov" in place of the "vcov" method will substitute the user's "vcov." when it is present in "...". \end{description} \section{Support for \code{rsm} objects} \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=left, fontsize=\small} As an example of how an existing package supports \pkg{lsmeans}, we show the support offered by the \pkg{rsm} package. Its "rsm" function returns an \dqt{rsm} object which is an extension of the \dqt{lm} class. Part of that extension has to do with "coded.data" structures whereby, as is typical in response-surface analysis, models are fitted to variables that have been linearly transformed (coded) so that $\pm1$ on the coded scale represents the scope of each predictor. Without any extra support in \pkg{rsm}, "lsmeans" will work just fine with \dqt{rsm} objects; but if the data are coded, it becomes awkward to present results in terms of the original predictors on their original, uncoded scale. The "lsmeans"-related methods in \pkg{rsm} provide a "mode" argument that may be used to specify whether we want to work with coded or uncoded data. The possible values for "mode" are \dqt{asis} (ignore any codings, if present), \dqt{coded} (use the coded scale), and \dqt{decoded} (use the decoded scale). The first two are actually the same in that no decoding is done; but it seems clearer to provide separate options because they represent two different situations. \subsection{The \code{recover.data} method} Note that coding is a \emph{predictor} transformation, not a response transformation (we could have that, too, as it's already supported by the \pkg{lsmeans} infrastructure). So, to handle the \dqt{decode} mode, we will need to actually decode the predictors used to construct he reference grid. That means we need to make "recover.data" a lot fancier! Here it is: %%%\begin{figure} <>= recover.data.rsm = function(object, data, mode = c("asis", "coded", "decoded"), ...) { mode = match.arg(mode) cod = codings(object) fcall = object$call if(is.null(data)) data = lsmeans::recover.data(fcall, delete.response(terms(object)), object$na.action, ...) if (!is.null(cod) && (mode == "decoded")) { pred = cpred = attr(data, "predictors") trms = attr(data, "terms") data = decode.data(as.coded.data(data, formulas = cod)) for (form in cod) { vn = all.vars(form) if (!is.na(idx <- grep(vn[1], pred))) { pred[idx] = vn[2] cpred = setdiff(cpred, vn[1]) } } attr(data, "predictors") = pred new.trms = update(trms, reformulate(c("1", cpred))) # excludes coded variables attr(new.trms, "orig") = trms # save orig terms as an attribute attr(data, "terms") = new.trms } data } @ %%%\end{figure} Lines 2--6 ensure that "mode" is legal, retrieves the codings from the object, and obtain the results we would get from "recover.data" had it been an \dqt{lm} object. If "mode" is not \dqt{decoded}, \emph{or} if no codings were used, that's all we need. Otherwise, we need to return the decoded data. However, it isn't quite that simple, because the model equation is still defined on the coded scale. Rather than to try to translate the model coefficients and covariance matrix to the decoded scale, we elected to remember what we will need to do later to put things back on the coded scale. In lines~8--9, we retrieve the attributes of the recovered data that provide the predictor names and "terms" object on the coded scale. In line~10, we replace the recovered data with the decoded data. By the way, the codings comprise a list of formulas with the coded name on the left and the original variable name on the right. It is possible that only some of the predictors are coded (for example, blocking factors will not be). In the "for" loop in lines~11--17, the coded predictor names are replaced with their decoded names. For technical reasons to be discussed later, we also remove these coded predictor names from a copy, "cpred", of the list of all predictors in the coded model. In line~18, the \dqt{predictors} attribute of "data" is replaced with the modified version. Now, there is a nasty technicality. The "ref.grid" function in \pkg{lsmeans} has a few lines of code after "recover.data" is called that determine if any terms in the model convert covariates to factors or vice versa; and this code uses the model formula. That formula involves variables on the coded scale, and those variables are no longer present in the data, so an error will occur if it tries to access them. Luckily, if we simply take those terms out of the formula, it won't hurt because those coded predictors would not have been converted in that way. So in line~19, we update "trms" with a simpler model with the coded variables excluded (the intercept is explicitly included to ensure there will be a right-hand side even is "cpred" is empty). We save that as the \dqt{terms} attribute, and the original terms as a new \dqt{orig} attribute to be retrieved later. The "data" object, modified or not, is returned. If data have been decoded, "ref.grid" will construct its grid using decoded variables. \subsection{The \code{lsm.basis} method} Now comes the "lsm.basis" method that will be called after the grid is defined. It is listed below: <>= lsm.basis.rsm = function(object, trms, xlev, grid, mode = c("asis", "coded", "decoded"), ...) { mode = match.arg(mode) cod = codings(object) if(!is.null(cod) && mode == "decoded") { grid = coded.data(grid, formulas = cod) trms = attr(trms, "orig") # get back the original terms we saved } m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) bhat = as.numeric(object$coefficients) V = lsmeans::.my.vcov(object, ...) if (sum(is.na(bhat)) > 0) nbasis = estimability::nonest.basis(object$qr) else nbasis = estimability::all.estble dfargs = list(df = object$df.residual) dffun = function(k, dfargs) dfargs$df list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = dfargs, misc = list()) } @ This is much simpler. All we have to do is determine if decoding was done (line~5); and, if so, convert the grid back to the coded scale (line~6) and recover the original \dqt{terms} attribute (line~7). The rest is borrowed directly from the "lsm.basis.lm" method in \pkg{lsmeans}. Note that line~13 uses one of the exported functions we described in the preceding section. Lines 15--18 use functions from the \pkg{estimability} package to handle the possibility that the model is rank-deficient. \subsection{Exporting the methods} \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=none, fontsize=\small} To make the methods available to users of the \pkg{rsm} package, the following code appears in the "NAMESPACE" file: <>= if (requireNamespace("lsmeans", quietly = TRUE)) { importFrom("lsmeans", "recover.data", "lsm.basis") importFrom("estimability", "all.estble", "nonest.basis") S3method(recover.data, rsm) S3method(lsm.basis, rsm) } @ This only has an effect if the user has the \pkg{lsmeans} package installed (in which case \pkg{estimability} is also installed, as it is required); otherwise the code is skipped. We need to import the prototypes for "recover.data" and "lsm.basis", and register our new methods. Also, packages "lsmeans" and "estimability" are included in the "Imports" section of the "DESCRIPTION" file. Alternatively, we could simply export the functions "recover.data.rsm" and "lsm.basis.rsm" without any need to import anything or register methods. It's simpler to do, but makes those functions user-visible and thus they require documentation. \subsection{A demonstration} Here's a demonstration of this new support. The standard example for "rsm" fits a second-order model "CR.rs2" to a dataset organized in two blocks and with two coded predictors. \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=none, fontsize=\normalsize} <>= library("rsm") example("rsm") ### (output is not shown) ### @ First, let's look at some results on the coded scale---which are the same as for an ordinary \dqt{lm} object. <<>>= lsmeans(CR.rs2, ~ x1 * x2, mode = "coded", at = list(x1 = c(-1, 0, 1), x2 = c(-2, 2))) @ Now, the coded variables "x1" and "x2" are derived from these coding formulas for predictors "Time" and "Temp": <<>>= codings(CR.rs1) @ Thus, for example, a coded value of $x_1=1$ corresponds to a time of $85 + 1\times5 = 90$. Here are some results working with decoded predictors. Note that the "at" list must now be given in terms of "Time" and "Temp": <<>>= lsmeans(CR.rs2, ~ Time * Temp, mode = "decoded", at = list(Time = c(80, 85, 90), Temp = c(165, 185))) @ Since the supplied settings are the same on the decoded scale as were used on the coded scale, the LS~means are identical to those in the previous output. \section{Conclusions} It is relatively simple to write appropriate methods that work with \lsm{} for model objects it does not support. I hope this vignette is helpful for understanding how. Furthermore, if you are the developer of a package that fits linear models, I encourage you to include "recover.data" and "lsm.basis" methods for those classes of objects, so that users have access to \pkg{lsmeans} support. \end{document}lsmeans/inst/doc/extending.R0000644000175100001440000001700113013732364015565 0ustar hornikusers### R code from vignette source 'extending.rnw' ################################################### ### code chunk number 1: extending.rnw:42-44 ################################################### options(show.signif.stars=FALSE, prompt="R> ", continue=" ") set.seed(271828) ################################################### ### code chunk number 2: extending.rnw:55-56 (eval = FALSE) ################################################### ## help("extending-lsmeans", package="lsmeans") ################################################### ### code chunk number 3: extending.rnw:64-68 ################################################### fake = expand.grid(rep = 1:5, A = c("a1","a2"), B = c("b1","b2","b3")) fake$y = c(11.46,12.93,11.87,11.01,11.92,17.80,13.41,13.96,14.27,15.82, 23.14,23.75,-2.09,28.43,23.01,24.11,25.51,24.11,23.95,30.37, 17.75,18.28,17.82,18.52,16.33,20.58,20.55,20.77,21.21,20.10) ################################################### ### code chunk number 4: extending.rnw:74-79 ################################################### library(MASS) fake.rlm = rlm(y ~ A * B, data = fake) library(lsmeans) lsmeans(fake.rlm, ~B | A) ################################################### ### code chunk number 5: extending.rnw:85-86 ################################################### fake.lts = ltsreg(y ~ A * B, data = fake) ################################################### ### code chunk number 6: extending.rnw:91-92 ################################################### lsmeans:::recover.data.lm ################################################### ### code chunk number 7: extending.rnw:95-96 ################################################### recover.data.lqs = lsmeans:::recover.data.lm ################################################### ### code chunk number 8: extending.rnw:99-101 ################################################### rec.fake = recover.data(fake.lts) head(rec.fake) ################################################### ### code chunk number 9: extending.rnw:115-116 ################################################### args(lsmeans:::lsm.basis.lm) ################################################### ### code chunk number 10: extending.rnw:123-124 ################################################### MASS:::predict.lqs ################################################### ### code chunk number 11: extending.rnw:128-139 ################################################### lsm.basis.lqs = function(object, trms, xlev, grid, ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) bhat = coef(object) Xmat = model.matrix(trms, data=object$model) V = rev(object$scale)[1]^2 * solve(t(Xmat) %*% Xmat) nbasis = matrix(NA) dfargs = list(df = nrow(Xmat) - ncol(Xmat)) dffun = function(k, dfargs) dfargs$df list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs) } ################################################### ### code chunk number 12: extending.rnw:143-144 ################################################### lsmeans(fake.lts, ~ B | A) ################################################### ### code chunk number 13: extending.rnw:155-156 (eval = FALSE) ################################################### ## nbasis = estimability::nonest.basis(Xmat) ################################################### ### code chunk number 14: extending.rnw:177-180 ################################################### form = ~ data$x + data[[5]] base::all.vars(form) lsmeans::.all.vars(form) ################################################### ### code chunk number 15: extending.rnw:187-188 ################################################### .get.offset(terms(~ speed + offset(.03*breaks)), head(warpbreaks)) ################################################### ### code chunk number 16: extending.rnw:205-229 (eval = FALSE) ################################################### ## recover.data.rsm = function(object, data, mode = c("asis", "coded", "decoded"), ...) { ## mode = match.arg(mode) ## cod = codings(object) ## fcall = object$call ## if(is.null(data)) ## data = lsmeans::recover.data(fcall, delete.response(terms(object)), object$na.action, ...) ## if (!is.null(cod) && (mode == "decoded")) { ## pred = cpred = attr(data, "predictors") ## trms = attr(data, "terms") ## data = decode.data(as.coded.data(data, formulas = cod)) ## for (form in cod) { ## vn = all.vars(form) ## if (!is.na(idx <- grep(vn[1], pred))) { ## pred[idx] = vn[2] ## cpred = setdiff(cpred, vn[1]) ## } ## } ## attr(data, "predictors") = pred ## new.trms = update(trms, reformulate(c("1", cpred))) # excludes coded variables ## attr(new.trms, "orig") = trms # save orig terms as an attribute ## attr(data, "terms") = new.trms ## } ## data ## } ################################################### ### code chunk number 17: extending.rnw:241-265 (eval = FALSE) ################################################### ## lsm.basis.rsm = function(object, trms, xlev, grid, ## mode = c("asis", "coded", "decoded"), ...) { ## mode = match.arg(mode) ## cod = codings(object) ## if(!is.null(cod) && mode == "decoded") { ## grid = coded.data(grid, formulas = cod) ## trms = attr(trms, "orig") # get back the original terms we saved ## } ## ## m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) ## X = model.matrix(trms, m, contrasts.arg = object$contrasts) ## bhat = as.numeric(object$coefficients) ## V = lsmeans::.my.vcov(object, ...) ## ## if (sum(is.na(bhat)) > 0) ## nbasis = estimability::nonest.basis(object$qr) ## else ## nbasis = estimability::all.estble ## dfargs = list(df = object$df.residual) ## dffun = function(k, dfargs) dfargs$df ## ## list(X = X, bhat = bhat, nbasis = nbasis, V = V, ## dffun = dffun, dfargs = dfargs, misc = list()) ## } ################################################### ### code chunk number 18: extending.rnw:274-280 (eval = FALSE) ################################################### ## if (requireNamespace("lsmeans", quietly = TRUE)) { ## importFrom("lsmeans", "recover.data", "lsm.basis") ## importFrom("estimability", "all.estble", "nonest.basis") ## S3method(recover.data, rsm) ## S3method(lsm.basis, rsm) ## } ################################################### ### code chunk number 19: extending.rnw:290-292 ################################################### library("rsm") example("rsm") ### (output is not shown) ### ################################################### ### code chunk number 20: extending.rnw:295-297 ################################################### lsmeans(CR.rs2, ~ x1 * x2, mode = "coded", at = list(x1 = c(-1, 0, 1), x2 = c(-2, 2))) ################################################### ### code chunk number 21: extending.rnw:300-301 ################################################### codings(CR.rs1) ################################################### ### code chunk number 22: extending.rnw:304-306 ################################################### lsmeans(CR.rs2, ~ Time * Temp, mode = "decoded", at = list(Time = c(80, 85, 90), Temp = c(165, 185))) lsmeans/inst/doc/using-lsmeans.R0000644000175100001440000005373013013732364016376 0ustar hornikusers### R code from vignette source 'using-lsmeans.rnw' ################################################### ### code chunk number 1: using-lsmeans.rnw:69-71 ################################################### options(show.signif.stars=FALSE, prompt="R> ", continue=" ", useFancyQuotes=FALSE, width=100, digits=6) ################################################### ### code chunk number 2: using-lsmeans.rnw:155-158 ################################################### library("lsmeans") oranges.lm1 <- lm(sales1 ~ price1 + price2 + day + store, data = oranges) anova(oranges.lm1) ################################################### ### code chunk number 3: using-lsmeans.rnw:161-162 ################################################### ( oranges.rg1 <- ref.grid(oranges.lm1) ) ################################################### ### code chunk number 4: using-lsmeans.rnw:165-166 ################################################### summary(oranges.rg1) ################################################### ### code chunk number 5: using-lsmeans.rnw:171-172 ################################################### lsmeans(oranges.rg1, "day") ## or lsmeans(oranges.lm1, "day") ################################################### ### code chunk number 6: using-lsmeans.rnw:175-176 ################################################### with(oranges, tapply(sales1, day, mean)) ################################################### ### code chunk number 7: using-lsmeans.rnw:187-189 ################################################### lsmeans(oranges.lm1, "day", at = list(price1 = 50, price2 = c(40,60), day = c("2","3","4")) ) ################################################### ### code chunk number 8: using-lsmeans.rnw:198-201 ################################################### org.lsm <- lsmeans(oranges.lm1, "day", by = "price2", at = list(price1 = 50, price2 = c(40,60), day = c("2","3","4")) ) org.lsm ################################################### ### code chunk number 9: using-lsmeans.rnw:204-207 (eval = FALSE) ################################################### ## lsmeans(oranges.lm1, ~ day | price, at = ... ) # Ex 1 ## lsmeans(oranges.lm1, c("day","price2"), at = ... ) # Ex 2 ## lsmeans(oranges.lm1, ~ day * price, at = ... ) # Ex 3 ################################################### ### code chunk number 10: using-lsmeans.rnw:217-218 ################################################### str(org.lsm) ################################################### ### code chunk number 11: using-lsmeans.rnw:224-226 ################################################### ( org.sum <- summary(org.lsm, infer = c(TRUE,TRUE), level = .90, adjust = "bon", by = "day") ) ################################################### ### code chunk number 12: using-lsmeans.rnw:231-232 ################################################### class(org.sum) ################################################### ### code chunk number 13: using-lsmeans.rnw:236-237 ################################################### transform(org.sum, lsrubles = lsmean * 34.2) ################################################### ### code chunk number 14: using-lsmeans.rnw:245-247 ################################################### org.lsm2 <- update(org.lsm, by.vars = NULL, level = .99) org.lsm2 ################################################### ### code chunk number 15: org-plot ################################################### plot(org.lsm, by = "price2") ################################################### ### code chunk number 16: using-lsmeans.rnw:270-271 ################################################### contrast(org.lsm, method = "eff") ################################################### ### code chunk number 17: using-lsmeans.rnw:276-278 ################################################### days.lsm <- lsmeans(oranges.rg1, "day") ( days_contr.lsm <- contrast(days.lsm, "trt.vs.ctrl", ref = c(5,6)) ) ################################################### ### code chunk number 18: using-lsmeans.rnw:283-284 (eval = FALSE) ################################################### ## confint(contrast(days.lsm, "trt.vs.ctrlk")) ################################################### ### code chunk number 19: using-lsmeans.rnw:292-293 ################################################### pairs(org.lsm) ################################################### ### code chunk number 20: using-lsmeans.rnw:296-297 ################################################### cld(days.lsm, alpha = .10) ################################################### ### code chunk number 21: days-cmp ################################################### plot(days.lsm, comparisons = TRUE, alpha = .10) ################################################### ### code chunk number 22: using-lsmeans.rnw:324-325 ################################################### rbind(pairs(org.lsm)[1:3], pairs(org.lsm, by = "day")[1]) ################################################### ### code chunk number 23: using-lsmeans.rnw:330-331 ################################################### rbind(pairs(lsmeans(org.lsm, "day")), pairs(lsmeans(org.lsm, "price2"))) ################################################### ### code chunk number 24: using-lsmeans.rnw:337-340 ################################################### oranges.mlm <- lm(cbind(sales1,sales2) ~ price1 + price2 + day + store, data = oranges) ref.grid(oranges.mlm) ################################################### ### code chunk number 25: using-lsmeans.rnw:343-345 ################################################### org.mlsm <- lsmeans(oranges.mlm, ~ day | variety, mult.name = "variety") cld(org.mlsm, sort = FALSE) ################################################### ### code chunk number 26: using-lsmeans.rnw:353-354 ################################################### org.vardiff <- update(pairs(org.mlsm, by = "day"), by = NULL) ################################################### ### code chunk number 27: using-lsmeans.rnw:357-358 ################################################### cld(org.vardiff) ################################################### ### code chunk number 28: using-lsmeans.rnw:364-365 ################################################### contrast(org.mlsm, interaction = c("poly", "pairwise")) ################################################### ### code chunk number 29: using-lsmeans.rnw:373-375 ################################################### # Ensure we see the same results each time set.seed(123454321) ################################################### ### code chunk number 30: using-lsmeans.rnw:377-380 ################################################### library("multcomp") days.glht <- as.glht(days_contr.lsm) summary(days.glht, test = adjusted("Westfall")) ################################################### ### code chunk number 31: using-lsmeans.rnw:383-385 (eval = FALSE) ################################################### ## days.glht1 <- glht(oranges.lm1, ## lsm("day", contr = "trt.vs.ctrl", ref = c(5,6))) ################################################### ### code chunk number 32: using-lsmeans.rnw:389-391 (eval = FALSE) ################################################### ## summary(days_contr.lsm, adjust = "mvt") ## summary(days.glht) ################################################### ### code chunk number 33: using-lsmeans.rnw:397-398 (eval = FALSE) ################################################### ## summary(as.glht(pairs(org.lsm))) ################################################### ### code chunk number 34: using-lsmeans.rnw:401-402 (eval = FALSE) ################################################### ## summary(as.glht(pairs(org.lsm), by = NULL)) ################################################### ### code chunk number 35: using-lsmeans.rnw:405-406 (eval = FALSE) ################################################### ## summary(as.glht(pairs(org.lsm, by = NULL))) ################################################### ### code chunk number 36: using-lsmeans.rnw:418-423 ################################################### data("Oats", package = "nlme") library("lme4") Oats.lmer <- lmer(log(yield) ~ Variety*factor(nitro) + (1|Block/Variety), data = Oats) anova(Oats.lmer) ################################################### ### code chunk number 37: oatcontr (eval = FALSE) ################################################### ## contrast(lsmeans(Oats.lmer, "nitro"), "poly") ################################################### ### code chunk number 38: using-lsmeans.rnw:430-431 ################################################### cat("NOTE: Results may be misleading due to involvement in interactions") ################################################### ### code chunk number 39: using-lsmeans.rnw:433-434 ################################################### contrast(lsmeans(Oats.lmer, "nitro"), "poly") ################################################### ### code chunk number 40: using-lsmeans.rnw:438-440 ################################################### Oats.lmer2 <- lmer(log(yield) ~ Variety + poly(nitro,2) + (1|Block/Variety), data = Oats) ################################################### ### code chunk number 41: using-lsmeans.rnw:444-445 ################################################### Oats.lsm2 <- lsmeans(Oats.lmer2, ~ nitro | Variety, cov.reduce = FALSE) ################################################### ### code chunk number 42: using-lsmeans.rnw:457-462 ################################################### library("xtable") xtbl <- xtable(Oats.lsm2, caption = "Example using \\texttt{xtable}", label = "xtable:example") print(xtbl, table.placement = "t") cat("See Table~\\ref{xtable:example}.\n") ################################################### ### code chunk number 43: oatslmer ################################################### lsmip(Oats.lmer, Variety ~ nitro, ylab = "Observed log(yield)") ################################################### ### code chunk number 44: oatslmer2 ################################################### lsmip(Oats.lsm2, Variety ~ nitro, ylab = "Predicted log(yield)") ################################################### ### code chunk number 45: using-lsmeans.rnw:498-499 ################################################### str(Oats.lsm2) ################################################### ### code chunk number 46: using-lsmeans.rnw:502-503 ################################################### summary(Oats.lsm2, type = "response") ################################################### ### code chunk number 47: using-lsmeans.rnw:514-518 ################################################### Oats.log1 <- lmer(log(yield + 5) ~ Variety + factor(nitro) + (1|Block/Variety), data = Oats) ( Oats.rg1 <- update(ref.grid(Oats.log1), tran = make.tran("genlog", 5)) ) ################################################### ### code chunk number 48: using-lsmeans.rnw:523-524 ################################################### round(predict(Oats.rg1, type = "response"), 1) ################################################### ### code chunk number 49: using-lsmeans.rnw:529-531 ################################################### my.tran <- make.tran("boxcox", c(.567, 10)) my.tran$linkfun(10:15) ################################################### ### code chunk number 50: using-lsmeans.rnw:539-543 ################################################### Oats.bc <- with(my.tran, lmer(linkfun(yield) ~ Variety + factor(nitro) + (1|Block/Variety), data = Oats)) ( rg.bc <- ref.grid(Oats.bc) ) round(predict(rg.bc, type = "response"), 1) ################################################### ### code chunk number 51: using-lsmeans.rnw:548-549 ################################################### rg.bc.regrid <- regrid(rg.bc) ################################################### ### code chunk number 52: using-lsmeans.rnw:552-553 ################################################### round(rg.bc.regrid@bhat, 1) ################################################### ### code chunk number 53: using-lsmeans.rnw:558-560 ################################################### summary(lsmeans(rg.bc, "Variety"), type = "response") lsmeans(rg.bc.regrid, "Variety") ################################################### ### code chunk number 54: using-lsmeans.rnw:579-583 ################################################### rg.log <- regrid(rg.bc, "log") lsm.log <- lsmeans(rg.log, "Variety") summary(lsm.log, type = "response") summary(pairs(lsm.log), type = "response") ################################################### ### code chunk number 55: using-lsmeans.rnw:598-600 ################################################### Oats.Vlsm = lsmeans(Oats.lmer2, "Variety") test(Oats.Vlsm, null = log(100), type = "response") ################################################### ### code chunk number 56: using-lsmeans.rnw:612-613 ################################################### test(Oats.Vlsm, null = log(100), delta = 0.20, type = "r") ################################################### ### code chunk number 57: using-lsmeans.rnw:620-621 ################################################### test(contrast(Oats.Vlsm, "trt.vs.ctrlk"), side = ">") ################################################### ### code chunk number 58: using-lsmeans.rnw:625-626 ################################################### test(contrast(Oats.Vlsm, "trt.vs.ctrlk"), side = "nonsup", delta = .25) ################################################### ### code chunk number 59: chick-plot ################################################### require("lattice") xyplot(weight ~ Time | Diet, groups = ~ Chick, data = ChickWeight, type = "o", layout=c(4, 1)) ################################################### ### code chunk number 60: using-lsmeans.rnw:648-650 ################################################### Chick.lmer <- lmer(sqrt(weight) ~ Diet * Time + (0 + Time | Chick), data = ChickWeight) ################################################### ### code chunk number 61: using-lsmeans.rnw:653-654 ################################################### Chick.lst <- lstrends (Chick.lmer, ~ Diet, var = "Time") ################################################### ### code chunk number 62: using-lsmeans.rnw:657-658 ################################################### cld (Chick.lst) ################################################### ### code chunk number 63: using-lsmeans.rnw:663-665 ################################################### lstrends(Chick.lmer, ~ Diet | Time, var = "Time", transform = "response", at = list(Time = c(5, 15))) ################################################### ### code chunk number 64: using-lsmeans.rnw:677-680 ################################################### lsm.options(ref.grid = list(level = .90), lsmeans = list(), contrast = list(infer = c(TRUE, TRUE))) ################################################### ### code chunk number 65: using-lsmeans.rnw:685-686 ################################################### get.lsm.option("estble.tol") ################################################### ### code chunk number 66: using-lsmeans.rnw:703-704 ################################################### lsmeans(Oats.lmer2, pairwise ~ Variety) ################################################### ### code chunk number 67: using-lsmeans.rnw:708-709 ################################################### lsm.options(ref.grid = NULL, contrast = NULL) ################################################### ### code chunk number 68: using-lsmeans.rnw:719-722 ################################################### nutr.lm <- lm(gain ~ (age + group + race)^2, data = nutrition) library("car") Anova(nutr.lm) ################################################### ### code chunk number 69: nutr-intplot ################################################### lsmip(nutr.lm, race ~ age | group) lsmeans(nutr.lm, ~ group*race) ################################################### ### code chunk number 70: using-lsmeans.rnw:739-741 ################################################### nutr.lsm <- lsmeans(nutr.lm, ~ group * race, weights = "proportional", at = list(age = c("2","3"), race = c("Black","White"))) ################################################### ### code chunk number 71: using-lsmeans.rnw:744-747 ################################################### nutr.lsm summary(pairs(nutr.lsm, by = "race"), by = NULL) summary(pairs(nutr.lsm, by = "group"), by = NULL) ################################################### ### code chunk number 72: using-lsmeans.rnw:760-764 ################################################### lsmeans(nutr.lm, "race", weights = "equal") lsmeans(nutr.lm, "race", weights = "prop") lsmeans(nutr.lm, "race", weights = "outer") lsmeans(nutr.lm, "race", weights = "cells") ################################################### ### code chunk number 73: using-lsmeans.rnw:773-775 ################################################### temp = lsmeans(nutr.lm, c("group","race"), weights = "prop") lsmeans(temp, "race", weights = "prop") ################################################### ### code chunk number 74: using-lsmeans.rnw:780-781 ################################################### with(nutrition, tapply(gain, race, mean)) ################################################### ### code chunk number 75: using-lsmeans.rnw:789-793 ################################################### library("mediation") levels(framing$educ) = c("NA","Ref","< HS", "HS", "> HS","Coll +") framing.glm = glm(cong_mesg ~ age + income + educ + emo + gender * factor(treat), family = binomial, data = framing) ################################################### ### code chunk number 76: framinga ################################################### lsmip(framing.glm, treat ~ educ | gender, type = "response") ################################################### ### code chunk number 77: framingb ################################################### lsmip(framing.glm, treat ~ educ | gender, type = "response", cov.reduce = emo ~ treat*gender + age + educ + income) ################################################### ### code chunk number 78: using-lsmeans.rnw:821-823 ################################################### ref.grid(framing.glm, cov.reduce = emo ~ treat*gender + age + educ + income)@grid ################################################### ### code chunk number 79: using-lsmeans.rnw:854-856 (eval = FALSE) ################################################### ## rg <- ref.grid(my.model, at = list(x1 = c(5,10,15)), ## cov.reduce = list(x2 ~ x1, x3 ~ x1 + x2)) ################################################### ### code chunk number 80: housing-plot ################################################### library("ordinal") data(housing, package = "MASS") housing.clm <- clm(Sat ~ (Infl + Type + Cont)^2, data = housing, weights = Freq, link = "probit") lsmip(housing.clm, Cont ~ Infl | Type, layout = c(4,1)) ################################################### ### code chunk number 81: using-lsmeans.rnw:908-909 ################################################### test(pairs(lsmeans(housing.clm, ~ Infl | Type)), joint = TRUE) ################################################### ### code chunk number 82: using-lsmeans.rnw:912-913 ################################################### test(pairs(lsmeans(housing.clm, ~ Cont | Type)), joint = TRUE) ################################################### ### code chunk number 83: using-lsmeans.rnw:918-919 ################################################### ref.grid(housing.clm, mode = "cum.prob") ################################################### ### code chunk number 84: using-lsmeans.rnw:922-924 ################################################### lsmeans(housing.clm, ~ Infl, at = list(cut = "Medium|High"), mode = "cum.prob") ################################################### ### code chunk number 85: using-lsmeans.rnw:927-929 ################################################### summary(lsmeans(housing.clm, ~ Infl, at = list(cut = "Medium|High"), mode = "linear.predictor"), type = "response") ################################################### ### code chunk number 86: using-lsmeans.rnw:937-945 ################################################### require("nlme") options(contrasts = c("contr.treatment", "contr.poly")) Chick.nlme = nlme(weight ~ SSlogis(Time, asym, xmid, scal), data = ChickWeight, fixed = list(asym + xmid ~ Diet, scal ~ 1), random = asym ~ 1 | Chick, start = c(200, 100, 200, 100, 10, 0, 0, 0, 7)) Chick.nlme ################################################### ### code chunk number 87: using-lsmeans.rnw:948-950 ################################################### cld(lsmeans(Chick.nlme, ~ Diet, param = "asym")) cld(lsmeans(Chick.nlme, ~ Diet, param = "xmid")) ################################################### ### code chunk number 88: using-lsmeans.rnw:962-967 ################################################### library("MCMCpack") counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12) outcome <- gl(3, 1, 9) treatment <- gl(3, 3) posterior <- MCMCpoisson(counts ~ outcome + treatment, mcmc = 1000) ################################################### ### code chunk number 89: using-lsmeans.rnw:970-971 ################################################### ( post.lsm <- lsmeans(posterior, "treatment") ) ################################################### ### code chunk number 90: using-lsmeans.rnw:974-976 ################################################### library("coda") summary(as.mcmc(post.lsm)) lsmeans/inst/doc/using-lsmeans.rnw0000644000175100001440000022551713013732364017007 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % % % \documentclass[11pt]{article} \usepackage[margin=1in]{geometry} \usepackage{mathpazo} \usepackage{hyperref} \usepackage{fancyvrb} \usepackage{multicol} \usepackage{natbib} \usepackage{Sweave} \usepackage{makeidx} \makeindex \hypersetup{colorlinks=true,allcolors=black,urlcolor=blue} \let\dq=" \DefineShortVerb{\"} \def\pkg{\textbf} \def\proglang{\textsf} \def\lsm{\pkg{lsmeans}} % double-quoted text \def\dqt#1{\code{\dq{}#1\dq{}}} % The objects I want to talk about \def\rg{\dqt{ref.grid}} \def\lsmo{\dqt{lsmobj}} % for use in place of \item in a description env where packages are listed \def\pitem#1{\item[\pkg{#1}]} \def\R{\proglang{R}} \def\SAS{\proglang{SAS}} \def\code{\texttt} \def\Fig#1{Figure~\ref{#1}} \def\bottomfraction{.5} % For indexing... % Naming: \[w]ix[fmt]{#1} -- always use lower-case for alphabetization. % fmt defines format, use w prefix for in-text refs (word is included) \def\ix#1{\index{#1@\MakeUppercase#1}} \def\wix#1{#1\ix{#1}} \def\ixcode#1{\index{#1@\texttt{#1}}} \def\wixcode#1{\texttt{#1}\ixcode{#1}} \def\ixpkg#1{\index{#1@\textbf{#1} package}} \def\wixpkg#1{\textbf{#1}\ixpkg{#1}} % Add subheadings... \def\ixsub#1#2{\index{#1@\MakeUppercase#1!#2}} \def\wixsub#1#2{#1\ixsub{#1}{#2}} \def\ixcodesub#1#2{\index{#1@\texttt{#1}!#2}} \def\wixcodesub#1#2{\texttt{#1}\ixcodesub{#1}{#2}} %\VignetteIndexEntry{Using lsmeans} %\VignetteDepends{lsmeans} %\VignetteKeywords{least-squares means} %\VignettePackage{lsmeans} % Initialization <>= options(show.signif.stars=FALSE, prompt="R> ", continue=" ", useFancyQuotes=FALSE, width=100, digits=6) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% declarations for jss.cls %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% almost as usual \author{Russell V.~Lenth\\The University of Iowa} \title{Using \lsm{}} %% without formatting \ifx %xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx %% for pretty printing and a nice hypersummary also set: \Plainauthor{Russell V.~Lenth} %% comma-separated \Plaintitle{Least-squares Means: The R Package lsmeans} %% without formatting \Shorttitle{The R Package lsmeans} %% a short title (if necessary) \fi %xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx \ifx % IGNORE xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx %% The address of (at least) one author should be given %% in the following format: \Address{ Russell V.~Lenth, Professor Emeritus\\ Department of Statistics and Actuarial Science\\ % 241 Schaeffer Hall\\ The University of Iowa\\ Iowa City, IA 52242 ~ USA\\ E-mail: \email{russell-lenth@uiowa.edu} %\\ % URL: \url{http://www.stat.uiowa.edu/~rlenth/} } %% It is also possible to add a telephone and fax number %% before the e-mail in the following format: %% Telephone: +43/1/31336-5053 %% Fax: +43/1/31336-734 \fi %xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx %% for those who use Sweave please include the following line (with % symbols): %% need no \usepackage{Sweave.sty} %% end of declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{document} \SweaveOpts{concordance=TRUE} \maketitle{} \begin{abstract} Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects of factors, and for testing linear contrasts among predictions. The \lsm{} package provides a simple way of obtaining least-squares means and contrasts thereof. It supports many models fitted by \R{} core packages (as well as a few key contributed ones) that fit linear or mixed models, and provides a simple way of extending it to cover more model classes. \end{abstract} \section{Introduction} \wix{least-squares means} (\wix{LS~means} for short) for a linear model are simply predictions---or averages thereof---over a regular grid of predictor settings which I call the \emph{\wix{reference grid}}. They date back at least to \cite{Har60} and his associated computer program \proglang{LSML} \citep{Har77} and the contributed \SAS{} procedure named \pkg{HARVEY}\index{SAS!PROC HARVEY} \citep{Har76}. Later, they were incorporated via \code{LSMEANS}\index{SAS!LSMEANS} statements for various linear model procedures such as \pkg{GLM} in the regular \SAS{} releases. See also \cite{Goo97} and \cite{SAS12} for more information about the \SAS{} implementation. In simple \wix{analysis-of-covariance models}, LS~means are the same as covariate-\wix{adjusted means}. In unbalanced factorial experiments, LS~means for each factor mimic the main-effects means but are adjusted for imbalance. The latter interpretation is quite similar to the ``\wix{unweighted means}'' method for unbalanced data, as presented in old design books. LS~means are not always well understood, in part because the term itself is confusing. \cite{Sea80} discusses exactly how they are defined for various factorial, nested, and covariance models. \citeauthor{Sea80} suggest the term ``\wix{predicted marginal means}'' (or \wix{PMMs}) as a better descriptor. However, the term ``least-squares means'' was already well established in the \SAS{} software, and it has stuck. The most important things to remember are:\ixsub{least-squares means}{defined} \begin{itemize} \item LS~means are computed relative to a \emph{reference grid}. \item Once the reference grid is established, LS~means are simply predictions on this grid, or marginal averages of a table of these predictions. \end{itemize} A user who understands these points will know what is being computed, and thus can judge whether or not LS~means are appropriate for the analysis. \section{The reference grid} Since the reference grid\ixsub{reference grid}{defined} is fundamental, it is our starting point. For each predictor in the model, we define a set of one or more \emph{reference levels}. The reference grid is then the set of all combinations of reference levels. If not specified explicitly, the default reference levels are obtained as follows: \begin{itemize} \item For each predictor that is a factor, its reference levels are the unique levels of that factor. \item Each numeric predictor has just one reference level---its mean over the dataset. \end{itemize} So the reference grid depends on both the model and the dataset. \subsection{Example: Orange sales}\index{Examples!orange sales} To illustrate, consider the \wixcode{oranges} data provided with \lsm{}. This dataset has sales of two varieties of oranges (response variables "sales1" and "sales2") at 6 stores (factor "store"), over a period of 6 days (factor "day"). The prices of the oranges (covariates "price1" and "price2") fluctuate in the different stores and the different days. There is just one observation on each store on each day. For starters, let's consider an additive covariance model for sales of the first variety, with the two factors and both "price1" and "price2" as covariates (since the price of the other variety could also affect sales). <<>>= library("lsmeans") oranges.lm1 <- lm(sales1 ~ price1 + price2 + day + store, data = oranges) anova(oranges.lm1) @ The \wixcode{ref.grid} function in \lsm{} may be used to establish the reference grid. Here is the default one: <<>>= ( oranges.rg1 <- ref.grid(oranges.lm1) ) @ As outlined above, the two covariates "price1" and "price2" have their means as their sole reference level; and the two factors have their levels as reference levels. The reference grid thus consists of the $1\times1\times6\times6=36$ combinations of these reference levels. LS~means are based on predictions on this reference grid, which we can obtain using "predict" or "summary": <<>>= summary(oranges.rg1) @ \subsection{LS means as \wix{marginal averages} over the reference grid} The ANOVA indicates there is a significant "day" effect after adjusting for the covariates, so we might want to do a follow-up analysis that involves comparing the days. The \wixcode{lsmeans} function provides a starting point: <<>>= lsmeans(oranges.rg1, "day") ## or lsmeans(oranges.lm1, "day") @ These results, as indicated in the annotation in the output, are in fact the averages of the predictions shown earlier, for each day, over the 6 stores. The above LS~means (often called ``\wix{adjusted means}'') are not the same as the overall means for each day: <<>>= with(oranges, tapply(sales1, day, mean)) @ These \wix{unadjusted means} are not comparable with one another because they are affected by the differing "price1" and "price2" values on each day, whereas the LS~means are comparable because they use predictions at uniform "price1" and "price2" values. Note that one may call "lsmeans" with either the reference grid or the model. If the model is given, then the first thing it does is create the reference grid; so if the reference grid is already available, as in this example, it's more efficient to make use of it. For users who dislike the term ``LS~means,'' there is also a \wixcode{pmmeans} function (for \wix{predicted marginal means}) which is an alias for "lsmeans" but relabels the "lsmean" column in the summary. \subsection{Altering the reference grid}\ixsub{reference grid}{altering} The wixcode{at} argument may be used to override defaults in the reference grid. The user may specify this argument either in a "ref.grid" call or an "lsmeans" call; and should specify a "list" with named sets of reference levels. Here is a silly example: <<>>= lsmeans(oranges.lm1, "day", at = list(price1 = 50, price2 = c(40,60), day = c("2","3","4")) ) @ Here, we restricted the results to three of the days, and used different prices. One possible surprise is that the predictions are averaged over the two "price2" values. That is because "price2" is no longer a single reference level, and we average over the levels of all factors not used to split-out the LS~means. This is probably not what we want.\footnote{% The \emph{default} reference grid produces LS~means exactly as described in \cite{Sea80}. However, an altered reference grid containing more than one value of a covariate, such as in this example, departs from (or generalizes, if you please) their definition by averaging with equal weights over those \wix{covariate levels}. It is not a good idea here, but there is an example later in this vignette where it makes sense.} To get separate sets of predictions for each "price2", one must specify it as another factor or as a \wixcode{by} factor in the "lsmeans" call (we will save the result for later discussion): <<>>= org.lsm <- lsmeans(oranges.lm1, "day", by = "price2", at = list(price1 = 50, price2 = c(40,60), day = c("2","3","4")) ) org.lsm @ Note: We could have obtained the same results using any of these: <>= lsmeans(oranges.lm1, ~ day | price, at = ... ) # Ex 1 lsmeans(oranges.lm1, c("day","price2"), at = ... ) # Ex 2 lsmeans(oranges.lm1, ~ day * price, at = ... ) # Ex 3 @ Ex~1 illustrates the formula method for \wix{specifying factors}\ixsub{factors}{specifying}, which is more compact. The "|" character replaces the "by" specification. Ex~2 and Ex~3 produce the same results, but their results are displayed as one table (with columns for "day" and "price") rather than as two separate tables.\ixsub{formula specs}{one-sided} \section{Working with the results}\index{ref.grid@\dqt{ref.grid} class} \subsection{Objects} The "ref.grid" function produces an object of class \rg{}, and the "lsmeans" function produces an object of class \lsmo{},\index{lsmobj@\dqt{lsmobj} class} which is a subclass of \rg. There is really no practical difference between these two classes except for their "show" methods---what is displayed by default---and the fact that an \lsmo{} is not (necessarily) a true reference grid as defined earlier in this article. Let's use the \wixcode{str} function to examine the \lsmo{} object just produced: <<>>= str(org.lsm) @ We no longer see the reference levels for all predictors in the model---only the levels of "day" and "price2". These \emph{act} like reference levels, but they do not define the reference grid upon which the predictions are based. \subsection{Summaries} There are several methods for \rg{} (and hence also for \lsmo{}) objects. One already seen is \wixcode{summary}. It has a number of arguments---see its help page. In the following call, we summarize "days.lsm" differently than before. We will also save the object produced by "summary" for further discussion. <<>>= ( org.sum <- summary(org.lsm, infer = c(TRUE,TRUE), level = .90, adjust = "bon", by = "day") ) @ The \wixcode{infer} argument causes both \wix{confidence intervals} and \wix{tests} to be produced; the default confidence level of $.95$ was overridden; a Bonferroni adjustment\ixcode{adjust}\ixcode{level}\ix{multiplicity adjustment}\ixsub{multiplicity adjustment}{Bonferroni} was applied to both the intervals and the $P$~values; and the tables are organized the opposite way from what we saw before. What kind of object was produced by "summary"? Let's see: <<>>= class(org.sum) @ The \dqt{\wixcode{summary.ref.grid}} class is an extension of \dqt{data.frame}. It includes some attributes that, among other things, cause additional messages to appear when the object is displayed. But it can also be used as a \dqt{data.frame} if the user just wants to use the results computationally. For example, suppose we want to convert the LS~means from dollars to Russian rubles (at the July 13, 2014 exchange rate): {\small <<>>= transform(org.sum, lsrubles = lsmean * 34.2) @ } Observe also that the summary is just one data frame with six rows, rather than a collection of three data frames; and it contains a column for all reference variables, including any "by" variables. Besides "str" and "summary", there is also a \wixcode{confint} method, which is the same as "summary" with "infer=c(TRUE,FALSE)", and a \wixcode{test} method (same as "summary" with "infer=c(FALSE,TRUE)", by default). The "test" method may in addition be called with "joint=TRUE"\ixsub{tests}{joint} to obtain a joint test that all or some of the linear functions are equal to zero or some other value. There is also an \wixcode{update} method which may be used for changing the object's display settings. For example: <<>>= org.lsm2 <- update(org.lsm, by.vars = NULL, level = .99) org.lsm2 @ \subsection{Plots}\ix{plots}\ix{graphical displays} Confidence intervals for LS~means may be displayed graphically, using the \wixcode{plot} method. For example: <>= plot(org.lsm, by = "price2") @ The resulting display is shown in \Fig{org-plot}. This function requires that the \wixpkg{lattice} package be installed. \begin{figure} \begin{center} \includegraphics{using-lsmeans-org-plot.pdf} \end{center} \caption{Confidence intervals for LS~means in the \code{oranges} example.}\label{org-plot} \end{figure} Additional graphical presentations are covered later in this vignette. \section{Contrasts and comparisons} \subsection{Contrasts in general}\ix{contrasts} Often, people want to do pairwise comparisons of LS~means, or compute other contrasts among them. This is the purpose of the \wixcode{contrast} function, which uses a \dqt{ref.grid} or \dqt{lsmobj} object as input. There are several standard contrast families such as \dqt{pairwise}, \dqt{trt.vs.ctrl}, and \dqt{\wixcode{poly}}.\ixsub{contrasts}{polynomial}\ixsub{contrasts}{effects (offsets from mean)} In the following command, we request \dqt{\wixcode{eff}} contrasts, which are differences between each mean and the overall mean: <<>>= contrast(org.lsm, method = "eff") @ Note that this preserves the "by" specification from before, and obtains the effects for each group. In this example, since it is an \wix{additive model}, we obtain exactly the same results in each group. This isn't wrong, it's just redundant.\index{Redundant results} Another popular method is Dunnett-style \wixsub{contrasts}{Dunnett}, where a particular LS~mean is compared with each of the others. This is done using \dqt{\wixcode{trt.vs.ctrl}}. In the following, we obtain (again) the LS~means for days, and compare each with the average of the LS~means on day~5 and~6. <<>>= days.lsm <- lsmeans(oranges.rg1, "day") ( days_contr.lsm <- contrast(days.lsm, "trt.vs.ctrl", ref = c(5,6)) ) @ For convenience, \dqt{\wixcode{trt.vs.ctrl1}} and \dqt{\wixcode{trt.vs.ctrlk}} methods are provided for use in lieu of "ref" for comparing with the first and the last LS~means. The \dqt{\wixcode{dunnettx}} adjustment is a good approximation to the exact Dunnett $P$~value adjustment. If the exact adjustment is desired, use \wixcode{adjust}" = "\dqt{\wixcode{mvt}}; but this can take a lot of computing time when there are several tests. Note that by default, "lsmeans" results are displayed with confidence intervals while "contrast" results are displayed with $t$ tests. One can easily override this; for example, <>= confint(contrast(days.lsm, "trt.vs.ctrlk")) @ (Results not shown.) In the above examples, a default \wixsub{multiplicity adjustment}{default} is determined from the contrast method. This may be overridden by adding an \wixcode{adjust} argument. \subsection{Pairwise comparisons}\ixsub{contrasts}{pairwise comparisons} Often, users want \wix{pairwise comparisons} among the LS~means. These may be obtained by specifying \dqt{\wixcode{pairwise}} or \dqt{\wixcode{revpairwise}} as the "method" argument in the call to \wixcodesub{contrast}{method@\code{method}}. For group labels $A,B,C$, \dqt{pairwise} generates the comparisons $A-B, A-C, B-C$ while \dqt{revpairwise} generates $B-A, C-A, C-B$. As a convenience, a \wixcode{pairs} method is provided that calls "contrast" with "method="\dqt{pairwise}:\ixsub{pairwise comparisons}{using \code{pairs}} <<>>= pairs(org.lsm) @ There is also a \wixcode{cld} (\wix{compact letter display}) method that lists the LS~means along with grouping symbols for pairwise contrasts. It requires the \wixpkg{multcompView} package \citep{mcview} to be installed. <<>>= cld(days.lsm, alpha = .10) @ Two LS~means that share one or more of the same grouping symbols are not significantly different at the stated value of "alpha", after applying the multiplicity adjustment (in this case Tukey's HSD). By default, the LS~means are ordered in this display, but this may be overridden with the argument "sort=FALSE". "cld" returns a \dqt{summary.ref.grid} object, not an "lsmobj". Another way to display pairwise comparisons is via the "comparisons" argument of \wixcode{plot}. \ixcodesub{plot}{\code{comparisons}}\ixsub{pairwise comparisons}{graphical} When this is set to "TRUE", arrows are added to the plot, with lengths set so that the amount by which they overlap (or don't overlap) matches as closely as possible to the amounts by which corresponding confidence intervals for differences cover (or don't cover) the value zero. \ix{comparison arrows} This does not always work, and if there are discrepancies, a message is printed. But it usually works as long as the standard errors of differences are not too discrepant. <>= plot(days.lsm, comparisons = TRUE, alpha = .10) @ \Fig{days-cmp} shows the result. Note that the pairs of means having overlapping arrows are the same as those grouped together in the "cld" display. However, these comparison arrows show more about the degree of significance in the comparisons. The lowest and highest LS~mean have arrows pointing only inward, as the others are not needed. If the confidence intervals and arrows together look too cluttered, one can add the argument \code{intervals = FALSE}, then only the arrows will be displayed.\ixcodesub{plot}{intervals@\code{intervals}} \begin{figure} \begin{center} \includegraphics{using-lsmeans-days-cmp.pdf} \end{center} \caption{Graphical comparisons of the LS~means for \code{days}.}\label{days-cmp} \end{figure} \subsection{Multiplicity adjustments---changing the family}\ixsub{multiplicity adjustment}{combining/subsetting families} You may have noticed that in the preceding examples where $P$-value adjustments were implemented, those adjustments were made \emph{separately} for each sub-table when a "by" variable is active. Some users prefer that all the adjusted tests together as one family---or even combine more than one family of tests into one family for purposes of adjustment. This may be done using the \wixcode{rbind} method (similar to using "rbind" to combine matrices. On the flip side, perhaps we want to exclude some tests. This may be used using the \ixcode{[]} operator: simply specify the row indexes of the tests to include. To illustrate, consider the previously obtained "org.lsm" object. In "pairs(org.lsm)", we obtain the same results twice (as seen above) because the model is additive. For the same reason, if we change the "by" variable to \dqt{day}, we'll obtain three copies of the same comparison of the two "price2"s. If we want to consider the three "day" comparisons and the one "price2" comparison together as one family of four tests, we can do: <<>>= rbind(pairs(org.lsm)[1:3], pairs(org.lsm, by = "day")[1]) @ Note that by default, the \dqt{mvt} adjustment level is used; for complicated families like this, ordinary Tukey and Dunnett adjustments are usually not appropriate. We arrived at this point by a circuitous path. In the additive model, the above conditional results are the same as the marginal ones: <<>>= rbind(pairs(lsmeans(org.lsm, "day")), pairs(lsmeans(org.lsm, "price2"))) @ \section{Multivariate models} The "oranges" data has two response variables. Let's try a \wix{multivariate model} for predicting the sales of the two varieties of oranges, and see what we get if we call "ref.grid": <<>>= oranges.mlm <- lm(cbind(sales1,sales2) ~ price1 + price2 + day + store, data = oranges) ref.grid(oranges.mlm) @ What happens is that the multivariate response is treated like an additional factor, by default named \wixcode{rep.meas}. In turn, it can be used to specify levels for LS~means. Here we rename the multivariate response to \dqt{variety} and obtain "day" means (and a compact letter display for comparisons thereof) for each "variety":\ixcode{mult.name} <<>>= org.mlsm <- lsmeans(oranges.mlm, ~ day | variety, mult.name = "variety") cld(org.mlsm, sort = FALSE) @ \section{Contrasts of contrasts (interaction contrasts)} With the preceding model, we might want to compare the two varieties on each day: <<>>= org.vardiff <- update(pairs(org.mlsm, by = "day"), by = NULL) @ The results (not yet shown) will comprise the six "sales1-sales2" differences, one for each day. The two "by" specifications seems odd, but the one in "pairs" specifies doing a separate comparison for each day, and the one in "update" asks that we convert it to one table with six rows, rather than 6 tables with one row each. Now, let's compare these differences to see if they vary from day to day. <<>>= cld(org.vardiff) @ There is little evidence of variety differences, nor that these differences vary from day to day. A newer feature of the \wixcodesub{contrast}{\code{interaction}} function is the optional "interaction" argument, which may be used to specify \wix{interaction contrasts}\ixsub{contrasts}{interaction}\ixsub{contrasts}{of contrasts} by naming which contrast to use for each variable (in the order of appearance in the grid). In a similar example to the above, suppose we want to compare each polynomial contrast in "day" between the two varieties:\ixsub{contrasts}{polynomial} <<>>= contrast(org.mlsm, interaction = c("poly", "pairwise")) @ \section[Interfacing with multcomp]{Interfacing with \pkg{multcomp}} The \wixpkg{multcomp} package \citep{multc} supports more options for simultaneous inference than are available in \lsm{}. Its \wixcode{glht} (general linear hypothesis testing) function and associated \dqt{glht} class are similar in some ways to "lsmeans" and \dqt{lsmobj} objects, respectively. So \lsm{} provides an \wixcode{as.glht} function to do the conversion. To illustrate, let us convert the "days_contr.lsm" object (produced earlier) to a "glht" object, and use it to obtain adjusted $P$~values under \wix{Westfall's adjustment procedure} (not available in \lsm{}): <>= # Ensure we see the same results each time set.seed(123454321) @ <<>>= library("multcomp") days.glht <- as.glht(days_contr.lsm) summary(days.glht, test = adjusted("Westfall")) @ In addition, \lsm{} provides an \wixcode{lsm} function (or its alias, \wixcode{pmm}) that may be called from within a call to "glht". Thus, another way to obtain the same "glht" object is to use <>= days.glht1 <- glht(oranges.lm1, lsm("day", contr = "trt.vs.ctrl", ref = c(5,6))) @ By the way, the following two statements will produce the same results: <>= summary(days_contr.lsm, adjust = "mvt") summary(days.glht) @ That is, the \dqt{mvt} adjust method in \lsm{} is the same as the default single-step $P$~value adjustment in \pkg{multcomp}.\ixsub{multiplicity adjustment}{single-step (\code{mvt})} One additional detail: If there is a "by" variable in effect, "glht" or "as.glht" returns a "list" of "glht" objects---one for each "by" level. There are courtesy "coef", "confint", "plot", "summary", and "vcov" methods for this \dqt{\wixcode{glht.list}} class to make things a bit more user-friendly. Recall the earlier example result "org.lsm", which contains information for LS~means for three "day"s at each of two values of "price2". Suppose we are interested in pairwise comparisons of these LS~means, by "price2". If we call <>= summary(as.glht(pairs(org.lsm))) @ (results not displayed) we will obtain two "glht" objects with three contrasts each, so that the results shown will incorporate multiplicity adjustments for each family of three contrasts. If, on the other hand, we want to consider those six contrasts as one family, use <>= summary(as.glht(pairs(org.lsm), by = NULL)) @ \ldots{} and note (look carefully at the parentheses) that this is \emph{not} the same as <>= summary(as.glht(pairs(org.lsm, by = NULL))) @ which removes the "by" grouping \emph{before} the pairwise comparisons are generated, thus yielding ${6 \choose 2}=15$ contrasts instead of just six.\ixsub{multiplicity adjustment}{effect of \code{by} on family} \section{A new example: Oat yields}\index{Examples!oat yields}\index{Examples!split-plot experiment} Orange-sales illustrations are probably getting tiresome. To illustrate some new features, let's turn to a new example. The \wixcode{Oats} dataset in the \wixpkg{nlme} package \citep{nlme} has the results of a split-plot experiment discussed in \citet{Yat35}. The experiment was conducted on six blocks (factor "Block"). Each block was divided into three plots, which were randomized to three varieties (factor "Variety") of oats. Each plot was divided into subplots and randomized to four levels of nitrogen (variable "nitro"). The response, "yield", was measured once on each subplot after a suitable growing period. We will fit a model using the "lmer" function in the \wixpkg{lme4} package \citep{lme4}. This will be a mixed model with random intercepts for "Block" and "Block:Variety" (which identifies the plots). A logarithmic transformation is applied to the response variable (mostly for illustration purposes, though it does produce a good fit to the data). Note that "nitro" is stored as a numeric variable, but we want to consider it as a factor in this initial model. <<>>= data("Oats", package = "nlme") library("lme4") Oats.lmer <- lmer(log(yield) ~ Variety*factor(nitro) + (1|Block/Variety), data = Oats) anova(Oats.lmer) @ Apparently, the interaction is not needed. But perhaps we can further simplify the model by using only a linear or quadratic trend in "nitro". We can find out by looking at polynomial contrasts:\ixsub{contrasts}{polynomial} <>= contrast(lsmeans(Oats.lmer, "nitro"), "poly") @ %%% Fake the warning message <>= cat("NOTE: Results may be misleading due to involvement in interactions") @ <>= <> @ \ixsub{warnings}{when interaction is in model} (A message is issued when we average over predictors that interact with those that delineate the LS~means. In this case, it is not a serious problem because the interaction is weak.) Both the linear and quadratic contrasts are pretty significant. All this suggests fitting an additive model where "nitro" is included as a numeric predictor with a quadratic trend. <<>>= Oats.lmer2 <- lmer(log(yield) ~ Variety + poly(nitro,2) + (1|Block/Variety), data = Oats) @ Remember that "nitro" is now used as a quantitative predictor.\ixsub{factors}{with quantitative levels} But for comparing with the previous model, we want to see predictions at the four unique "nitro" values rather than at the average of "nitro". This may be done using "at" as illustrated earlier, or a shortcut is to specify \wixcode{cov.reduce} as "FALSE", which tells "ref.grid" to use all the unique values of numeric predictors. <<>>= Oats.lsm2 <- lsmeans(Oats.lmer2, ~ nitro | Variety, cov.reduce = FALSE) @ The results are displayed as an export table (see Section~\ref{xtsect}) in Table~\ref{xtable:example}, page~\pageref{xtable:example}. These LS~means follow the same quadratic trend for each variety, but with different intercepts.\footnote{% This is the promised example where our generalization of \cite{Sea80}'s definition of LS~means makes sense. Suppose we want to compare the LS~means for \code{Variety} with those in the original model \code{Oats.lmer} where \code{nitro} was a factor, we want to average equally over the four \code{nitro} levels, even though \code{nitro} is a covariate in this second model.}\ixsub{reference grid}{altered for quantitative factor} Fractional \wixsub{degrees of freedom}{fractional} are displayed in these results. These are obtained by default using the Satterthwaite method, using routines in the \wixpkg{lmerTest} package \citep{lmert}. Adding the argument \code{mode = \dqt{kenward-roger}} to the \code{lsmeans} call will cause the degrees of freedom to be computed using instead the Kenward-Roger (K-R) method from the \wixpkg{pbkrtest} package \citep{pbkrt}, which also implements, as a side-effect, a bias adjustment in the estimated covariances (and hence standard errors). The K-R method is probably preferable, but it requires a lot more computation, and hence is no longer the default. A third option is to specify \code{mode = \dqt{asymptotic}}, for which all the degrees of freedom are set to \code{NA}---producing $z$~tests rather than $t$~tests. You may change the default via \code{lsm.options(lmer.df = \emph{\dqt{desired default}})}. These \code{mode} settings are partially matched, so \code{mode = \dqt{k}} is actually good enough. \section{Additional display methods} \subsection{Export tables}\ix{export tables}\label{xtsect} The \pkg{lsmeans} package provides an \wixcode{xtable} method \citep{xtable} that works with "lsmobj", "ref.grid", and "summary.ref.grid" objects. (It actually uses the \wixcode{xtableList} interface; see the \ixpkg{xtable} documentation for details.) This is quite useful if you want a nicely formatted table, especially using \wixcode{Sweave} or \wixcode{knitr}. To illustrate, we display the "Oats.lsm2" object just created. <>= library("xtable") xtbl <- xtable(Oats.lsm2, caption = "Example using \\texttt{xtable}", label = "xtable:example") print(xtbl, table.placement = "t") cat("See Table~\\ref{xtable:example}.\n") @ \subsection{Displaying LS means graphically}\ixsub{graphical displays}{interaction plot} We have already seen the use of the "plot" function to display confidence intervals and/or comparison arrows. The \lsm{} package also includes a function \wixcode{lsmip} that displays predictions in an interaction-plot-like manner.\ix{interaction plot} It uses a formula of the form \begin{Sinput} curve.factors ~ x.factors | by.factors \end{Sinput} This function also requires the \wixpkg{lattice} package. In the above formula, "curve.factors" specifies factor(s) used to delineate one displayed curve from another (i.e., groups in \pkg{lattice}'s parlance). "x.factors" are those whose levels are plotted on the horizontal axis. And "by.factors", if present, break the plots into panels. To illustrate, consider the first model we fitted to the "Oats" data. Let's do a graphical comparison of the two models we have fitted to the "Oats" data. <>= lsmip(Oats.lmer, Variety ~ nitro, ylab = "Observed log(yield)") @ \vspace{-12pt} <>= lsmip(Oats.lsm2, Variety ~ nitro, ylab = "Predicted log(yield)") @ The plots are shown in \Fig{intplots}. Note that the first model fits the cell means perfectly, so its plot is truly an interaction plot of the data. The other displays the parabolic trends we fitted in the revised model. \begin{figure} \includegraphics[width=3in]{using-lsmeans-oatslmer.pdf} \hfill \includegraphics[width=3in]{using-lsmeans-oatslmer2.pdf} \caption{Interaction plots for the cell means and the fitted model, \code{Oats} example.}\label{intplots} \end{figure} \section{Transformations}\ix{transformations} \subsection{Automatic support for transformations}\ixsub{transformations}{automatically detected} When a transformation or link function is used in fitting a model, "ref.grid" (also called by "lsmeans") stores that information in the returned object, as seen in this example: <<>>= str(Oats.lsm2) @ This allows us to conveniently unravel the transformation, via the \wixcode{type} argument in "summary" or related functions such as "lsmip" and "predict".\ixcodesub{summary}{type@\code{type = }\dqt{response}} Here are the predicted yields for (as opposed to predicted log yields) for the polynomial model: <<>>= summary(Oats.lsm2, type = "response") @ It is important to realize that the statistical inferences are all done \emph{before} reversing the transformation. Thus, $t$ ratios are based on the linear predictors and will differ from those computed using the printed estimates and standard errors. Likewise, \wixsub{confidence intervals}{back-transformed} are computed on the linear-predictor scale, then the endpoints are back-transformed. This kind of automatic support is available for most popular response transformations such as "log", "log10", and even transformations like "asin(sqrt())" and "sqrt(y)+sqrt(y+1)". The Details section for \verb|help("make.tran")| provides a complete list. It is also possible to support custom transformations via the "tran" argument in the "update" method---see its help page. \ixsub{transformations}{custom} \subsection{Using \code{make.tran}}\ixsub{transformations}{using \code{make.tran}}\ixsub{transformations}{requiring parameter(s)}\ixsub{transformations}{Box-Cox} The \wixcode{make.tran} function provides support for yet more popular types of transformations, particularly those that require specifying one or more parameters. Examples are general power transformations, \wix{Box-Cox transformations}, and transformations with a shifted origin such as "log(y + a)". Details may of course be found via \verb|help("make.tran")|. The function returns a "list" of functions, compatible with what is returned by "make.link" in the \pkg{stats} package. The latter is intended primarily for use with generalized linear models, and "make.tran" extends such capabilities to other response transformations. There are two basic ways to use "make.tran": retrospectively on an existing model, and prospectively in fitting a new model. Here is an example of retrospective use, where the $\log(y+5)$ transformation was used. This transformation is not auto-detected. <<>>= Oats.log1 <- lmer(log(yield + 5) ~ Variety + factor(nitro) + (1|Block/Variety), data = Oats) ( Oats.rg1 <- update(ref.grid(Oats.log1), tran = make.tran("genlog", 5)) ) @ Here, we created a reference grid for the model, then updated it with its "tran" component set to the result of "make.tran" for a generalized log transformation with parameter 5. \ixcodesub{update}{tran@\code{tran}} This updated reference grid has all the information needed to back-transform the results to the original "yield" scale: <<>>= round(predict(Oats.rg1, type = "response"), 1) @ \ixcodesub{tran}{using \code{linkfun}}\ixcodesub{make.tran}{as enclosing environment} Using "make.tran" prospectively makes use of the fact that the transformation itself is included in the returned list as a function named "linkfun" (somewhat oddly named due to the fact that "make.tran" mimics the functionality of "make.link"). When a model is fitted with "linkfun" as the transformation, its \wix{enclosing environment} is automatically used to obtain the transformation definitions. For illustration, consider a rather far-fetched transformation: <<>>= my.tran <- make.tran("boxcox", c(.567, 10)) my.tran$linkfun(10:15) @ This specifies a Box-Cox transformation with the origin shifted to $10$:% \footnote{To obtain an ordinary Box-Cox transformation, provide just one parameter: \code{make.tran(\dqt{boxcox}, .567)}.} \[ h(y) = \frac{(y-10)^{.567} - 1}{1 - .567} \] If we use "my.tran" as an enclosing environment for fitting the model, the transformation is saved automatically: <<>>= Oats.bc <- with(my.tran, lmer(linkfun(yield) ~ Variety + factor(nitro) + (1|Block/Variety), data = Oats)) ( rg.bc <- ref.grid(Oats.bc) ) round(predict(rg.bc, type = "response"), 1) @ \subsection{Using \code{regrid}}\ixsub{reference grid}{re-gridding to response scale} The \wixcode{regrid} function may be used to, in essence, give a new beginning to an existing reference grid (or "lsmobj"), most redefined on the response scale (i.e., back-transformed). Consider the preceding Box-Cox example, after applying "regrid": <<>>= rg.bc.regrid <- regrid(rg.bc) @ By default, the estimates are back-transformed to the response scale. In a "regrid" result, the "linfct" slot (linear functions) become the identity matrix, and the "bhat" slot (regression coefficients) become the predictions at each grid point: <<>>= round(rg.bc.regrid@bhat, 1) @ which matches the predictions shown previously. The interesting thing is what happens if we subsequently obtain LS~means. Compare these results: <<>>= summary(lsmeans(rg.bc, "Variety"), type = "response") lsmeans(rg.bc.regrid, "Variety") @ \ixcodesub{regrid}{effect on LS~means} Why are the answers somewhat different? Recall that LS~means are obtained via equally-weighted averages of predictions. In the first "lsmeans" call, the predictions, on the Box-Cox scale, are averaged together and then back-transformed to the response scale; whereas in the second "lsmeans" call, the predictions being averaged were already on the response scale. (Hence, the results are the usual arithmetic means of the predictions on the grid.) Since the Box-Cox transformation is nonlinear, averaging then back-transforming is not the same as back-transforming then averaging.\ixsub{mean}{arithmetic} Even the degrees of freedom (d.f.) differ in the two results, because degrees-of-freedom calculations take place on the linear-predictor scale. Once results are back-transformed, "regrid" ``freezes'' the calculated \wixsub{degrees of freedom}{containment method} for each prediction. Subsequently, a containment method is used whereby the returned d.f.\ is the minimum d.f.\ of predictions involved in each LS~mean. Some users prefer averaging the predictions on the response scale as they are then the arithmentic means; and now you see that the way to make that happen is through the "regrid" function. \subsection{Reverse-engineering a log transformation}\ixsub{reference grid}{re-gridding to log scale} When a response has been log-transformed, then there are useful special properties of back-transformed summaries: \begin{itemize} \item LS~means, when back-transformed to the response scale, are actually the \emph{geometric} means of the response-scale predictions.\ixsub{mean}{geometric} \item A difference of two LS~means on the log scale, after back-transforming, becomes an estimate of the \emph{ratio} of the two geometric means. Such comparisons via ratios can be quite useful for positive responses.\ixsub{pairwise comparisons}{by ratios instead of differences} \end{itemize} The \wixcodesub{regrid}{log@\dqt{log} option} function provides a \dqt{log} option that recomputes the reference grid \emph{as if} the response transformation had been the natural logarithm. We can then take advantage of the above special properties of log-transformed responses. The only proviso is that, on the response scale, all of the reference-grid predictions must be positive. To illustrate, we revisit the above Box-Cox model once again, and regrid it on the log scale: <<>>= rg.log <- regrid(rg.bc, "log") lsm.log <- lsmeans(rg.log, "Variety") summary(lsm.log, type = "response") summary(pairs(lsm.log), type = "response") @ The LS~means shown are the geometric means of the predictions, as opposed to the arithmetic means obtained above from "rg.bc.regrid". And the pairwise comparisons come out as ratios of these. \subsection{The \code{transform} argument}\ixcodesub{transform}{in \code{ref.grid} or \code{lsmeans}} For convenience, the user may use a "transform" argument to re-grid as part of a "ref.grid" or "lsmeans" call. For example, \verb|lsmeans(Oats.bc, "Variety", transform = "response")| is equivalent to \verb|lsmeans(rg.bc.regrid, "Variety")| but without needing the two code steps previously used to produce "rg.bc" and "rg.bc.regrid". \section{More on tests} \def\tj{\theta^{(j)}}%%% Notation for this section only The default settings for "test" yield traditional two-tailed $t$ (or $z$) tests of significance against zero. So if $\tj$ is the $j$th parameter (e.g., LS~mean or contrast) being estimated, we are testing the null hypothesis $H_0: \tj=0$ versus the alternative $H_1:\tj\ne 0$. We can, however, specify different types of tests in the \wixcode{test} or \wixcode{summary} functions. \subsection{Nonzero null values}\ixsub{tests}{nonzero null} If we wish to use nonzero null values, i,e., test $H_0:\tj=\tj_0$, use "test" or "summary" with the \wixcode{null} argument set to the desired $\tj_0$ values. For example, in the Oat-yield example, suppose we want to test each of the "Variety" yields against 100 (actually $\log 100$ since the response was transformed): <<>>= Oats.Vlsm = lsmeans(Oats.lmer2, "Variety") test(Oats.Vlsm, null = log(100), type = "response") @ Note that "null" should always be given on the linear-predictor scale (in this case $\log$ yield), even when specifying \verb|type="response"|. We could have specified different null values for each hypothesis by providing a vector of three numbers. \subsection{Equivalence tests}\ixsub{tests}{equivalence}\ix{equivalence tests}\ix{TOST method} The preceding results say that none of the variety means differs significantly from 100, after transforming. But this is not the same as saying that we have evidence that the means are close to 100 (that is, absence of proof is not proof of absence). To make a strong statement that an effect is small, we should use an equivalence test, which more-or-less turns the hypotheses around: \[ H_0: |\tj - \tj_0| \ge \delta \qquad\mbox{versus}\qquad H_1: |\tj - \tj_0| < \delta \] where $\delta$ is a specified threshold of equivalence. A common test procedure is the two one-sided test (TOST) method \citep{Sch87}, whereby we obtain equivalence only if we can establish both that $\tj-\tj_0>-\delta$ and that $\tj-\tj_0<\delta$. In "lsmeans", we do this by pre-identifying the less significant of these two tests: \[ t = \frac{|\hat\tj-\tj_0| - \delta}{SE(\hat\tj)} \] and the $P$~value is the \emph{left}-tail probability of this quantity from the central $t$ distribution. In "test" or "summary", an equivalence test is requested by specifying a nonzero \wixcode{delta} argument, which in turn is used as the threshold $\delta$. In the Oat-yield example, the following results are obtained using a threshold of $\delta=0.20$:\ixcodesub{test}{delta@\code{delta} argument} <<>>= test(Oats.Vlsm, null = log(100), delta = 0.20, type = "r") @ So two of the three Variety means are established as being within $.20$ of $\log100$. The natural log scale has the special property that small increments on the log scale translate to approximate percentage differences of the same size. That is, a threshold of $.20$ corresponds to about a 20\% difference: $\log 80 - \log100 = \log.8 \approx -.223$, and $\log120 - \log100 = \log1.2 \approx .182$. \subsection{One-sided tests, noninferiority, nonsuperiority} \ixsub{tests}{one-sided} The \wixcode{side} argument is also available to specify \wix{one-sided tests}. A right-tailed alternative may be requested using "side" partially matching one of \dqt{+}, \dqt{right}, \verb|">"|, "+1", "1", \dqt{superiority}, or (see later) \dqt{noninferiority}. Similarly, a left-tailed alternative may be specified using "side" equal to \dqt{-}, \dqt{left}, \verb|"<"|, "-1", \dqt{inferiority}, or \dqt{nonsuperiority}. (And for completeness, a two-sided alternative is specified using "0", "2", \verb|"!="|, \dqt{both}, \dqt{two-sided}, \dqt{equivalence}, or \dqt{=}.) In the following example, we test to see if either Golden Rain or Marvellous has better yield than Victory: <<>>= test(contrast(Oats.Vlsm, "trt.vs.ctrlk"), side = ">") @ \ixsub{tests}{noninferity or nonsuperiority} The one-sided version of an equivalence test is called a noninferiority or nonsuperiority test. It is obtained by specifying both "side" and a nonzero "delta". For example, to test whether Victory is as good as the other two within a 25\% threshold, use <<>>= test(contrast(Oats.Vlsm, "trt.vs.ctrlk"), side = "nonsup", delta = .25) @ We find strong evidence that, with the stated threshold of .25, Golden Rain is nonsuperior to Victory (so that Victory is noninferior to Golden Rain); but not strong evidence that Victory is noninferior to Marvellous. \section{Trends}\ix{trends, estimating and comparing}\ix{slopes, estimating and comparing} \index{Examples!chick weights}\index{Examples!comparing trends} The \lsm{} package provides a function \wixcode{lstrends} for estimating and comparing the slopes of fitted lines (or curves). To illustrate, consider the built-in R dataset \wixcode{ChickWeight} which has data on the growths of newly hatched chicks under four different diets. The following code produces the display in \Fig{chick-plot}. <>= require("lattice") xyplot(weight ~ Time | Diet, groups = ~ Chick, data = ChickWeight, type = "o", layout=c(4, 1)) @ \begin{figure} \centerline{\includegraphics[width=6in]{using-lsmeans-chick-plot}} \caption{Growth curves of chicks, dataset \texttt{ChickWeight}.}\label{chick-plot} \end{figure} Let us fit a model to these data using random slopes for each chick and allowing for a different average slope for each diet (a square-root transformation straightens-out the curves somewhat): <<>>= Chick.lmer <- lmer(sqrt(weight) ~ Diet * Time + (0 + Time | Chick), data = ChickWeight) @ We can then call \wixcode{lstrends} (or, its anti-SAS alias, \wixcode{pmtrends}) to estimate and compare the average slopes for each diet. <<>>= Chick.lst <- lstrends (Chick.lmer, ~ Diet, var = "Time") @ Now, let's summarize the estimated trends and pairwise comparisons of these slopes using a compact letter display. <<>>= cld (Chick.lst) @ According to the Tukey~HSD comparisons (with default significance level of $.05$), there are two groupings of slopes: Diet~1's mean slope is significantly less than $3$ or $4$'s, Diet~2's slope is not distinguished from any other. Because of the response transformation, the slopes we just computed are for trends on the square-root-weight scale. If you want the trends on the actual weight scale after back-transforming, that is possible via the "transform" argument:\ixcodesub{lstrends}{with response transformation}\ixcodesub{transform}{in \code{lstrends}} <<>>= lstrends(Chick.lmer, ~ Diet | Time, var = "Time", transform = "response", at = list(Time = c(5, 15))) @ We specified two different "Time" values to emphasize that after back-transforming, the slopes are different at each "Time", whereas (by the model specification) the slopes don't depend on "Time" when we leave it on the square-root scale. Note: "lstrends" computes a difference quotient based on two slightly different reference grids. Thus, if it it must be called with a model object, not a "ref.grid" object. \ixsub{reference grid}{difference quotient of two} \section{User preferences}\ix{user preferences} \lsm{} sets certain defaults for displaying results---for example, using $.95$ for the confidence coefficient, and showing intervals for "lsmeans" output and test statistics for "contrast" results. As discussed before, one may use arguments in "summary" to change what is displayed, or "update" to change the defaults for a given object. But suppose you want different defaults to begin with. These can be set using the \wixcode{lsm.options} statement. For example: <<>>= lsm.options(ref.grid = list(level = .90), lsmeans = list(), contrast = list(infer = c(TRUE, TRUE))) @ This requests that any object created by "ref.grid" be set to have confidence levels default to $90$\%, and that "contrast" results are displayed with both intervals and tests. No new options are set for "lsmeans" results, and the "lsmeans" part could have been omitted. These options are stored with objects created by "ref.grid", "lsmeans", and "contrast". For example, even though no new defaults are set for "lsmeans", future calls to "lsmeans" \emph{on a model object} will be displayed with 90\% confidence intervals, because "lsmeans" calls "ref.grid". However, calling "lsmeans" on an existing \dqt{ref.grid} object will inherit that object's setting. Certain other options are available; for example, the \dqt{\wixcode{estble.tol}} option sets the tolerance for determining estimability of linear contrasts. To see its current value: <<>>= get.lsm.option("estble.tol") @ Defaults for this and some other parameters are saved in \wixcode{defaults.lsm}. \ixcode{get.lsm.option} %%%%%%%%%%%%%%%%% INDEXING COMPLETED TO HERE 3-5-16 %%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Two-sided formulas}\ixsub{factors}{specifying}\ixsub{formula specs}{two-sided} In its original design, the only way to obtain contrasts and comparisons in \lsm{} was to specify a two-sided formula, e.g., "pairwise ~ treatment", in the "lsmeans" call. The result is then a list of "lsmobj" objects (class \dqt{lsm.list}). \index{lsm.list@\dqt{lsm.list} class} In its newer versions, \lsm{} offers a richer family of objects that can be re-used, and dealing with a list of objects can be awkward or confusing, so its continued use is not encouraged. Nonetheless, it is still available for backward compatibility. Here is an example where, with one command, we obtain both the LS~means and pairwise comparisons for "Variety" in the model "Oats.lmer2": {\small <<>>= lsmeans(Oats.lmer2, pairwise ~ Variety) @ } This example also illustrates the effect of the preceding "lsm.options" settings. Let us now return to the default display for contrast results. <<>>= lsm.options(ref.grid = NULL, contrast = NULL) @ \section{Messy data}\ix{messy data}\index{Examples!nutrition study}\index{Examples!messy data} To illustrate some more \code{lsmeans} capabilities, consider the dataset named \wixcode{nutrition} that is provided with the \lsm{} package. These data come from \citet{Mil92}, and contain the results of an observational study on nutrition education. Low-income mothers are classified by race, age category, and whether or not they received food stamps (the \code{group} factor); and the response variable is a gain score (post minus pre scores) after completing a nutrition training program. Consider the model that includes all main effects and two-way interactions. A Type-II (hierarchical) analysis-of-variance table is also shown. <<>>= nutr.lm <- lm(gain ~ (age + group + race)^2, data = nutrition) library("car") Anova(nutr.lm) @ One main effect ("group") is quite significant, and there is possibly an interaction with "race". Let us look at the \code{group} by \code{race} LS~means: <>= lsmip(nutr.lm, race ~ age | group) lsmeans(nutr.lm, ~ group*race) @ \begin{figure} \centerline{\includegraphics[scale=.75]{using-lsmeans-nutr-intplot}} \caption{Predictions for the nutrition data}\label{nutr-intplot} \end{figure} \Fig{nutr-intplot} shows the predictions from this model. One thing the output illustrates is that \code{lsmeans} incorporates an \wix{estimability} check, and returns a missing value when a prediction cannot be made uniquely. In this example, we have very few Hispanic mothers in the dataset, resulting in \wix{empty cells}. This creates a \wix{rank deficiency} in the fitted model, and some predictors are thrown out. We can avoid non-estimable cases by using \code{at} to restrict the reference levels to a smaller set. A useful summary of the results might be obtained by narrowing the scope of the reference levels to two races and the two middle age groups, where most of the data lie. However, always keep in mind that whenever we change the reference grid, we also change the definition of the LS~means. Moreover, it may be more appropriate to average the two ages using weights proportional to their frequencies in the data set. The simplest way to do this is to add a \wixcode{weights} argument.\footnote{ It may also be done by specifying a custom function in the \wixcode{fac.reduce} argument, but for simple weighting, \code{weights} is simpler.} With those ideas in mind, here are the LS~means and comparisons within rows and columns: <<>>= nutr.lsm <- lsmeans(nutr.lm, ~ group * race, weights = "proportional", at = list(age = c("2","3"), race = c("Black","White"))) @ So here are the results <<>>= nutr.lsm summary(pairs(nutr.lsm, by = "race"), by = NULL) summary(pairs(nutr.lsm, by = "group"), by = NULL) @ The general conclusion from these analyses is that for age groups 2 and~3, the expected gains from the training are higher among families receiving food stamps. Note that this analysis is somewhat different than the results we would obtain by subsetting the data before analysis, as we are borrowing information from the other observations in estimating and testing these LS~means. \subsection{More on weighting}\label{weights} The \wixcodesub{weights}{equal, proportional, outer, cells} argument can be a vector of numerical weights (it has to be of the right length), or one of four text values: \dqt{equal} (weight the predictions equally when averaging them, the default), \dqt{proportional} (weight them proportionally to the observed frequencies of the factor combinations being averaged over), \dqt{outer} (weight according to the outer products of the one-factor marginal counts), or \dqt{cells} (weight each mean differently, according to the frequencies of the predictions being averaged). \Fig{wtcomp} shows the LS~means for "race" using the four different weighting schemes. (Note: If the model itself has weights, then the total weights are used instead of counts.) \begin{figure} \hspace{-.06\linewidth} \begin{minipage}{1.12\linewidth} \hrule \columnseprule=.2pt \begin{multicols}{2}\footnotesize <<>>= lsmeans(nutr.lm, "race", weights = "equal") lsmeans(nutr.lm, "race", weights = "prop") lsmeans(nutr.lm, "race", weights = "outer") lsmeans(nutr.lm, "race", weights = "cells") @ \end{multicols} \hrule \end{minipage} \caption{Comparison of four different weighting methods}\label{wtcomp} \end{figure} Note there are four different sets of answers. The \dqt{equal} weighting is self-explanatory. But what's the distinction between \dqt{proportional} and \dqt{outer}? To clarify, consider: <<>>= temp = lsmeans(nutr.lm, c("group","race"), weights = "prop") lsmeans(temp, "race", weights = "prop") @ The previous results using \dqt{outer} weights are the same as those using \dqt{proportional} weights on one factor at a time. Thus, if only one factor is being averaged over, \dqt{outer} and \dqt{proportional} are the same. Another way to look at it is that outer weights are like the expected counts in a chi-square test; each factor is weighted independently of the others. The results for \dqt{cells} weights stand out because everything is estimable---that's because the empty cells in the data were given weight zero. These results are the same as the unadjusted means: <<>>= with(nutrition, tapply(gain, race, mean)) @ \subsection{Alternative covariate adjustments}\ix{covariate adjustments} \index{Example!framing experiment} The \wixcode{framing} data in the \pkg{mediation} package has the results of an experiment conducted by \cite{Bra08} where subjects were given the opportunity to send a message to Congress regarding immigration. However, before being offered this, some subjects ("treat=1") were first shown a news story that portrays Latinos in a negative way. Besides the binary response (whether or not they elected to send a message), we also measured "emo", the subjects' emotional state after the treatment was applied. There are various demographic variables as well. Before fitting a logistic regression model, I will change the labels for "educ" to shorter strings. <<>>= library("mediation") levels(framing$educ) = c("NA","Ref","< HS", "HS", "> HS","Coll +") framing.glm = glm(cong_mesg ~ age + income + educ + emo + gender * factor(treat), family = binomial, data = framing) @ The left-hand plot in \Fig{framing} displays the conventional \wix{adjusted means}, where predictions are made with the covariates "age", "income", and "emo" set to their mean values: <>= lsmip(framing.glm, treat ~ educ | gender, type = "response") @ This plot is rather implausible because the displayed treatment effects are the opposite for females as for males, and the effect of education isn't monotone as one might expect. \begin{figure} \begin{center} \begin{tabular}{c@{\qquad}c} (a) & (b) \\ \includegraphics[width=3in]{using-lsmeans-framinga.pdf} & \includegraphics[width=3in]{using-lsmeans-framingb.pdf} \end{tabular} \end{center} \caption{Estimated responses for the \code{framing} data. (a)~Holding \code{emo} constant at its mean; (b)~Using predictions of \code{emo} for each \code{treat}.}\label{framing} \end{figure} \ix{covariate affected by treatments}\ix{mediating covariate} However, "emo" is a post-treatment measurement. This means that the treatment could have affected it (it is a \emph{mediating} covariate). If it is indeed affected by "treat", then \Fig{framing}(a) would be misleading because "emo" is held constant. Instead, consider making the predictions where "emo" is set to its predicted value at each combination of "treat" and the demographic variables. This is easily done by setting \wixcodesub{cov.reduce}{as a formula} to a formula for how to predict "emo": <>= lsmip(framing.glm, treat ~ educ | gender, type = "response", cov.reduce = emo ~ treat*gender + age + educ + income) @ This plot is shown in \Fig{framing}(b). It is quite different, suggesting that "emo" does indeed play a strong mediating role. (The \pkg{mediation} package has functions for estimating the strength of these effects.) The predictions suggest that, taking emotional response into account, male subjects exposed to the negative news story are more likely to send the message than are females or those not seeing the negative news story. Also, the effect of "educ" is (almost) monotone. You can see what values of "emo" are used in these predictions by looking at the "grid" slot in the reference grid: <<>>= ref.grid(framing.glm, cov.reduce = emo ~ treat*gender + age + educ + income)@grid @ whereas the overall mean of \Sexpr{round(mean(framing$emo), 3)} is used as the value of "emo" in \Fig{framing}(a). \ifx %%% Old covariate example is commented-out %%%%%%%%%%%%%%%%%% \cite{urq82} reports data on slaughter weights of animals that entered a feedlot as yearling calves. The animals came from 11 different herds, and each animal was randomized to one of three diets. In addition, the weight of each yearling at entry was recorded. The "feedlot" dataset provided in \lsm{} contains these results. From the feedlot operator's perspective, both diets and herds are fixed effects. Let us fit a factorial model with slaughter weight "swt" as the response and entry weight "ewt" as a covariate. %<<>>= feedlot.lm <- lm(swt ~ ewt + herd * diet, data = feedlot) Anova(feedlot.lm) @ The interaction tesrm doesn't make much of a contribution here, so we will work with an additive model instead (which also ameliorates some non-estimability issues due to missing cells). %<<>>= feedlot.add <- update(feedlot.lm, . ~ . - herd:diet) @ Here are the "LS~means" for the herds, and a compact letter display for comparisons thereof: %<<>>= cld(lsmeans(feedlot.add, "herd")) @ No herds are found to be different---not a surprise given that the $P$~value for "herd" is about the same as for the original model. However, these predictions are made at the same entry weight for every herd. This is \emph{not} the right thing to do here, because the herds differ in genetic makeup, the way they were fed and managed, and so forth---which affect the yearlings' entry weights. This is an example where a treatment affects a covariate. Each herd should have its own reference value for entry weight. This is done in "lsmeans" by providing a formula in the "cov.reduce" argument. The formula "ewt ~ herd" indicates that the reference grid should be constructed using the predicted value of "ewt", based on a linear model with "herd" as the predictor. Here are the results: %<<>>= cld(lsmeans(feedlot.add, "herd", cov.reduce = ewt ~ herd)) @ What a world of difference! We now see many significant differences in the comparisons. By the way, another approach would be to simply omit "ewt" from the model, to prevent making inappropriate adjustments in the traditional analysis. With such a model (not shown), the predictions are similar to those above; however, their standard errors are substantially higher, because---as seen in the ANOVA table---the covariate explains a lot of the variation. \fi %%%%%%%%%%%%% end of commented-out section %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Another use of formulas in \wixcodesub{cov.reduce}{to reflect dependence} is to create representative values of some covariates when others are specified in \wixcode{at}. For example, suppose there are three covariates $x_1,x_2,x_3$ in a model, and we want to see predictions at a few different values of $x_1$. We might use <>= rg <- ref.grid(my.model, at = list(x1 = c(5,10,15)), cov.reduce = list(x2 ~ x1, x3 ~ x1 + x2)) @ (When more than one formula is given, they are processed in the order given.) The values used for $x_2$ and $x_3$ will depend on $x_1$ and should in some sense be more realistic values of those covariates as $x_1$ varies than would be the overall means of $x_2$ and $x_3$. Of course, it would be important to display the values used---available as "rg@grid"---when reporting the analysis. \section{Other types of models}\ix{models supported} \subsection[Models supported by lsmeans]{Models supported by \lsm{}} The \lsm{} package comes with built-in support for quite a number of packages and model classes, including \dqt{lm}, \dqt{mlm}, \dqt{aov}, \dqt{aovlist}, and \dqt{glm} in the \wixpkg{stats} package, mixed models such as \dqt{lme}, \dqt{lmerMod}, and \dqt{glmerMod}, several survival models, GEE-type models, models having responses that are ordinal, multinomial, counts, and interval-(0,1), and Bayesian models. For a complete list, use \code{help(\dqt{models})}. \ifx % COMPLETE-ish LIST IS NOW COMMENTED-OUT ----------------- \begin{quote} \begin{description} \pitem{stats}: \dqt{lm}, \dqt{mlm}, \dqt{aov}, \dqt{aovlist}, \dqt{glm} \pitem{nlme}: \dqt{lme}, \dqt{gls}, \dqt{nlme} \pitem{lme4}: \dqt{lmerMod}, \dqt{glmerMod} \pitem{survival}: \dqt{survreg}, \dqt{coxph} %%%\pitem{coxme}: \dqt{coxme} \pitem{MASS}: \dqt{polr} \pitem{gee, geepack}: \dqt{gee}, \dqt{geeglm}, \dqt{geese} \pitem{ordinal}: \dqt{clm}, \dqt{clmm} \pitem{rms}: \dqt{rms} and descendents such as \dqt{ols}, \dqt{lrm}, \dqt{orm}, etc. \end{description} \end{quote} \fi % ----------------------------------------- \lsm{} support for all these models works similarly to the examples we have presented. Note that generalized linear or mixed models, and several others such as survival models, typically employ link functions such as "log" or "logit". In most cases, the LS~means displayed are on the scale of the linear predictor, and any averaging over the reference grid is performed on the linear-predictor scale; but there are exceptions. Some objects have optional arguments that can be specified in the "ref.grid" or "lsmeans" call: see "?models" for details. \subsection{Ordinal-data example} The "clm" and "clmm" functions in \wixpkg{ordinal}, as well as the "polr" function in \wixpkg{MASS}, fit polytomous regression models to \wix{Likert-scale data}. They do this by modeling the ordinal response as a categorization of a continuous latent variable $S$, then estimating thresholds for this categorization and fitting a generalized linear model to the cumulative probabilities for each threshold. By default, "lsmeans" produces predictions of the \wix{latent variable}. \index{Example!ordinal response}\index{Examples!housing data} The example shown here is based on the \wixcode{housing} data in the \pkg{MASS} package, where the response variable is satisfaction ("Sat") on a three-point scale of low, medium, high; and predictors include "Type" (type of rental unit, four levels), "Infl" (influence on management of the unit, three levels), and "Cont" (contact with other residents, two levels). We will assume that the latent variable is normally distributed (by specifying a probit link). <>= library("ordinal") data(housing, package = "MASS") housing.clm <- clm(Sat ~ (Infl + Type + Cont)^2, data = housing, weights = Freq, link = "probit") lsmip(housing.clm, Cont ~ Infl | Type, layout = c(4,1)) @ \begin{figure} \begin{center} \includegraphics[width=6in]{using-lsmeans-housing-plot.pdf} \end{center} \caption{Interaction plot for the latent variable in the \code{housing} example.}\label{housing-plot} \end{figure} The plot is shown in \Fig{housing-plot}. Generally, the higher the influence, the higher the satisfaction. Overall $F$ tests of the "Infl" effect suggest that it is strong for all four housing types: <<>>= test(pairs(lsmeans(housing.clm, ~ Infl | Type)), joint = TRUE) @ The tests are asymptotic (signaled by "df2 = NA"), so they are actually chi-square tests for the statistics $X^2 = df_1\cdot F$ with $df_1$ degrees of freedom. Higher contact also seems to be associated with higher satisfaction, but terrace apartments may be an exception. Let's see: <<>>= test(pairs(lsmeans(housing.clm, ~ Cont | Type)), joint = TRUE) @ So the effect is inconclusive for both atria and terraces. The \wixcode{mode} argument may be used to choose what to examine. Modes \dqt{linear.predictor} and \dqt{cum.prob} create an additional pseudo-factor named "cut" for the thresholds at which the predictions are made. <<>>= ref.grid(housing.clm, mode = "cum.prob") @ So here are our estimated marginal probabilities for "Infl" of being less than highly satisfied: <<>>= lsmeans(housing.clm, ~ Infl, at = list(cut = "Medium|High"), mode = "cum.prob") @ Compare these results with those for the back-transformed linear predictor: <<>>= summary(lsmeans(housing.clm, ~ Infl, at = list(cut = "Medium|High"), mode = "linear.predictor"), type = "response") @ The results are similar, but somewhat different because of the back-transformation\ix{transformations} coming before (first case) or after (second case) averaging or computing confidence limits. \subsection{Chick weights, revisited}\index{Examples!chick weights}\index{Examples!nonlinear curves} Previously, we used the \wixcode{ChickWeight} data to illustrate the use of "lstrends". That example made the simplifying assumption that the growth trends are linear, which is clearly questionable. To do a better job of fitting the data, consider instead the idea of fitting a \wix{logistic curve} to each chick's data. The \pkg{stats} package provides the "SSlogis" function for this purpose: it is an S-shaped curve (scaled from the cdf of a logistic distribution) having three parameters "asym" (the asymptotic value at which it levels off), "xmid" (the $x$ coordinate of its inflection point), and "scal" (roughly the difference between the median and the .73rd quantile). Also, the \wixpkg{nlme} package's \wixcode{nlme} function can fit a set of \wix{nonlinear curves} such that the parameters of those curves may be modeled using a mixed-effects linear model. Accordingly, let us fit a model where each chick has a logistic curve for which the "asym" parameter varies randomly for each chick, and for which both "asym" and "xmid" depend on the chick's diet. We chose starting values by examining the curves and making a rough judgment of the typical asymptotic value, midpoint, and scale for each diet. We need to keep firmly in mind how factors are coded; so we explicitly show that we intend to use \dqt{contr.treatment} coding, by which the first mean is estimated directly, and the remaining estimates are offsets from that. We need a set of four starting values for "asym" and "xmid", and one for "scal". <<>>= require("nlme") options(contrasts = c("contr.treatment", "contr.poly")) Chick.nlme = nlme(weight ~ SSlogis(Time, asym, xmid, scal), data = ChickWeight, fixed = list(asym + xmid ~ Diet, scal ~ 1), random = asym ~ 1 | Chick, start = c(200, 100, 200, 100, 10, 0, 0, 0, 7)) Chick.nlme @ Now we can use "lsmeans" to compare the parameters based on "Diet": <<>>= cld(lsmeans(Chick.nlme, ~ Diet, param = "asym")) cld(lsmeans(Chick.nlme, ~ Diet, param = "xmid")) @ The result is that diet~3 has both a higher mean "asym" an a higher mean "xmid" than the other diets. This is compatible with the results of the earlier "lstrends" analysis, but grounded in terms of the parameters of the logistic curve. \subsection{Extending to more models}\ix{extending the \pkg{lsmeans} package} The functions "ref.grid" and "lsmeans" work by first reconstructing the dataset (so that the reference grid can be identified) and extracting needed information about the model, such as the regression coefficients, covariance matrix, and the linear functions associated with each point in the reference grid. For a fitted model of class, say, \dqt{modelobj}, these tasks are accomplished by defining S3 methods \wixcode{recover.data}".modelobj" and \wixcode{lsm.basis}".modelobj". The help page \dqt{extending-lsmeans} and the vignette by the same name provide details and examples. Developers of packages that fit models are encouraged to include support for \lsm{} by incorporating (and exporting) "recover.data" and "lsm.basis" methods for their model classes. \subsection{Bayesian models}\index{Examples!Bayesian Poisson regression} Certain \wix{Bayesian models} are now supported by \lsm{}. For illustration, consider a two-factor Poisson regression example given in the \wixpkg{MCMCpack} package: <<>>= library("MCMCpack") counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12) outcome <- gl(3, 1, 9) treatment <- gl(3, 3) posterior <- MCMCpoisson(counts ~ outcome + treatment, mcmc = 1000) @ The result is an "mcmc" object\index{mcmc@\code{mcmc} object} (defined in the \wixpkg{coda} package), but it has an added \dqt{call} attribute that enables "lsmeans" to do its work. Here are results for treatments, averaged over outcomes: <<>>= ( post.lsm <- lsmeans(posterior, "treatment") ) @ This is a frequentist summary, based on the mean and covariance of the regression parameters in the "posterior" sample. But \lsm{} provides an \wixcode{as.mcmc} method to obtain a sample from the posterior distribution of the LS~means\ix{posterior LS~means} (that is, the original posterior sample of regression coefficients, transformed by the appropriate linear functions.) <<>>= library("coda") summary(as.mcmc(post.lsm)) @ Since "as.mcmc" produces an "mcmc" object, any of the other available methods may be used with it. \section{Discussion} The design goal of \lsm{} is primarily to provide the functionality of the "LSMEANS"\index{SAS!LSMEANS} statement in various \SAS{} procedures. Thus its emphasis is on tabular results which, of course, may also be used as data for further analysis or graphics. By design, it can be extended with relative ease to additional model classes. A unique capability of \lsm{} is its explicit reliance on the concept of a reference grid, which I feel is a useful approach for understanding what is being computed. Some \lsm{} capabilities exceed those of \SAS, including the "lstrends" capability, more flexibility in organizing the output, and more built-in contrast families. In addition, \SAS{} does not allow LS~means for factor combinations when the model does not include the interaction of those factors; or creating a grid of covariate values using "at". There are a few other \R{} packages that provide capabilities that overlap with those of \lsm{}. The \wixpkg{effects} package \citep{effects,fox09} can compute LS~means. However, for an unbalanced dataset, it does not use equal \wix{weights}, but rather it appears to use ``outer'' weights, as described in Section~\ref{weights}. Also, it does not check \wix{estimability}, so some results could be questionable. The emphasis of \pkg{effects} is on graphical rather than tabular displays. It has special strengths for curve-fitting models such as splines. In contrast, \lsm{}'s strengths are more in the area of factorial models where one wants traditional summaries in the form of estimates, contrasts, and interaction plots. The \wixpkg{doBy} package \citep{doBy} provides an "LSmeans" function that has some of the capabilities of "lsmeans", but it produces a data frame rather than a reusable object. In earlier versions of the package, this function was named "popMeans". The package also has an "LSmatrix" function to obtain the linear functions needed to obtain LS~means. Also, the \wixpkg{lmerTest} package also offers an "lsmeans" function, as well as "difflsmeans" for differences of LS~means. These are designed particularly for "lmerMod" objects. \bibliography{lsmeans}\bibliographystyle{jss} \printindex \end{document} lsmeans/inst/doc/using-lsmeans.pdf0000644000175100001440000156207613013732366016761 0ustar hornikusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 4878 /Filter /FlateDecode /N 91 /First 735 >> stream x\[s7~_k3;pjkl"߽-,&Tb?_w-EgG`&p`"Cח)zqsWlr==m1Tկ?]CaBQyO}w:`[>Q :dzN )-7Ǫ~DNh&ViTTlktQsp˂"DRlHʮHRLIR)~S &%I$5P$TP\a* D$oAUؑB dQ֦J6֥C!(T0*8oAj$Ƀ$7'Ή6 RHQW!I6#.HFA:UzT3X8\SeNrEI7 I7b#P̆뤼`DoA֕gV@qG=I oHPD70;U}?ѳo1l~t1RYS{t:OC1اG#XRDIbF4  oȑl ~vGO?>ߔf;'/N׏>< v/!oGo>~A_"G~|Sa?䩏ΒH1u6#_6kl8@ n!+1V04;FSAz>$aaK("%KwyҮnhrgdx6\YaޕEF,(~ lsEӊ7Kk[pSl遧NcScey]Ԗ:MfaY{~/\ Fn1;W7 uwߑ;y1ܦ37RnEK&lMC0*& e|m᫏{㻿}ꇗhp6|McZ9yǵy5W2ONNNspCq;O?9a+Ĝ6bܞv'GOq¼ۅM?kq½?g|%ve{lH]!%77g}~:={|0 l4ܕsҘƲ'#XT)ۃLPCzO ͆]qB/FhǔSEY|/L%P.ipa2,;{ON.Cj O;z2/-瀏r3Y[;Vu;pi0Ҁh2yuFNVCFv$~Io0MtF;ygS^\8|O@ J~:^_ڏ$ớ'ږTFꞶ)iZsjq/_˳=~0]=x3A{B"Y8xjϢ)x sKc _(/vFWŃJ/cZAʫh=T*ڭE^kuTHpeXZ@ߝ<ː]">bDJlwLBvdCZ\4)eEcf0,R [S{NWdj ZH9>j%AJLvBUYFlTcҢ51j163q4 p) =-걔I)h}ڤ\.za=DvF s, +ͣhD&'`\. bج ޸zO{6NY˞5UuO/njQҳɪ RgVۧo:=ruo뼼cVY6KI{)"(lH--#I/6 Ϟ#Ű [K&!P:rD6_0NYC2g^ZD}IZHgȻk돡VќhXNO. 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(Jr%~Ɂ|=Uu+^ %y"DYO2}Ew}LbMVW򢞒F+DDs6ēvGJ$1ma&S +}><ׄE +oؒLy 3? aXa<]cV߶ *Ѐq¸m6>;nnZY9i{єb|f?xǡsOC>ݽ`:U;4!fh ߱"^VjoYǵVGAK"wAQW>4ucWWu;躝+H&+rX՗o vޏX @GL"łU;sJP/#Ln"L:R޳v>MpgiK6p4}N; Xw&GA=x~0?T:j{ m.ј[p~%=WW?ݞ~ 3 ?oףxL ܾ:;'tU kwrMnSgק4L.op>xLt"LJm=|f|F@Iendstream endobj 93 0 obj << /Filter /FlateDecode /Length 2901 >> stream xڭ]6= ddޡ({Gc[e7C')Mp(r8jN޾Hxū7Y91ebMMIIQKbnf=f7ɲ$+JH[vdbNqlbl4qƟqlgOIy3[MO0Z#ɍɉU~4o'nl_OȈ[x_2mgtOg Oi`f7>SK~73 [2p^&H3SY%*]StkDtpSٞΣ ^G7? _AX<2R#Z`X9]ܖyQb0]{4 iHЏe{\ YM2# j4Y #@lT[B.4P@O5* r+ aA \'!@@dT̬<4647t9_GrWw-{ >1 2\0XDVwEU M @@*?UI[6e!@3zw aD[=ժ=vXk"k[e#l,D=,A[vD!'B.xKFR4k{oLN |V9!ѣdE>P`煓`Lex !_**lQ߃F?:˹HӤ-)C a/  FEV+p rEƗsXDcrgb,AX|}^% p?ݢ~s c:Kz}LU"WL@ʀ_~#g#i; |/FNn]}R?q_Aw 1:M~ Eklٿ-q ${!^GWp/ʗkMwEfR]ꁪ<=uFaҫ %I @p%lQwkQwca @6俽[ZcQ\*=cDpӴ`/*Hh 䃕sCnroGJBX *./TEacI~Kz?פÎ^ΣZ{x<)k-@K_p{3bHi7AyP犬ElK_t=֮bع~ ޖ\ԞtG9>렦ʗ\vWE,F\?kO;/=p>@sEfEzҀB .t?*706Zk]M2~4.K۴QXž2n];u@ */Hy8QUwlRnEQ]B.S(H'}[)*g1Px c{ n^E%EQ d( ڕA=ɓ}WRoLFJT>.5*ȮJ;Dvch# ȡ^:z ꩊ\wQ{{J k9'.9,,ēd:]|(a,~)ȵ^+0ՈɇkUO|26\,MRX1ڢ!s11FZrc[ƪRItD#a9jV<01ơ8dùZn~u^(;fe2WBH}r7D$g|*A^we>ne$ˤnSa<( >F݈5&:'|ro*y FQ?Į29qy!;t8KY Tp_HMNVsc%*aP!Q |c G_Sj 4,Y;Z>g0PQPþm̸\/>/%瞽PWǜ)E܎Z?㈆ю\+y*y¼hc/11gZ R1a奙!\43P,а׀EI0/<9;_wtL!+-@ΈX'RKL>Qam<]( yWN>!M5X0r+ {Dx r׃[$?μ~` +Ztţ5PQA2 ScV_|{H!p1y'U 9ۈ857߷΢ 48קּA 7ixm}XgXEXo0?ܿOendstream endobj 94 0 obj << /Type /ObjStm /Length 3062 /Filter /FlateDecode /N 91 /First 815 >> stream x[َT9}#h"Zb-hzEe%|h cB{8 &|NWr֪X#NI8z%Ep *cT _9+*{cR%yD .NK`)8e<ss&^T6|R!{rU((  0# ^E/.SX!X 2I-4d%>1f `$%.sRcT# ($J)QQPرT)14ۦ\PbC$,.ESa;I*WY*Rœ-"ô!o *OAJ~6+5T^\q4T }^awNɓmCX @(A+TC%ΩYaVcUe\3hiyJЀ#%h5|w)s{WʀUYn7V~CFPx?0P/,w9p Cwy=_o8w_{ WwVgƙ<}|w<fwvώvwM x_n!->oYG|OOG~ PW 3N_HF,}|r)ZhrNty;ϼc}/]/$P}[-xz&t̃m;mJpޜۧ 3D[u++zjա~ ;i0yy&XaPv$hsv`Rgex12հhVZLnhO@  Ĕ ? `4aBH#(iPY=iOvdv4h8%+sl!9a6&$ BZivBͱ<F5"#؃k(+f(^hbO BRh@XQ`KԄm:jI {܎y)] uRXA(pL>`;>k;f~.Nap)Tg˒u4a< ED"J[GuYyP i<ӬW!ךGP?DA1 bA-,mBWH(GHw >쐷ש؃¤^-z$ oU1|k' /8$Z%ȁsf* 02:S݌H,F*Lp,q2 -j$u$!Mn $U*28Hf#BPIȌ:NQ\p-+xa&~|ԵBJFeԹcgF8pƂڲ!T([Ґ_̔lJ5I/*E̍A)]3tt.N'q3A[d0B@L q%͔I bX0eIR-CJ98YF3Ҳe̎lZ1sE.~|KBg*\`lN= '{lCÏWvD` PP-*rJ[̙pm $|?v,2fW[*խģ:V~aJ-.A¹0<ٳ=W4Ǭyι"05#dN>Џ#PO"ëbFQi͔m.lEHK fj;uZ3jC+#4D!v_[Q׆O`*eJlݡ Cpۺl`Wȃб.L2?(c)xRh.D6@;Vk:*m'`͸}!X8ǵ4J@aY%6=^7?eg 7.kn (0زv RTp}S/>~@ 5 9d17.NB@,Y2VsZ3b2%!*p] v"T.ʚkNe )eАM X= -lC+CK*VdD 2P*#qP Vi(8U kh~r쑻83 Cj/aI (I m`j/pWl{O-E=J S/^:17Xg̙OÍnd*9V+٘Om¸֦ wT S6`mCІK+)~Xp4a_3s: 7;_?Eg /ЏS?~, SEX^b;N/vzӓNO:=ӓNO:=ӓNO:N/uzakt9vzK^r;N/wz˝^BfŮO ̓C7Z&/{ڞvx :۾vk1Y*72{;04TsrNW^[Ԭ*N@?D ˅оdRW@oFJJ9mńPZL dת/?o_{Yuqgܻ[r}PLAMR'Cks7;> Zw27L{Q-}$gGT#,yz-. 30cyf^K2ʬM{Ӽ5slN̩ٚڼ[6fgٛ[a4Yﶈϱo9~xLmwSB*,-0vdWǛۧoEޯO^*ćK? ss;q_"LԜ]ՔdUE$)SBHS)=ޜlZqٯv.o?n$pı?y.^=\풽_{]fY\o߄]|9)lrehc`թQendstream endobj 186 0 obj << /Filter /FlateDecode /Length 2693 >> stream xڭZKϯ0rkėdz AL#LmZ.䷧^)YuEŪb=>;[/o2yʦzq{(Ȫ4jq];˕R&re *iZq?n\,J!lҰ)';M$ǯ5;6;nޝ}O`s \'.y} N{\vTtrJnM#j ~{S@gmh{ k"EGhߡ"Quʤ 4 |Δ%zw6tqGAVcK-+xϔթ0@ N'o VFeɞh<݄-YjdA$= oG "=l[f_vE=LVg=76#z'Obd$U:-J5 cf/5DmSѡ$Ad{bZZ4sIa\%H .eVХ5A8|"οZ $՚+rw-ClߓQ>6ƺ婩(;KsJZeIkfYޚ?##cZDވۣ(+]DF&Z]lw;!4 o|4sNaV'}Q+wZ_\d )Y.D Wyh܉4>`*Yꬊ;M}cFB!E.@?qƉN)̻& k?lV(Yź0ec 9uV{?d'RHY] ؤ%.5 &RW:^cW Kv{( Ѽةi1:&e+qh,k0hom3z)fMAb2C0rU<}GSiU<,l&v΅dDQQG&@Ϝ1v>fd3)s E J` |ٕza>WQw xR} LJlAA6PV|4% \ TWa><˜88 {.ٕ7{ERl]f,L3ua*B%&rg}+PeK e\u0iKulI*W)HQs'koVl͟w(PNo9Cƴk;~HZ}?sdЀ9w!u4 >vrd=mNSO3 3π"TsFUxFof"o0+*ΕyW#>"F/db6GX!#ۚ\(7ooLȎҧM{L\|5S'кƈt6Llt.Pf M^G0H٦WoAG|'\=Zih"׼# =4W`.!?>v~i7D " _ iԔꠊuVچ4%U|NUOEdr\x8gG+.dZC~Cܔ͸FS' r>g6fҟ c8,\ O)2/$d #6҂njdd5K燃5=C/u) وRґfx%CNb K@ot)aQn2#)S8W r) ȶnHl 8j#8:u}e#< &|XŵOrf-"(_x\?E0>Ss s}y 5lGl`EvtbWBjW] gP2ZN)LWp0"S|jO+ Y18ID&ΪQ *-]qU#iB+ADF_ a<ҡiBӆ |"N_iQXiVOT%d1HpoPyǓVlvTw GٺKB} KW^8E|@ bKɅ hd#Ȯhˇ2K37d**8 xse,T ;w?9;+B bN% > stream xYKo6Wۋ )P`[l =bHn_9-B4obU+W7󂳪a /eMŔuWY><'-]ۻ_L3Lm][ֺ[+1ߋ_ojKp^))`nt@{7ݢL>slvNwWHPa(Wf.G ,ŜJ& ֨e7+׭qZf4覭Q&섥 J*iTLK]H@J\$K4R_1Jc1bL큝)cO@iG3֤nmpy4s:=PX& V럃Ь ?dLr I@ R"P3b&9NQ$5Ava)IDc?>8}$IXFMg55րZSKRК\O(KQXfG5җs.*/{ˤЌ]fNsINt6̡It2v?Lٌ@3nDlHHrxc! yI< Uك?X~(fjk6I;wtƚ&I9˾v}WO P,J$v܈5?q0b)켞lj${kteS%֤_\9W<tƘx]!ΆHTΒ>s(&|^h6yaH'IvBߪ* _'Ix & oL*r'O) gp@ QE%widn!2 Q~h:]hAsq7\ԕ" MP减՞h=Qq>znq \~ܵϰ݇ }{ =Ͱ- Gm:ݯD]1㜗`x;O],wݫ[:Vۍgfc̙-C]"L鸲u^l 9z{"$M*#;\ k5:kS.-t{ٺ> stream xM~g/gUCN;v&L;[-{f@d&DAħ~Q. ,TYҫn4EESefWk]jYԲ秃Z+miB:L'Z6^X1 R2FwB[5š"$7TV}r*稰AvA ?g JQay&i 䆕9*X&Lm7nPXe2LT-3}RMgg1,r*Ygt>Cnhq97 e b&*[ r]Y( f;]4&- ${o0O!!9@vx 3xmOmJ޽ A;=СU Ձbkd{ILgPg ؇Z\qkӛK'&Q^\vspu]ݲ#KvdG˜0/$Mh1_vDしѶ@QEa]1/+g|i'09Tr)ښzyhM-|Cx;%PSm o-=5*sa@|RBj"Qc  tVxG~CEDLl8&bҊxE3`DY:JQJf=&aafrNٹ;ӏC_ O*KtdHٓ"-RٱUc'HOOϊuLHb]5 * I`pq>좲yeg,U 3~fT^EcuDxQ~D /r3QhxHIh<-4~6-]QFKDߖ MNGY/3aM L~VLa!$.B1}t([!WԵUtv-`I:XY'I2&|dQ†̖O1^!s^։L%Jb.j_b.krl!EB\lE{ , Ue cOjS_'TO*"W ԱjƼpef:28c{~dàr=.BˁMh_fNa-0!@:@qDH"$8@0R F nfPrz90q\tЬ )>l3Ї'3 o$|jɠf'!!Y":dCXS:I=ْߟ B$Z+q~AqC^n=F?#/ta㐅n1(Nd8È\c2:hdQI-3X3sj;Y3.]0jD Fhq.ZgP;S&$]xv֊ JԝjIw¢dBW5vȏvJZ SDO$}|~&8=+H{b/"P;LSKFL)jM$9!R:$3Ԁu61q6Á+SG4B`GrI}w'FvE% XyQ.H |ƂmK ӆ?͙E5b68$-V_H=Z'@!=€ "܁- ,}܊=m`:O>sIܤ5;Ry Rz"{>[ѺpfDgC[t \EںpjySx *]I*X4G׃ˎXfeulHz?p܏nQ 2G}Sx]Q0EqC')dܥ6uDBSv>V'I#|:ؤ mDj\jͼ⎫w39)2R?/G8x<`؎} (N,d*RźHPηn6QEa~Pr -0U|ֿ_oyo3"Rlk%ҕ#}c;6A..I-AہK/ݜ÷\7𲆨ʔUj\ir}z",S1C /"6AJDWrmCi$' f6rjuT˿3;fљɣ2۠ۨQ'wa8ɬ]E jQrs$a6kc(Kzmlp2gg-UԦ(v2]ҳW XKm^-At(]EEaadwl(e z~|V ÙakYj]֙祎K^hA3$鞟"c>XNm|V8-\UW Ȱ5 SZXcx7.~WΐUsRpd1-G7T~~w h0ۯ ҋ7ߕ-|ABu\(?,>GQ3Q-TR9Xum)[T#Kt5k l*_Զr-f60 .h=R(> stream xˎeH䐅7@6yg3 ߞzub9lvWWW׻Jg7zWxvkgWYkfmTk5[0 `]o j ۹~nGa0M5,'z7"o՞_q^f7Gw3Ui"tUW͖8GTM8e}qPx{a`ϖOwƝ} _eӆ)=Dve3Xl.0LZK2 E`M+cLP%%n9hC5ʹ똄7 ?Qxp˼ԑAY\C#okd< "nDZڍ3o \w #]a m0Sg*)7,3vꪑNe^\@zt_~ۓo'oAM/C#R͒IWhńšrY\KM LD7ciLоToa_`>n#{ȷ| "IS0u$8%R"H^-˝<(@ uZ+b 2˺F)AWJ .Uz *QK=0h-i-(lT5O0 Nn3J6i:"0 J&;yD:tByTyF^}xyы() ܾB&KMkn ŁNTqTf)UL=cdS\V hTݜY7ދ cF}W9d*ҘCuI?c%*ε[Y֍d -dttx=yx~Q_IچEݖqs<e'%yx2wq{ -35O&i_~_mqUd1 j_g?OWIvqL0L0&L _Yt\Xy\iB`k*Dhz;h4p*Tlעќ _F?0OlOWa7xpXR ~XG1H_Ky6.D>"DBRp; OcV,(NP@PH] Db[c#grLY*CGLMN]\6s9]R)TH+RiL`R׈]Ө qJ0 IdM}B2ƃbzAlZc`e}$"PLW?zJ7Q}DM !UD_T F(k91$vybg;!6`BV6ewRmoshIl{y/Ak[ն g%{=/\| WӢbZFu:ؖ*taq#g]7Y^%#dNr6 dly=L)v>\`n. m HQ/2k i HW aj/6$IH7swg,*#ɥ`dPxu罆 >ƈ.5!, `x΃_ ݆7H\|S- @~*f\gʗz!%I)E4:~|HA%`Z( O˒J.:3ZO*/6BpO_rx,Q-| RI*Q8K7A|.d^ bV0?D6ɀ i d?=pK0|R7+=!&ܵ!$' /&9$#QO}*9`wՇ)?r'cj08eȜ;+2g鷊o To& v*fM`fZ‚ფR~KJS%Ge]QYO).J2桛?e\F g(g Wfx6Fǟjq/9 5-C㳷M۲7U? 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should be fixed in later versions mo.excl.lm <- lm (yield ~ Block + Variety, data = mo, na.action = na.exclude) lsmeans(mo.excl.lm, "Variety") # ------------ univariate ------------- # make an unbalanced, collinear, dataset with covariates set.seed(19841776) warp <- warpbreaks[-c(1,2,3,5,8,13,21,34), ] warp$x1 <- rnorm(nrow(warp), 17, 3) warp$x2 <- warp$x1^3 / 1007 warp.lm <- lm(breaks ~ poly(x1,3) + x2 + wool*tension, data=warp) # Note: This model is not full-rank ( warp.lsm <- lsmeans(warp.lm, "tension", by = "wool") ) # (Nothing is estimable) # However, contrasts ARE estimable: (warp.pairs <- pairs(warp.lsm)) #switcheroo of by variables: (tmp = pairs(warp.lsm, by = "tension")) # compare these contrasts pairs(tmp, by = "contrast") # Joint tests test(warp.pairs, joint = TRUE) # all 6 but reduces to 4 test(warp.pairs, joint = TRUE, rows = 1:3) # just wool A test(warp.pairs, joint = TRUE, rows = 2:3) # just wool A but not lin dep # should be identical result test(warp.pairs, joint = TRUE, rows = 4:5) # just wool B # Test different ways of accessing data ## ... using "with" ... warp.lm2 <- with(warp, lm(breaks ~ x1 + x2 + wool*tension)) lsmeans(warp.lm2, ~ tension) ## ... using "attach" ... attach(warp) warp.lm3 <- lm(breaks ~ x1 + x2 + wool*tension) lsmeans(warp.lm3, "tension") detach("warp") # won't work if detached try(lsmeans(warp.lm3, "tension")) # However, we're OK again if we use 'data' lsmeans(warp.lm3, "tension", data = warp) # --- aovlist objects ---- # dataset borrowed from pbkrtest beets <- data.frame ( harvest = factor(rep(rep(c("h1","h2"), each=5) , 3)), block = factor(rep(rep(c("blk1","blk2","blk3"), each=5), 2)), sow = factor(letters[c(3,4,5,2,1,3,2,4,5,1,5,2,3,4,1, 2,1,5,4,3,4,1,3,2,5,1,4,3,2,5)]), yield = c(128,118,95,131,136.5,136.5,150,140,99.5,156, 99,128,126,120.5,137.5,147,153.5,100,139,141, 115.5,135,130,134,91.5,155,140.5,142,151,102) ) # Use true contrasts for coding... old.opt <- options(contrasts = c("contr.helmert","contr.poly")) beets.aov <- aov(yield ~ harvest*sow + Error(block/harvest), data=beets) lsmeans(beets.aov, eff ~ sow | harvest) # restore old 'contrasts' that confound the intercept options(old.opt) # --------------- Other stuff ------------------- # using cld cld(lsmeans(warp.lm2, ~ tension | wool)) # passing to glht require(multcomp) # This will fail because glht can't deal with rank deficiency # Hope this changes. try( as.glht(pairs(warp.lsm)) ) # However, warp.lm2 isn't rank-deficient warp.lsm2 <- lsmeans(warp.lm2, ~ tension) warp.con <- contrast(warp.lsm2, "eff") summary(warp.con, adjust = "sidak") summary(as.glht(warp.con)) summary(glht(warp.lm2, lsm(eff ~ tension | wool))) # confint confint(contrast(warp.lsm2, "trt.vs.ctrl1")) # lstrends warp.lm4 <- lm(log(breaks) ~ tension*wool*x1, data = warp) lstrends(warp.lm4, ~tension|wool, var = "x1") lstrends(warp.lm4, ~tension|wool, var = "x1", transform = "response") # exotic chain rule examples lstrends(warp.lm4, ~tension|wool, var = "sqrt(x1-7)") lstrends(warp.lm4, ~tension|wool, var = "sqrt(x1-7)", transform = "response") # -------- Transformations ------------- ## ... of response ... warp.lm5 <- lm(log(breaks) ~ x1 + x2 + tension*wool, data = warp) warp.lsm5 <- lsmeans(warp.lm5, ~tension | wool) summary(warp.lsm5) summary(warp.lsm5, type = "resp") ## In a GLM # One of the glm examples... d.AD <- data.frame(treatment = gl(3,3), outcome = gl(3,1,9), counts = c(18,17,15,20,10,20,25,13,12)) glm.D93 <- glm(counts ~ outcome + treatment, family = poisson(), data = d.AD) ( lsm.D93 <- lsmeans(glm.D93, ~ outcome) ) # un-log the results to obtain rates summary(lsm.D93, type = "resp") # un-log some comparisons to obtain ratios summary(contrast(lsm.D93, "trt.vs.ctrl", ref = 2), type = "resp", adjust = "none") # weighting nutr.lm <- lm(gain ~ (age + group + race)^2, data = nutrition) lsmeans(nutr.lm, "race", weights = "equal") lsmeans(nutr.lm, "race", weights = "prop") lsmeans(nutr.lm, "race", weights = "outer") lsmeans(nutr.lm, "race", weights = "cells") # covariate predictions feedlot.add <- lm(swt ~ ewt + herd + diet, data = feedlot) lsmeans(feedlot.add, "herd", cov.reduce = ewt ~ herd) lsmeans/tests/tests1.Rout.save0000644000175100001440000005203013013732335016127 0ustar hornikusers R Under development (unstable) (2016-10-02 r71437) -- "Unsuffered Consequences" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # Tests of lsmeans for lm and mlm objects > > require(lsmeans) Loading required package: lsmeans Loading required package: estimability > > # ---------- multivariate --------------------------------- > > MOats.lm <- lm (yield ~ Block + Variety, data = MOats) > MOats.rg <- ref.grid (MOats.lm, + mult.levs = list(nitro = c(0,.2,.4,.6))) > lsmeans(MOats.rg, ~ nitro | Variety) Variety = Golden Rain: nitro lsmean SE df lower.CL upper.CL 0.0 80.00000 5.540591 10 67.65479 92.34521 0.2 98.50000 6.602048 10 83.78972 113.21028 0.4 114.66667 8.695358 10 95.29220 134.04113 0.6 124.83333 7.303221 10 108.56074 141.10592 Variety = Marvellous: nitro lsmean SE df lower.CL upper.CL 0.0 86.66667 5.540591 10 74.32146 99.01187 0.2 108.50000 6.602048 10 93.78972 123.21028 0.4 117.16667 8.695358 10 97.79220 136.54113 0.6 126.83333 7.303221 10 110.56074 143.10592 Variety = Victory: nitro lsmean SE df lower.CL upper.CL 0.0 71.50000 5.540591 10 59.15479 83.84521 0.2 89.66667 6.602048 10 74.95639 104.37695 0.4 110.83333 8.695358 10 91.45887 130.20780 0.6 118.50000 7.303221 10 102.22741 134.77259 Results are averaged over the levels of: Block Confidence level used: 0.95 > > # Try putting missing values whenever Yield is "Marvellous" > # plus another one for good measure > mo = MOats > mo$yield[mo$Variety == "Marvellous", 3] <- NA > mo$yield[2,4] <- NA > mo.lm <- lm (yield ~ Block + Variety, data = mo) > lsmeans(mo.lm, "Variety") Variety lsmean SE df lower.CL upper.CL Golden Rain 107.825 3.658594 4 97.66711 117.9829 Victory 97.625 3.092077 4 89.04002 106.2100 Results are averaged over the levels of: Block, rep.meas Confidence level used: 0.95 > > # Same as above, but use na.exclude > ## In R 3.0.2, this will return NAs for the SEs and test stats > ## Reported as Bug 15693 - should be fixed in later versions > mo.excl.lm <- lm (yield ~ Block + Variety, data = mo, na.action = na.exclude) > lsmeans(mo.excl.lm, "Variety") Variety lsmean SE df lower.CL upper.CL Golden Rain 107.825 3.658594 4 97.66711 117.9829 Victory 97.625 3.092077 4 89.04002 106.2100 Results are averaged over the levels of: Block, rep.meas Confidence level used: 0.95 > > > # ------------ univariate ------------- > # make an unbalanced, collinear, dataset with covariates > set.seed(19841776) > warp <- warpbreaks[-c(1,2,3,5,8,13,21,34), ] > warp$x1 <- rnorm(nrow(warp), 17, 3) > warp$x2 <- warp$x1^3 / 1007 > warp.lm <- lm(breaks ~ poly(x1,3) + x2 + wool*tension, data=warp) > # Note: This model is not full-rank > ( warp.lsm <- lsmeans(warp.lm, "tension", by = "wool") ) wool = A: tension lsmean SE df asymp.LCL asymp.UCL L NA NA NA NA NA M NA NA NA NA NA H NA NA NA NA NA wool = B: tension lsmean SE df asymp.LCL asymp.UCL L NA NA NA NA NA M NA NA NA NA NA H NA NA NA NA NA Confidence level used: 0.95 > # (Nothing is estimable) > > # However, contrasts ARE estimable: > (warp.pairs <- pairs(warp.lsm)) wool = A: contrast estimate SE df t.ratio p.value L - M 24.5835929 6.065419 37 4.053 0.0007 L - H 24.7443633 6.179264 37 4.004 0.0008 M - H 0.1607705 4.959344 37 0.032 0.9994 wool = B: contrast estimate SE df t.ratio p.value L - M -1.5953197 4.805899 37 -0.332 0.9412 L - H 8.2584640 4.920788 37 1.678 0.2269 M - H 9.8537837 4.687418 37 2.102 0.1032 P value adjustment: tukey method for comparing a family of 3 estimates > > #switcheroo of by variables: > (tmp = pairs(warp.lsm, by = "tension")) tension = L: contrast estimate SE df t.ratio p.value A - B 20.604585 6.155409 37 3.347 0.0019 tension = M: contrast estimate SE df t.ratio p.value A - B -5.574328 4.907136 37 -1.136 0.2633 tension = H: contrast estimate SE df t.ratio p.value A - B 4.118686 5.000739 37 0.824 0.4154 > > # compare these contrasts > pairs(tmp, by = "contrast") contrast = A - B: contrast1 estimate SE df t.ratio p.value L - M 26.178913 7.679004 37 3.409 0.0044 L - H 16.485899 7.867834 37 2.095 0.1046 M - H -9.693013 6.867934 37 -1.411 0.3456 P value adjustment: tukey method for comparing a family of 3 estimates > > # Joint tests > test(warp.pairs, joint = TRUE) # all 6 but reduces to 4 There are linearly dependent rows - df are reduced accordingly wool df1 df2 F p.value A 2 37 9.709 0.0004 B 2 37 2.471 0.0983 > > test(warp.pairs, joint = TRUE, rows = 1:3) # just wool A There are linearly dependent rows - df are reduced accordingly df1 df2 F p.value 2 37 9.709 0.0004 > > test(warp.pairs, joint = TRUE, rows = 2:3) # just wool A but not lin dep df1 df2 F p.value 2 37 9.709 0.0004 > # should be identical result > > test(warp.pairs, joint = TRUE, rows = 4:5) # just wool B df1 df2 F p.value 2 37 2.471 0.0983 > > > # Test different ways of accessing data > ## ... using "with" ... > warp.lm2 <- with(warp, lm(breaks ~ x1 + x2 + wool*tension)) > lsmeans(warp.lm2, ~ tension) NOTE: Results may be misleading due to involvement in interactions tension lsmean SE df lower.CL upper.CL L 38.01851 3.027239 38 31.89018 44.14684 M 26.51562 2.358621 38 21.74084 31.29040 H 21.50623 2.379736 38 16.68871 26.32376 Results are averaged over the levels of: wool Confidence level used: 0.95 > > ## ... using "attach" ... > attach(warp) > warp.lm3 <- lm(breaks ~ x1 + x2 + wool*tension) > lsmeans(warp.lm3, "tension") NOTE: Results may be misleading due to involvement in interactions tension lsmean SE df lower.CL upper.CL L 38.01851 3.027239 38 31.89018 44.14684 M 26.51562 2.358621 38 21.74084 31.29040 H 21.50623 2.379736 38 16.68871 26.32376 Results are averaged over the levels of: wool Confidence level used: 0.95 > > detach("warp") > # won't work if detached > try(lsmeans(warp.lm3, "tension")) Error in eval(predvars, data, env) : object 'x1' not found Error in ref.grid(object = structure(list(coefficients = structure(c(39.9798298407202, : Perhaps a 'data' or 'params' argument is needed > > # However, we're OK again if we use 'data' > lsmeans(warp.lm3, "tension", data = warp) NOTE: Results may be misleading due to involvement in interactions tension lsmean SE df lower.CL upper.CL L 38.01851 3.027239 38 31.89018 44.14684 M 26.51562 2.358621 38 21.74084 31.29040 H 21.50623 2.379736 38 16.68871 26.32376 Results are averaged over the levels of: wool Confidence level used: 0.95 > > > # --- aovlist objects ---- > # dataset borrowed from pbkrtest > beets <- data.frame ( + harvest = factor(rep(rep(c("h1","h2"), each=5) , 3)), + block = factor(rep(rep(c("blk1","blk2","blk3"), each=5), 2)), + sow = factor(letters[c(3,4,5,2,1,3,2,4,5,1,5,2,3,4,1, + 2,1,5,4,3,4,1,3,2,5,1,4,3,2,5)]), + yield = c(128,118,95,131,136.5,136.5,150,140,99.5,156, + 99,128,126,120.5,137.5,147,153.5,100,139,141, + 115.5,135,130,134,91.5,155,140.5,142,151,102) ) > # Use true contrasts for coding... > old.opt <- options(contrasts = c("contr.helmert","contr.poly")) > beets.aov <- aov(yield ~ harvest*sow + Error(block/harvest), data=beets) > lsmeans(beets.aov, eff ~ sow | harvest) $lsmeans harvest = h1: sow lsmean SE df lower.CL upper.CL a 136.33333 1.313393 18.89 133.58327 139.08340 b 131.00000 1.313393 18.89 128.24993 133.75007 c 128.00000 1.313393 18.89 125.24993 130.75007 d 118.00000 1.313393 18.89 115.24993 120.75007 e 95.16667 1.313393 18.89 92.41660 97.91673 harvest = h2: sow lsmean SE df lower.CL upper.CL a 154.83333 1.313393 18.89 152.08327 157.58340 b 149.33333 1.313393 18.89 146.58327 152.08340 c 139.83333 1.313393 18.89 137.08327 142.58340 d 139.83333 1.313393 18.89 137.08327 142.58340 e 100.50000 1.313393 18.89 97.74993 103.25007 Confidence level used: 0.95 $contrasts harvest = h1: contrast estimate SE df t.ratio p.value a effect 14.633333 1.220428 16 11.990 <.0001 b effect 9.300000 1.220428 16 7.620 <.0001 c effect 6.300000 1.220428 16 5.162 0.0001 d effect -3.700000 1.220428 16 -3.032 0.0079 e effect -26.533333 1.220428 16 -21.741 <.0001 harvest = h2: contrast estimate SE df t.ratio p.value a effect 17.966667 1.220428 16 14.722 <.0001 b effect 12.466667 1.220428 16 10.215 <.0001 c effect 2.966667 1.220428 16 2.431 0.0272 d effect 2.966667 1.220428 16 2.431 0.0272 e effect -36.366667 1.220428 16 -29.798 <.0001 P value adjustment: fdr method for 5 tests > > # restore old 'contrasts' that confound the intercept > options(old.opt) > > > # --------------- Other stuff ------------------- > # using cld > cld(lsmeans(warp.lm2, ~ tension | wool)) wool = A: tension lsmean SE df lower.CL upper.CL .group H 23.53324 3.489132 38 16.46986 30.59662 1 M 23.74441 3.497424 38 16.66424 30.82457 1 L 48.35296 4.913079 38 38.40695 58.29897 2 wool = B: tension lsmean SE df lower.CL upper.CL .group H 19.47922 3.308686 38 12.78114 26.17731 1 L 27.68406 3.501649 38 20.59534 34.77278 1 M 29.28683 3.244426 38 22.71884 35.85483 1 Confidence level used: 0.95 P value adjustment: tukey method for comparing a family of 3 estimates significance level used: alpha = 0.05 > > # passing to glht > require(multcomp) Loading required package: multcomp Loading required package: mvtnorm Loading required package: survival Loading required package: TH.data Loading required package: MASS Attaching package: 'TH.data' The following object is masked from 'package:MASS': geyser > # This will fail because glht can't deal with rank deficiency > # Hope this changes. > try( as.glht(pairs(warp.lsm)) ) Note: df set to 37 Error in glht.matrix(model = structure(list(object = ), .Names = "object", class = c("lsmwrap", : 'ncol(linfct)' is not equal to 'length(coef(model))' In addition: Warning message: In glht.matrix(model = list(object = ), : 1 out of 10 coefficients not estimable in 'model' > > # However, warp.lm2 isn't rank-deficient > warp.lsm2 <- lsmeans(warp.lm2, ~ tension) NOTE: Results may be misleading due to involvement in interactions > warp.con <- contrast(warp.lsm2, "eff") > summary(warp.con, adjust = "sidak") contrast estimate SE df t.ratio p.value L effect 9.338389 2.332591 38 4.003 0.0008 M effect -2.164500 2.018477 38 -1.072 0.6426 H effect -7.173889 2.059398 38 -3.483 0.0038 Results are averaged over the levels of: wool P value adjustment: sidak method for 3 tests > summary(as.glht(warp.con)) Note: df set to 38 Simultaneous Tests for General Linear Hypotheses Linear Hypotheses: Estimate Std. Error t value Pr(>|t|) L effect == 0 9.338 2.333 4.003 < 0.001 *** M effect == 0 -2.165 2.018 -1.072 0.53536 H effect == 0 -7.174 2.059 -3.483 0.00357 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Adjusted p values reported -- single-step method) > > summary(glht(warp.lm2, lsm(eff ~ tension | wool))) Note: df set to 38 $`wool = A` Simultaneous Tests for General Linear Hypotheses Fit: lm(formula = breaks ~ x1 + x2 + wool * tension) Linear Hypotheses: Estimate Std. Error t value Pr(>|t|) L effect == 0 16.476 3.655 4.508 <0.001 *** M effect == 0 -8.132 3.023 -2.690 0.0274 * H effect == 0 -8.344 3.047 -2.738 0.0245 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Adjusted p values reported -- single-step method) $`wool = B` Simultaneous Tests for General Linear Hypotheses Fit: lm(formula = breaks ~ x1 + x2 + wool * tension) Linear Hypotheses: Estimate Std. Error t value Pr(>|t|) L effect == 0 2.201 2.790 0.789 0.7121 M effect == 0 3.803 2.665 1.427 0.3373 H effect == 0 -6.004 2.701 -2.223 0.0801 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Adjusted p values reported -- single-step method) > > # confint > confint(contrast(warp.lsm2, "trt.vs.ctrl1")) contrast estimate SE df lower.CL upper.CL M - L -11.50289 3.845692 38 -20.38431 -2.621472 H - L -16.51228 3.910237 38 -25.54276 -7.481796 Results are averaged over the levels of: wool Confidence level used: 0.95 Conf-level adjustment: dunnettx method for 2 estimates > > # lstrends > warp.lm4 <- lm(log(breaks) ~ tension*wool*x1, data = warp) > lstrends(warp.lm4, ~tension|wool, var = "x1") wool = A: tension x1.trend SE df lower.CL upper.CL L -0.20402014 0.10134674 34 -0.409981497 0.001941214 M 0.08236286 0.04306539 34 -0.005156549 0.169882263 H 0.04675497 0.03684713 34 -0.028127400 0.121637343 wool = B: tension x1.trend SE df lower.CL upper.CL L 0.06079456 0.05318559 34 -0.047291572 0.168880693 M 0.03676248 0.03214052 34 -0.028554928 0.102079881 H 0.01436618 0.02731486 34 -0.041144293 0.069876660 Trends are based on the log (transformed) scale Confidence level used: 0.95 > lstrends(warp.lm4, ~tension|wool, var = "x1", transform = "response") wool = A: tension x1.trend SE df lower.CL upper.CL L -11.0371815 5.4827056 34 -22.1793798 0.1050168 M 1.6835245 0.8802711 34 -0.1054016 3.4724507 H 0.9777406 0.7705476 34 -0.5882005 2.5436817 wool = B: tension x1.trend SE df lower.CL upper.CL L 1.6678054 1.4590651 34 -1.2973716 4.6329825 M 1.0345211 0.9044562 34 -0.8035551 2.8725972 H 0.2665878 0.5068716 34 -0.7634991 1.2966748 Trends are obtained after back-transforming from the log scale Confidence level used: 0.95 > > # exotic chain rule examples > lstrends(warp.lm4, ~tension|wool, var = "sqrt(x1-7)") wool = A: tension sqrt(x1-7).trend SE df lower.CL upper.CL L -1.32842099 0.6598914 34 -2.66948167 0.01263968 M 0.53628307 0.2804084 34 -0.03357545 1.10614160 H 0.30443213 0.2399199 34 -0.18314382 0.79200808 wool = B: tension sqrt(x1-7).trend SE df lower.CL upper.CL L 0.39584705 0.3463034 34 -0.30792605 1.09962015 M 0.23936875 0.2092742 34 -0.18592755 0.66466505 H 0.09354145 0.1778532 34 -0.26789974 0.45498264 Trends are based on the log (transformed) scale Confidence level used: 0.95 > lstrends(warp.lm4, ~tension|wool, var = "sqrt(x1-7)", transform = "response") wool = A: tension sqrt(x1-7).trend SE df lower.CL upper.CL L -71.865569 35.699128 34 -144.4149266 0.6837877 M 10.961807 5.731643 34 -0.6862935 22.6099068 H 6.366289 5.017209 34 -3.8299059 16.5624831 wool = B: tension sqrt(x1-7).trend SE df lower.CL upper.CL L 10.859456 9.500301 34 -8.4474784 30.1663901 M 6.735999 5.889118 34 -5.2321277 18.7041260 H 1.735813 3.300355 34 -4.9713144 8.4429412 Trends are obtained after back-transforming from the log scale Confidence level used: 0.95 > > > > # -------- Transformations ------------- > ## ... of response ... > warp.lm5 <- lm(log(breaks) ~ x1 + x2 + tension*wool, data = warp) > warp.lsm5 <- lsmeans(warp.lm5, ~tension | wool) > summary(warp.lsm5) wool = A: tension lsmean SE df lower.CL upper.CL L 3.813803 0.1826369 38 3.444074 4.183532 M 3.099011 0.1300119 38 2.835816 3.362206 H 3.056508 0.1297036 38 2.793937 3.319079 wool = B: tension lsmean SE df lower.CL upper.CL L 3.282384 0.1301689 38 3.018870 3.545897 M 3.332706 0.1206070 38 3.088550 3.576862 H 2.933453 0.1229958 38 2.684461 3.182445 Results are given on the log (not the response) scale. Confidence level used: 0.95 > summary(warp.lsm5, type = "resp") wool = A: tension response SE df lower.CL upper.CL L 45.32245 8.277551 38 31.31426 65.59711 M 22.17601 2.883144 38 17.04430 28.85278 H 21.25321 2.756618 38 16.34524 27.63489 wool = B: tension response SE df lower.CL upper.CL L 26.63919 3.467596 38 20.46816 34.67077 M 28.01405 3.378691 38 21.94524 35.76116 H 18.79240 2.311387 38 14.65030 24.10561 Confidence level used: 0.95 Intervals are back-transformed from the log scale > > ## In a GLM > # One of the glm examples... > d.AD <- data.frame(treatment = gl(3,3), outcome = gl(3,1,9), + counts = c(18,17,15,20,10,20,25,13,12)) > glm.D93 <- glm(counts ~ outcome + treatment, family = poisson(), data = d.AD) > > ( lsm.D93 <- lsmeans(glm.D93, ~ outcome) ) outcome lsmean SE df asymp.LCL asymp.UCL 1 3.044522 0.1259882 NA 2.797590 3.291455 2 2.590267 0.1581139 NA 2.280370 2.900165 3 2.751535 0.1458650 NA 2.465645 3.037425 Results are averaged over the levels of: treatment Results are given on the log (not the response) scale. Confidence level used: 0.95 > # un-log the results to obtain rates > summary(lsm.D93, type = "resp") outcome rate SE df asymp.LCL asymp.UCL 1 21.00000 2.645751 NA 16.405066 26.88194 2 13.33333 2.108185 NA 9.780295 18.17714 3 15.66667 2.285218 NA 11.771074 20.85149 Results are averaged over the levels of: treatment Confidence level used: 0.95 Intervals are back-transformed from the log scale > > # un-log some comparisons to obtain ratios > summary(contrast(lsm.D93, "trt.vs.ctrl", ref = 2), + type = "resp", adjust = "none") contrast rate.ratio SE df z.ratio p.value 1 - 2 1.575 0.3184189 NA 2.247 0.0246 3 - 2 1.175 0.2527659 NA 0.750 0.4535 Results are averaged over the levels of: treatment Tests are performed on the log scale > > > # weighting > nutr.lm <- lm(gain ~ (age + group + race)^2, data = nutrition) > lsmeans(nutr.lm, "race", weights = "equal") NOTE: Results may be misleading due to involvement in interactions race lsmean SE df lower.CL upper.CL Black 1.258929 1.646003 92 -2.0101755 4.528033 Hispanic NA NA NA NA NA White 2.932008 1.346612 92 0.2575192 5.606496 Results are averaged over the levels of: age, group Confidence level used: 0.95 > > lsmeans(nutr.lm, "race", weights = "prop") NOTE: Results may be misleading due to involvement in interactions race lsmean SE df lower.CL upper.CL Black 1.926554 1.3940313 92 -0.8421122 4.695221 Hispanic NA NA NA NA NA White 2.522821 0.6044604 92 1.3223102 3.723331 Results are averaged over the levels of: age, group Confidence level used: 0.95 > > lsmeans(nutr.lm, "race", weights = "outer") NOTE: Results may be misleading due to involvement in interactions race lsmean SE df lower.CL upper.CL Black 2.546674 1.4313624 92 -0.2961356 5.389483 Hispanic NA NA NA NA NA White 3.142940 0.7494154 92 1.6545363 4.631344 Results are averaged over the levels of: age, group Confidence level used: 0.95 > > lsmeans(nutr.lm, "race", weights = "cells") NOTE: Results may be misleading due to involvement in interactions race lsmean SE df lower.CL upper.CL Black 0.3809524 1.166180 92 -1.935181 2.697086 Hispanic 1.6666667 3.085422 92 -4.461247 7.794581 White 2.7951807 0.586592 92 1.630158 3.960203 Results are averaged over the levels of: age, group Confidence level used: 0.95 > > > # covariate predictions > feedlot.add <- lm(swt ~ ewt + herd + diet, data = feedlot) > lsmeans(feedlot.add, "herd", cov.reduce = ewt ~ herd) herd lsmean SE df lower.CL upper.CL 9 856.1375 26.66927 53 802.6457 909.6293 16 943.2625 34.05583 53 874.9551 1011.5698 3 993.4646 23.83657 53 945.6545 1041.2747 32 959.9984 20.47652 53 918.9277 1001.0691 24 984.5125 22.79846 53 938.7846 1030.2404 31 1105.4646 23.83657 53 1057.6545 1153.2747 19 1058.3821 22.24379 53 1013.7667 1102.9975 36 1126.9800 25.97394 53 1074.8829 1179.0771 34 1010.5553 21.88667 53 966.6562 1054.4544 35 1092.9718 20.58269 53 1051.6882 1134.2555 33 1072.0051 26.22250 53 1019.4094 1124.6007 Results are averaged over the levels of: diet Confidence level used: 0.95 > > > proc.time() user system elapsed 2.68 0.17 2.84 lsmeans/NAMESPACE0000644000175100001440000001021513013732335013150 0ustar hornikusers# Namespace for lsmeans package # Imports # From default packages (new req from CRAN, July 2015) importFrom("graphics", "pairs", "plot") importFrom("stats", "as.formula", "coef", "complete.cases", "confint", "cov", "cov2cor", "delete.response", "deriv", "family", "lm", "make.link", "model.frame", "model.matrix", "na.omit", "na.pass", "p.adjust", "p.adjust.methods", "pchisq", "pf", "poly", "predict", "pt", "ptukey", "qbeta", "qf", "qt", "qtukey", "reformulate", "terms", "uniroot", "update", "vcov") importFrom("utils", "head", "installed.packages", "str") importFrom("nlme", "fixef") # Others import(methods) import(mvtnorm) import(estimability) # 'requireNamespace' block below allows me to put multcomp in # Imports instead of Depends. if (requireNamespace("multcomp", quietly = TRUE)) { importFrom(multcomp, cld, glht, modelparm) export(cld) # need user to see it even if multcomp not loaded S3method(cld, ref.grid) S3method(cld, lsm.list) S3method(glht, lsmlf) S3method(glht, ref.grid) S3method(modelparm, lsmwrap) } if (requireNamespace("plyr", quietly = TRUE)) { importFrom(plyr, alply) } if (requireNamespace("coda", quietly = TRUE)) { importFrom(coda, as.mcmc) export(as.mcmc) S3method(as.mcmc, ref.grid) } if (requireNamespace("xtable", quietly = TRUE)) { importFrom(xtable, xtable, xtableList) } # Visible exports exportPattern("*.lsmc") #all contrast fcns export( as.glht, contrast, defaults.lsm, get.lsm.option, get.pmm.option, lsm, lsm.basis, lsmeans, lsmeans.character, lsmobj, lsmip, lsm.options, lstrends, make.tran, pmm, pmmeans, pmmobj, pmmip, pmm.options, pmtrends, ###rbind, # my own version that overrides one in methods recover.data, ref.grid, regrid, test ) # hidden functions of possible use to other package developers export( .all.vars, .aovlist.dffun, .diag, .get.offset, .my.vcov ) # S3 methods for recover.data and lsm.basis that are needed by other packages S3method(recover.data, call) S3method(recover.data, aovlist) S3method(lsm.basis, aovlist) S3method(recover.data, lm) S3method(lsm.basis, lm) S3method(recover.data, lme) S3method(lsm.basis, lme) S3method(recover.data, merMod) S3method(lsm.basis, merMod) S3method(lsm.basis, mlm) # recover.data for mlm uses lm # S3 methods - # For clarity, I'm showing them all # but commenting out the ones used only interally S3method("[", ref.grid) S3method(as.glht, ref.grid) S3method(as.glht, lsm.list) S3method(confint, ref.grid) S3method(confint, lsm.list) S3method(contrast, ref.grid) S3method(contrast, lsm.list) #S3method(glht, lsmlf) # in namespace block above #S3method(glht, ref.grid) S3method(lsmeans, character) S3method(lsmeans.character, default) S3method(lsmeans.character, ref.grid) S3method(lsmeans, default) S3method(lsmeans, formula) S3method(lsmeans, list) S3method(lsmip, default) #S3method(modelparm, lsmwrap) # in namespace block above S3method(pairs, ref.grid) S3method(pairs, lsm.list) S3method(plot, lsmobj) S3method(plot, summary.ref.grid) S3method(predict, ref.grid) S3method(print, ref.grid) S3method(print, summary.ref.grid) S3method(print, lsm.list) S3method(print, xtable.lsm) S3method(rbind, ref.grid) S3method(str, ref.grid) S3method(str, lsm.list) S3method(summary, ref.grid) S3method(summary, lsm.list) S3method(test, ref.grid) S3method(test, lsm.list) S3method(update, ref.grid) S3method(vcov, ref.grid) S3method(xtable, ref.grid) S3method(xtable, summary.ref.grid) # Support for my glht.list objects (cede to Torsten if he wants 'em in multcomp) # DOESN'T WORK: S3method(cld, glht.list) S3method(coef, glht.list) S3method(confint, glht.list) S3method(plot, glht.list) # NOT NEEDED: S3method(print, glht.list) S3method(summary, glht.list) S3method(vcov, glht.list) # interim support for old lsmeans function # removed at version 2.14 # export(.old.lsmeans) # S3method(print, lsm) # S3method(print, data.frame.lsm) lsmeans/data/0000755000175100001440000000000013013732335012643 5ustar hornikuserslsmeans/data/oranges.RData0000644000175100001440000000154513013732335015223 0ustar hornikusers r0b```b`f@& `d`aEy @.*`%1̌80vlԲQFC01L` S LbpK򋀬hA2 H  f)X*&[!"a[$1K,lG7[]P)] d\G(<61qA7D堶IYQ+v*/v0zͯA73{gOO!Ϧy䎼(y=AA"G)>i>f9e? Țk-z5.N{ ${U6ŭ񆃈{k Nlp> U)$\28(a\g]xA_5gDބ]=꜃BP䰃ߒxDɬ0tPUnG;{}B累+8l`wX8 /v_bd|wic57_99Ȕig=n -;*xi}lCN~pyr05T.A@ 퐺?PdS>s0y;1f;0(1\.`I3Bxǃv#3^RXJg]VyV/(,H(ȺT$957ְdfmWSzO쐶Z/v7IOa8JztN~s={ݪ5˸Z89RY}\p-clsmeans/data/nutrition.RData0000644000175100001440000000075213013732335015617 0ustar hornikusersVN@jD%C![1O.aAn~3U )!/ %eGy`RIٚ-ϧIvvO2Ԛ8Eg1-za\ gH2۸MdMuFM#UCu5}Ht=lmp5&&,{1!͟ȱ铹(_)GB[~NK)ɋܯ|01(YJ;Ce\PzQhC攎`vqz?b<]/> 4Nřp֍dWhdōўc2G;kY\a<ŗ '˟'t`v틎?hoA+:|/ՍWyFoK}!"Qy -0= n lsmeans/data/autonoise.RData0000644000175100001440000000052513013732335015570 0ustar hornikusersSMK@M- /Cs=x^d H*zg{:ծCxo&vnuR ҄A|u7fqj1C>qq5sv{m53gך=?.k9ُ5C9ѓJEvf.˖,y m>9R5 ײulGf.C.~HiCPOc|V}-"i7{?^|HISg9B Rl`v;lsmeans/data/fiber.RData0000644000175100001440000000041613013732335014650 0ustar hornikusersmRN0؅HG-!)S&uh4 2?аwbi3;}iM !Dw$Eyol87ss\VSW-z7íU?Y^*7GNhq Éo+5o3ƽ2O: a |l9鏄xIO_>yzu5*ߗ&:iMg+1Ԛ6ǯ?Wuݏ QQ` ا9ƻ{lsmeans/data/feedlot.RData0000644000175100001440000000115013013732335015177 0ustar hornikusersU=OA]vHd)M~ DRĭsLl'Ny RH)%E J))SH[nNv" Λy37JL)#PrK}$# RzZSix—)fs"dĖc"KG,Gz- OFsxleɪ eou4kc ':s #>N>fE>ؗcTFPO1ϸ?9ON:7y%םrxx^܇7 Ak?qﲛ؃. h>)>́!c>'wNE=ZxeK"_=^E6v5ZƕZk Vp6}]e<鮕"vLwHlsmeans/R/0000755000175100001440000000000013013732367012140 5ustar hornikuserslsmeans/R/xtable-method.R0000644000175100001440000001050313013732335015012 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### xtable method # Modified from xtableLSMeans function provided by David Scott xtable.ref.grid = function(x, caption = NULL, label = NULL, align = NULL, digits = 4, display = NULL, auto = FALSE, ...) { xtable.summary.ref.grid(summary(x, ...), caption = caption, label = label, align = align, digits = digits, display = display, auto = auto) } xtable.summary.ref.grid = function (x, caption = NULL, label = NULL, align = NULL, digits = 4, display = NULL, auto = FALSE, ...) { if (!is.null(x$df)) x$df = round(x$df, 2) if (!is.null(x$t.ratio)) x$t.ratio = round(x$t.ratio, 3) if (!is.null(x$z.ratio)) x$z.ratio = round(x$z.ratio, 3) if (!is.null(x$p.value)) { fp = x$p.value = format(round(x$p.value,4), nsmall=4, sci=FALSE) x$p.value[fp=="0.0000"] = "<.0001" } if (!is.null(byv <- attr(x, "by.vars"))) { byc = which(names(x) %in% byv) xList = split(as.data.frame(x), f = x[, byc]) labs = rep("", length(xList)) for (i in 1:length(xList)) { levs = sapply(xList[[i]][1, byc], as.character) labs[i] = paste(paste(byv, levs, sep = " = "), collapse = ", ") xList[[i]] = as.data.frame(xList[[i]][, -byc, drop = FALSE]) } attr(xList, "subheadings") = labs } else { xList = list(as.data.frame(x)) } attr(xList, "message") = attr(x, "mesg") result = xtable::xtableList(xList, caption = caption, label = label, align = align, digits = digits, display = display, auto = auto, ...) digits = xtable::digits(result[[1]]) # format df and t ratios digits = xtable::digits(result[[1]]) i = which(names(x) == "df") if (length(i) > 0) { dfd = ifelse(all(zapsmall(x$df - round(x$df)) == 0), 0, 2) digits[i + 1 - length(byv)] = ifelse(is.na(dfd), 0, dfd) } i = which(names(x) %in% c("t.ratio", "z.ratio")) if (length(i) > 0) digits[i + 1 - length(byv)] = 3 for (i in seq_along(result)) xtable::digits(result[[i]]) = digits class(result) = c("xtable.lsm", "xtableList") result } # My own print method print.xtable.lsm = function(x, type = getOption("xtable.type", "latex"), include.rownames = FALSE, sanitize.message.function = footnotesize, ...) { footnotesize = switch(type, html = function(x) paste0("", x, ""), latex = function(x) paste0("{\\footnotesize ", x, "}"), function(x) x ) invisible(xtable::print.xtableList(x, include.rownames = include.rownames, sanitize.message.function = sanitize.message.function, ...)) } lsmeans/R/aovlist-support.R0000644000175100001440000001414313013732335015454 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # lsmeans support for aovlist objects recover.data.aovlist = function(object, ...) { fcall = attr(object, "call") trms = terms(object) # Find the Error terms lbls = attr(trms, "term.labels") err.idx = grep("^Error\\(", lbls) newf = as.formula(paste(c(".~.", lbls[err.idx]), collapse = "-")) trms = terms(update(trms, newf)) recover.data(fcall, delete.response(trms), na.action = attr(object, "na.action"), ...) } # This works great for balanced experiments, and goes horribly wrong # even for slightly unbalanced ones. So I abort on these kinds of cases lsm.basis.aovlist = function (object, trms, xlev, grid, vcov., ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) contr = attr(object, "contrasts") X = model.matrix(trms, m, contrasts.arg = contr) xnms = dimnames(X)[[2]] # Check for situations we can't handle... colsums = apply(X[, setdiff(xnms, "(Intercept)"), drop=FALSE], 2, sum) if (any(round(colsums,3) != 0)) warning("Some predictors are correlated with the intercept - results are biased.\n", "May help to re-fit with different contrasts, e.g. 'contr.sum'") if (length(unlist(lapply(object, function(x) names(coef(x))))) > length(xnms)) message("NOTE: Results are based on intra-block estimates.") # initialize arrays nonint = setdiff(names(object), "(Intercept)") k = length(xnms) bhat = rep(NA, k) # I'll use NAs to track which slots I've filled V = matrix(0, nrow=k, ncol=k) names(bhat) = xnms dimnames(V) = list(xnms, xnms) empty.list = as.list(nonint) names(empty.list) = nonint Vmats = Vidx = Vdf = empty.list wts = matrix(0, nrow = length(nonint), ncol = k) dimnames(wts) = list(nonint, xnms) # NOTE: At present, I just do intra-block analysis: wts are all 0 and 1 btemp = bhat #++ temp for tracking indexes #++Work thru strata in reverse order for (nm in rev(nonint)) { x = object[[nm]] bi = coef(x) bi = bi[!is.na(bi)] ii = match(names(bi), xnms) Vidx[[nm]] = use = setdiff(ii, which(!is.na(bhat))) #++ omit elts already filled if(length(use) > 0) { ii.left = seq_along(ii)[!is.na(match(ii,use))] wts[nm, use] = 1 bhat[use] = bi[ii.left] Vi = vcov(x)[ii.left, ii.left, drop=FALSE] Vmats[[nm]] = Vi V[use,use] = Vi } else { Vmats[[nm]] = matrix(0, nrow=0, ncol=0) } # Any cases with 0 df will have NaN for covariances. I make df = -1 # in those cases so I don't divide by 0 later in Satterthwaite calcs Vdf[[nm]] = ifelse(x$df > 0, x$df, -1) } x <- object[["(Intercept)"]] if (!is.null(x)) { # The intercept belongs in the 1st error stratum # So have to add a row and column to its covariance matrix bhat[1] = x$coefficients[1] wts[1,1] = 1 Vidx[[1]] = ii = c(1, Vidx[[1]]) k = length(ii) vv = matrix(0, nrow=k, ncol=k) if (k > 1) vv[2:k,2:k] = Vmats[[1]] # Variance of intercept is EMS of this stratum divided by N # Here I'm assuming there are no weights N = sum(sapply(object, function(x) length(x$residuals))) V[1,1] = vv[1,1] = sum(object[[2]]$residuals^2) / object[[2]]$df / N #dimnames(vv) = list(c(xnms[ii], xnms[ii])) Vmats[[1]] = vv } # override V if vcov. is supplied if(!missing(vcov.)) { V = .my.vcov(object, vcov.) dfargs = list() dffun = function(k, dfargs) NA } else { dfargs = list(Vmats=Vmats, Vidx=Vidx, Vdf=unlist(Vdf), wts = wts) dffun = function(k, dfargs) { lsmeans::.aovlist.dffun(k, dfargs) } } nbasis = estimability::all.estble # Consider this further? misc = list() list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = dfargs, misc = misc) } .aovlist.dffun = function(k, dfargs) { if(is.matrix(k) && (nrow(k) > 1)) { dfs = apply(k, 1, .aovlist.dffun, dfargs) min(dfs) } else { v = sapply(seq_along(dfargs$Vdf), function(j) { ii = dfargs$Vidx[[j]] kk = (k * dfargs$wts[j, ])[ii] #sum(kk * .mat.times.vec(dfargs$Vmats[[j]], kk)) .qf.non0(dfargs$Vmats[[j]], kk) }) sum(v)^2 / sum(v^2 / dfargs$Vdf) # Good ole Satterthwaite } }lsmeans/R/transformations.R0000644000175100001440000002007413013732335015512 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # Code to implement transformations my way ### My modification/expansion of stats:make.link() ### Also, if not found, returns make.link("identity") modified with ## unknown = TRUE, name = link ## In addition, I make all links truly monotone on (-Inf, Inf) in ## lieu of valideta ## ## Extensions to make.link results: ## unknown: set to TRUE if link is unknown ## mult: scalar multiple of transformation ## .make.link = function(link) { if (link %in% c("logit", "probit", "cauchit", "cloglog", "identity", "log")) result = stats::make.link(link) else result = switch(link, sqrt = { tmp = make.link("sqrt") tmp$linkinv = function(eta) pmax(0, eta)^2 tmp$mu.eta = function(eta) 2*pmax(0, eta) tmp }, `1/mu^2` = { tmp = make.link("1/mu^2") tmp$linkinv = function(eta) 1/sqrt(pmax(0, eta)) tmp$mu.eta = function(eta) -1/(2*pmax(0, eta)^1.5) tmp }, inverse = { tmp = make.link("inverse") tmp$linkinv = function(eta) 1/pmax(0, eta) tmp$mu.eta = function(eta) -1/pmax(0, eta)^2 tmp }, `/` = .make.link("inverse"), reciprocal = .make.link("inverse"), log10 = list( linkinv = function(eta) 10^eta, mu.eta = function(eta) 10^eta * log(10), name = "log10" ), log2 = list( linkinv = function(eta) 2^eta, mu.eta = function(eta) 2^eta * log(2), name = "log2" ), asin.sqrt = make.tran("asin.sqrt"), `asin.sqrt./` = make.tran("asin.sqrt", 100), asinh.sqrt = list( linkinv = function(eta) sinh(eta)^2, mu.eta = function(eta) sinh(2 * eta), name = "asinh(sqrt(mu))" ), exp = list( linkinv = function(eta) log(eta), mu.eta = function(eta) 1/eta, name = "exp" ), `+.sqrt` = { tmp = .make.link("sqrt") tmp$mult = 2 tmp }, log.o.r. = { tmp = make.link("log") tmp$name = "log odds ratio" tmp }, { # default if not included, flags it as unknown tmp = stats::make.link("identity") tmp$unknown = TRUE tmp$name = link tmp } ) result } # Implementation of additional transformations, typically ones with parameters # Returns a list like stats::make.link, but often with an additional "param" member # types: # glog: log(mu + param) make.tran = function(type = c("genlog", "power", "boxcox", "sympower", "asin.sqrt"), param = 1) { type = match.arg(type) origin = 0 mu.lbl = "mu" if (length(param) > 1) { origin = param[2] param = param[1] mu.lbl = paste0("(mu - ", round(origin, 3), ")") } switch(type, genlog = { if((origin < 0) || (origin == 1)) stop('"genlog" transformation must have a positive base != 1') logbase = ifelse(origin == 0, 1, log(origin)) xlab = ifelse(origin == 0, "", paste0(" (base ", round(origin, 3), ")")) list(linkfun = function(mu) log(pmax(mu + param, 0)) / logbase, linkinv = function(eta) pmax(exp(logbase * eta), .Machine$double.eps) - param, mu.eta = function(eta) logbase * pmax(exp(logbase * eta), .Machine$double.eps), valideta = function(eta) TRUE, param = c(param, origin), name = paste0("log(mu + ", round(param,3), ")", xlab) ) }, power = { if (param == 0) { if(origin == 0) make.link("log") else make.tran("genlog", -origin) } else list( linkfun = function(mu) pmax(mu - origin, 0)^param, linkinv = function(eta) origin + pmax(eta, 0)^(1/param), mu.eta = function(eta) pmax(eta, 0)^(1/param - 1) / param, valideta = function(eta) all(eta > 0), param = c(param, origin), name = ifelse(param > 0, paste0(mu.lbl, "^", round(param,3)), paste0(mu.lbl, "^(", round(param,3), ")")) ) }, boxcox = { if (param == 0) { result = if(origin == 0) make.link("log") else make.tran("genlog", -origin) return (result) } min.eta = ifelse(param > 0, -1 / param, -Inf) xlab = ifelse(origin == 0, "", paste0(" with origin at ", round(origin, 3))) list( linkfun = function(mu) ((mu - origin)^param - 1) / param, linkinv = function(eta) origin + (1 + param * pmax(eta, min.eta))^(1/param), mu.eta = function(eta) (1 + param * pmax(eta, min.eta))^(1/param - 1), valideta = function(eta) all(eta > min.eta), param = c(param, origin), name = paste0("Box-Cox (lambda = ", round(param, 3), ")", xlab) ) }, sympower = { if (param <= 0) stop('"sympower" transformation requires positive param') if (origin == 0) mu.lbl = paste0("(", mu.lbl, ")") absmu.lbl = gsub("\\(|\\)", "|", mu.lbl) list(linkfun = function(mu) sign(mu - origin) * abs(mu - origin)^param, linkinv = function(eta) origin + sign(eta) * abs(eta)^(1/param), mu.eta = function(eta) (abs(eta))^(1/param - 1), valideta = function(eta) all(eta > min.eta), param = c(param, origin), name = paste0(absmu.lbl, "^", round(param,3), " * sign", mu.lbl) ) }, asin.sqrt = { mu.lbl = ifelse(param == 1, "mu", paste0("mu/", round(param,3))) list(linkfun = function(mu) asin(sqrt(mu/param)), linkinv = function(eta) param * sin(pmax(pmin(eta, pi/2), 0))^2, mu.eta = function(eta) param * sin(2*pmax(pmin(eta, pi/2), 0)), valideta = function(eta) all(eta <= pi/2) && all(eta >= 0), name = paste0("asin(sqrt(", mu.lbl, "))") ) } ) } lsmeans/R/ordinal-support.R0000644000175100001440000003573313013732335015433 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### support for the ordinal package recover.data.clm = function(object, mode = "latent", ...) { if (!is.na(pmatch(mode, "scale"))) { if (is.null(trms <- object$S.terms)) return("Specified mode=\"scale\", but no scale model is present") # ref.grid's error handler takes it from here recover.data(object$call, trms, object$na.action, ...) } else if (is.null(object$S.terms) && is.null(object$nom.terms)) recover.data.lm(object, ...) else { # bring-in predictors from loc, scale, and nom models trms = delete.response(object$terms) #preds = union(.all.vars(trms), union(.all.vars(object$S.terms), .all.vars(object$nom.terms))) x.preds = union(.all.vars(object$S.terms), .all.vars(object$nom.terms)) #x.trms = update(trms, reformulate(preds)) x.trms = terms(update(trms, reformulate(c(".", x.preds)))) recover.data(object$call, x.trms, object$na.action, ...) } } # For now at least, clmm doesn't cover scale, nominal options recover.data.clmm = recover.data.lm # Note: For ALL thresholds, object$Theta has all the threshold values # for the different cuts (same as object$alpha when threshold=="flexible") # and object$tJac is s.t. tJac %*% alpha = Theta # Note also that some functions of cut are constrained to be zero when # threshold != "flexible". Can get basis using nonest.basis(t(tJac)) # # opt arg 'mode' - determines what goes into ref.grid # 'rescale' - (loc, scale) for linear transformation of latent result lsm.basis.clm = function (object, trms, xlev, grid, mode = c("latent", "linear.predictor", "cum.prob", "exc.prob", "prob", "mean.class", "scale"), rescale = c(0,1), ...) { # general stuff mode = match.arg(mode) if (mode == "scale") return (.lsm.basis.clm.scale(object, trms, xlev, grid, ...)) if (is.null(object$contrasts)) warning("Contrasts used to fit the model are unknown.\n", "Defaulting to system option, but results may be wrong.") bhat = coef(object) V = .my.vcov(object, ...) tJac = object$tJac dffun = function(...) NA link = as.character(object$info$link) cnm = dimnames(object$tJac)[[1]] if (is.null(cnm)) cnm = paste(seq_len(nrow(tJac)), "|", 1 + seq_len(nrow(tJac)), sep = "") misc = list() # My strategy is to piece together the needed matrices for each threshold parameter # Then assemble the results ### ----- Location part ----- ### contrasts = object$contrasts # Remember trms was trumped-up to include scale and nominal predictors. # Recover the actual terms for the principal model trms = delete.response(object$terms) m = model.frame(trms, grid, na.action = na.pass, xlev = object$xlevels) X = model.matrix(trms, m, contrasts.arg = contrasts) xint = match("(Intercept)", colnames(X), nomatch = 0L) if (xint > 0L) { X = X[, -xint, drop = FALSE] } ### ----- Nominal part ----- ### if (is.null(object$nom.terms)) NOM = matrix(1, nrow = nrow(X)) else { mn = model.frame(object$nom.terms, grid, na.action = na.pass, xlev = object$nom.xlevels) NOM = model.matrix(object$nom.terms, mn, contrasts.arg = object$nom.contrasts) } bigNom = kronecker(tJac, NOM) # cols are in wrong order... I'll get the indexes by transposing a matrix of subscripts if (ncol(NOM) > 1) bigNom = bigNom[, as.numeric(t(matrix(seq_len(ncol(bigNom)), nrow=ncol(NOM))))] ### ----- Scale part ----- ### if (!is.null(object$S.terms)) { ms = model.frame(object$S.terms, grid, na.action = na.pass, xlev = object$S.xlevels) S = model.matrix(object$S.terms, ms, contrasts.arg = object$S.contrasts) S = S[, names(object$zeta), drop = FALSE] if (!is.null(attr(object$S.terms, "offset"))) { soff = .get.offset(object$S.terms, grid) # we'll add a column to S and adjust bhat and V accordingly S = cbind(S, offset = soff) bhat = c(bhat, offset = 1) V = rbind(cbind(V, offset = 0), offset = 0) } si = misc$scale.idx = length(object$alpha) + length(object$beta) + seq_len(ncol(S)) # Make sure there are no name clashes names(bhat)[si] = paste(".S", names(object$zeta), sep=".") misc$estHook = ".clm.estHook" misc$vcovHook = ".clm.vcovHook" } else S = NULL ### ----- Get non-estimability basis ----- ### nbasis = snbasis = estimability::all.estble if (any(is.na(bhat))) { #####mm = model.matrix(object) # workaround to fact that model.matrix doesn't get the contrasts right... mf = update(object, method = "model.frame")$mf mm = list(X = model.matrix(object$terms, data=mf, contrasts.arg = object$contrasts)) if (any(is.na(c(object$alpha, object$beta)))) { NOMX = mm$X if (is.null(mm$NOM)) NOMX = mm$X else { ##NOMX = cbind(mm$NOM, mm$X[, -1, drop=false]) mmNOM = model.matrix(object$nom.terms, data = mf, contrasts.arg = object$nom.contrasts) NOMX = cbind(mmNOM, mm$X[, -1]) } nbasis = estimability::nonest.basis(NOMX) # replicate and reverse the sign of the NOM parts nomcols = seq_len(ncol(NOM)) nbasis = apply(nbasis, 2, function(x) c(rep(-x[nomcols], each = length(object$alpha)), x[-nomcols])) } if (!is.null(mm$S)) { if (any(is.na(object$zeta))) { ####snbasis = nonest.basis(mm$S) mmS = model.matrix(object$S.terms, data = mf, contrasts.arg = object$S.contrasts) snbasis = estimability::nonest.basis(mmS) # put intercept part at end snbasis = rbind(snbasis[-1, , drop=FALSE], snbasis[1, ]) if (!is.null(attr(object$S.terms, "offset"))) snbasis = rbind(snbasis, 0) snbasis = rbind(matrix(0, ncol=ncol(snbasis), nrow=min(si)-1), snbasis) # Note scale intercept is included, so tack it on to the end of everything S = cbind(S, .S.intcpt = 1) bhat = c(bhat, .S.intcpt = 0) V = rbind(cbind(V, .S.intcpt = 0), .S.intcpt = 0) si = misc$scale.idx = c(si, 1 + max(si)) } } if (is.na(nbasis[1])) # then only nonest part is scale nbasis = snbasis else { if (!is.null(S)) # pad nbasis with zeros when there's a scale model nbasis = rbind(nbasis, matrix(0, nrow=length(si), ncol=ncol(nbasis))) if (!is.na(snbasis[1])) nbasis = cbind(nbasis, snbasis) } } if (mode == "latent") { # Create constant columns for means of scale and nominal parts J = matrix(1, nrow = nrow(X)) nomm = rescale[2] * apply(bigNom, 2, mean) X = rescale[2] * X if (!is.null(S)) { sm = apply(S, 2, mean) X = cbind(X, kronecker(-J, matrix(sm, nrow = 1))) } bigX = cbind(kronecker(-J, matrix(nomm, nrow = 1)), X) misc$offset.mult = misc$offset.mult * rescale[2] intcpt = seq_len(ncol(tJac)) bhat[intcpt] = bhat[intcpt] - rescale[1] / rescale[2] } else { ### ----- Piece together big matrix for each threshold ----- ### misc$ylevs = list(cut = cnm) misc$tran = link misc$inv.lbl = "cumprob" misc$offset.mult = -1 if (!is.null(S)) X = cbind(X, S) J = matrix(1, nrow=nrow(tJac)) bigX = cbind(bigNom, kronecker(-J, X)) if (mode != "linear.predictor") { misc$mode = mode misc$respName = as.character(object$terms)[2] misc$postGridHook = ".clm.postGrid" } } dimnames(bigX)[[2]] = names(bhat) list(X = bigX, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = list(), misc = misc) } # fuction called at end of ref.grid # I use this for polr as well .clm.postGrid = function(object) { mode = object@misc$mode object@misc$postGridHook = object@misc$mode = NULL object = regrid(object, TRUE) if(object@misc$estName == "exc.prob") { # Exceedance probs object@bhat = 1 - object@bhat object@misc$estName = "cum.prob" } if (mode == "prob") { object = .clm.prob.grid(object) } else if (mode == "mean.class") { object = .clm.mean.class(object) } else if (mode == "exc.prob") { object@bhat = 1 - object@bhat object@misc$estName = "exc.prob" } # (else mode == "cum.prob" and it's all OK) object@misc$respName = NULL object } # Make the linear-predictor ref.grid into one for class probabilities # This assumes that object has already been re-gridded and back-transformed .clm.prob.grid = function(object, thresh = "cut", newname = object@misc$respName) { byv = setdiff(names(object@levels), thresh) newrg = contrast(object, ".diff_cum", by = byv) class(newrg) = "ref.grid" misc = newrg@misc misc$infer = c(FALSE,FALSE) misc$estName = "prob" misc$pri.vars = misc$by.vars = NULL newrg@misc = misc names(newrg@levels)[1] = names(newrg@grid)[1] = newname newrg@roles = object@roles newrg@roles$multresp = newname newrg } .clm.mean.class = function(object) { prg = .clm.prob.grid(object, newname = "class") byv = setdiff(names(prg@levels), "class") lf = as.numeric(prg@levels$class) newrg = contrast(prg, list(mean = lf), by = byv) newrg = update(newrg, infer = c(FALSE, FALSE), pri.vars = NULL, by.vars = NULL, estName = "mean.class") newrg@levels$contrast = newrg@grid$contrast = NULL prg@roles$multresp = NULL newrg@roles = prg@roles class(newrg) = "ref.grid" newrg } # Contrast fcn for turning estimates of cumulative probabilities # into cell probabilities .diff_cum.lsmc = function(levs, sep = "|", ...) { plevs = unique(setdiff(unlist(strsplit(levs, sep, TRUE)), sep)) k = 1 + length(levs) if (length(plevs) != k) plevs = seq_len(k) M = matrix(0, nrow = length(levs), ncol = k) for (i in seq_along(levs)) M[i, c(i,i+1)] = c(1,-1) dimnames(M) = list(levs, plevs) M = as.data.frame(M) attr(M, "desc") = "Differences of cumulative probabilities" attr(M, "adjust") = "none" attr(M, "offset") = c(rep(0, k-1), 1) M } #### replacement estimation routines for cases with a scale param ## workhorse for estHook and vcovHook functions .clm.hook = function(object, tol = 1e-8) { scols = object@misc$scale.idx bhat = object@bhat active = !is.na(bhat) bhat[!active] = 0 linfct = object@linfct estble = estimability::is.estble(linfct, object@nbasis, tol) ###apply(linfct, 1, .is.estble, object@nbasis, tol) estble[!estble] = NA rsigma = estble * as.numeric(linfct[, scols, drop = FALSE] %*% object@bhat[scols]) rsigma = exp(rsigma) * estble # I'll do the scaling later eta = as.numeric(linfct[, -scols, drop = FALSE] %*% bhat[-scols]) if (!is.null(object@grid$.offset.)) eta = eta + object@grid$.offset. for (j in scols) linfct[, j] = eta * linfct[, j] linfct = (.diag(rsigma) %*% linfct) [, active, drop = FALSE] list(est = eta * rsigma, V = linfct %*% tcrossprod(object@V, linfct)) } .clm.estHook = function(object, do.se = TRUE, tol = 1e-8, ...) { raw.matl = .clm.hook(object, tol) SE = if (do.se) sqrt(diag(raw.matl$V)) else NA cbind(est = raw.matl$est, SE = SE, df = NA) } .clm.vcovHook = function(object, tol = 1e-8, ...) { .clm.hook(object, tol)$V } ### Special lsm.basis fcn for the scale model .lsm.basis.clm.scale = function(object, trms, xlev, grid, ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$S.contrasts) bhat = c(`(intercept)` = 0, object$zeta) nbasis = estimability::all.estble if (any(is.na(bhat))) { mf = update(object, method = "model.frame")$mf S = model.matrix(trms, mf, contrasts.arg = object$S.contrasts) nbasis = estimability::nonest.basis(S) } k = sum(!is.na(bhat)) - 1 V = .my.vcov(object, ...) pick = nrow(V) - k + seq_len(k) V = V[pick, pick, drop = FALSE] V = cbind(0, rbind(0,V)) misc = list(tran = "log") list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = function(...) NA, dfargs = list(), misc = misc) } lsm.basis.clmm = function (object, trms, xlev, grid, ...) { if(is.null(object$Hessian)) { message("Updating the model to obtain the Hessian...") object = update(object, Hess = TRUE) } # borrowed from Maxime's code -- need to understand this better, e.g. when it happens H = object$Hessian if (any(apply(object$Hessian, 1, function(x) all(x == 0)))) { H = H[names(coef(object)), names(coef(object))] object$Hessian = H } result = lsm.basis.clm(object, trms, xlev, grid, ...) # strip off covariances of random effects keep = seq_along(result$bhat) result$V = result$V[keep,keep] result } lsmeans/R/helpers.R0000644000175100001440000007445713013732335013741 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### Helper functions for lsmeans ### Here we have 'recover.data' and 'lsm.basis' methods ### For models that this package supports. #---------------------------------------------------------- ### Recover data methods will return a data.frame with ### the original data, and at least these additional attrs: # attr(, "terms") - terms component of object # attr(, "responses") - names of response variable # attr(, "predictors") - names of predictors #----------------------------------------------------------- # generic version recover.data = function(object, ...) UseMethod("recover.data") #---------------------------------------------------------- ### lsm.basis methods create a basis for the reference grid # # Required args: # object - the model object # trms - terms component of object # xlev - named list of factor levels (but not the coerced ones) # grid - reference grid # All methods must return a list with these elements: # X - basis for linear fcns over reference grid # bhat - regression coefficients for fixed effects (INCLUDING any NAs) # nbasis - matrix whose columns for a basis for non-estimable functions of beta; all.estble if none # V - estimated covariance matrix of bhat # dffun - function(k, dfargs) to find df for k'bhat having std error se # dfargs - additional arguments, if any, for dffun # misc - Extra info ... # -- if extra levels need to be added (e.g. mlm, polr), # put them in misc$ylevs # -- For transformations or link fcns, use misc$tran # for name (see 'make.link'), and use misc$inv.lbl # for label to use in 'summary' when tran is inverted # (ref.grid looks at lhs of model for tran if none found) # Note: if no df exists, set dffun = function(...) NA and dfargs = list() #-------------------------------------------------------------- # generic version lsm.basis = function(object, trms, xlev, grid, ...) UseMethod("lsm.basis") #-------------------------------------------------------------- ### DEFAULT METHODS (we hit these when a model is NOT supported) # I'll have it return the message if we caught the error in this way # Then caller can use try() to check for other types of errors, # and just print this message otherwise recover.data.default = function(object, ...) { paste("Can't handle an object of class ", dQuote(class(object)[1]), "\n", paste(.show_supported(), collapse="")) } lsm.basis.default = function(object, trms, xlev, grid, ...) { stop("Can't handle an object of class", dQuote(class(object)[1]), "\n", .show_supported()) } # Private fcn to get a list of supported objects # does this by looking in namespace [ns] and methods [meth] # then strips that off leaving extensions .show_supported = function(ns = "lsmeans", meth = "lsm.basis") { "Use help(\"models\", package = \"lsmeans\") for information on supported models." } #-------------------------------------------------------------- ### call' objects # This recover.data method serves as the workhorse for the others # For model objects, call this with the object's call and its terms component # Late addition: if data is non-null, use it in place of recovered data # Later addition: na.action arg req'd - vector of row indexes removed due to NAs # na.action is ignored when data is non-NULL recover.data.call = function(object, trms, na.action, data = NULL, params = NULL, ...) { fcall = object # because I'm easily confused vars = setdiff(.all.vars(trms), params) tbl = data if (length(vars) == 0) { tbl = data.frame(c(1,1)) vars = names(tbl) = 1 } if (is.null(tbl)) { m = match(c("formula", "data", "subset", "weights"), names(fcall), 0L) fcall = fcall[c(1L, m)] fcall$drop.unused.levels = TRUE fcall[[1L]] = as.name("model.frame") fcall$xlev = NULL # we'll ignore xlev # If we have an explicit list of cases to exclude, let everything through now if (!is.null(na.action)) fcall$na.action = na.pass else # exclude incomplete cases fcall$na.action = na.omit form = reformulate(vars) fcall$formula = update(trms, form) env = environment(trms) if (is.null(env)) env = parent.frame() tbl = eval(fcall, env, parent.frame()) # Now we can drop na.action's rows if (!is.null(na.action)) tbl = tbl[-(na.action), , drop=FALSE] } else { tbl = tbl[, vars, drop = FALSE] # consider only the variables actually needed tbl = tbl[complete.cases(tbl), , drop=FALSE] } attr(tbl, "call") = object # the original call attr(tbl, "terms") = trms attr(tbl, "predictors") = setdiff(.all.vars(delete.response(trms)), params) attr(tbl, "responses") = setdiff(vars, union(attr(tbl, "predictors"), params)) tbl } #-------------------------------------------------------------- ### lm objects (and also aov, rlm, others that inherit) -- but NOT aovList recover.data.lm = function(object, ...) { fcall = object$call recover.data(fcall, delete.response(terms(object)), object$na.action, ...) } lsm.basis.lm = function(object, trms, xlev, grid, ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) # coef() works right for lm but coef.aov tosses out NAs bhat = as.numeric(object$coefficients) # stretches it out if multivariate - see mlm method V = .my.vcov(object, ...) if (sum(is.na(bhat)) > 0) nbasis = estimability::nonest.basis(object$qr) else nbasis = estimability::all.estble misc = list() if (inherits(object, "glm")) { misc = .std.link.labels(object$family, misc) dffun = function(k, dfargs) NA dfargs = list() } else { dfargs = list(df = object$df.residual) dffun = function(k, dfargs) dfargs$df } list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=misc) } #-------------------------------------------------------------- ### mlm objects # (recover.data.lm works just fine) lsm.basis.mlm = function(object, trms, xlev, grid, ...) { class(object) = c("mlm", "lm") # avoids error in vcov for "maov" objects bas = lsm.basis.lm(object, trms, xlev, grid, ...) bhat = coef(object) k = ncol(bhat) bas$X = kronecker(diag(rep(1,k)), bas$X) bas$nbasis = kronecker(rep(1,k), bas$nbasis) ylevs = dimnames(bhat)[[2]] if (is.null(ylevs)) ylevs = seq_len(k) bas$misc$ylevs = list(rep.meas = ylevs) bas } #-------------------------------------------------------------- ### merMod objects (lme4 package) recover.data.merMod = function(object, ...) { if(!lme4::isLMM(object) && !lme4::isGLMM(object)) return("Can't handle a nonlinear mixed model") fcall = object@call recover.data(fcall, delete.response(terms(object)), attr(object@frame, "na.action"), ...) } lsm.basis.merMod = function(object, trms, xlev, grid, vcov., mode = get.lsm.option("lmer.df"), ...) { if (missing(vcov.)) V = as.matrix(vcov(object, correlation = FALSE)) else V = as.matrix(.my.vcov(object, vcov.)) dfargs = misc = list() if (lme4::isLMM(object)) { mode = match.arg(tolower(mode), c("satterthwaite", "kenward-roger", "asymptotic")) if (mode == "satterthwaite") { if (requireNamespace("lmerTest")) { dfargs = list(object = object) dffun = function(k, dfargs) lmerTest::calcSatterth(dfargs$object, k)$denom } else { message("Install package 'lmerTest' to obtain Satterthwaite degrees of freedom") mode = "asymptotic" } } else if (mode == "kenward-roger") { pbdis = .lsm.is.true("disable.pbkrtest") Nlim = get.lsm.option("pbkrtest.limit") objN = lme4::getME(object, "N") toobig = objN > Nlim if (!pbdis && !toobig && requireNamespace("pbkrtest") && missing(vcov.)) { dfargs = list(unadjV = V, adjV = pbkrtest::vcovAdj.lmerMod(object, 0)) V = as.matrix(dfargs$adjV) tst = try(pbkrtest::Lb_ddf) if(class(tst) != "try-error") dffun = function(k, dfargs) pbkrtest::Lb_ddf (k, dfargs$unadjV, dfargs$adjV) else { mode = "asymptotic" warning("To obtain d.f., install 'pbkrtest' version 0.4-1 or later") } } else { if(!pbdis && !("pbkrtest" %in% row.names(installed.packages()))) message("Install package 'pbkrtest' to obtain bias corrections and degrees of freedom") else if(toobig) message("Note: Adjusted covariance and degrees-of-freedom calculations have been\n", "disabled because the number of observations exceeds ", Nlim, ".\n", "Standard errors and tests may be more biased than if they were adjusted.\n", "To enable adjustments, set lsm.options(pbkrtest.limit = ", objN, ") or larger,\n", "but be warned that this may result in large computation time and memory use.") mode = "asymptotic" } } if (mode == "asymptotic") { dffun = function(k, dfargs) NA } misc$initMesg = paste("Degrees-of-freedom method:", mode) } else if (lme4::isGLMM(object)) { dffun = function(k, dfargs) NA misc = .std.link.labels(family(object), misc) } else stop("Can't handle a nonlinear mixed model") contrasts = attr(object@pp$X, "contrasts") m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = contrasts) bhat = lme4::fixef(object) if (length(bhat) < ncol(X)) { # Newer versions of lmer can handle rank deficiency, but we need to do a couple of # backflips to put the pieces together right, # First, figure out which columns were retained kept = match(names(bhat), dimnames(X)[[2]]) # Now re-do bhat with NAs in the right places bhat = NA * X[1, ] bhat[kept] = lme4::fixef(object) # we have to reconstruct the model matrix modmat = model.matrix(trms, object@frame, contrasts.arg=contrasts) nbasis = estimability::nonest.basis(modmat) } else nbasis=estimability::all.estble list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=misc) } #-------------------------------------------------------------- ### mer objects (from old lme4 version, now lme4.0) ### re-enabled; CRAN check now seems to work with multiple add'l repositories recover.data.mer = function(object, ...) { if(!lme4.0::isLMM(object) && !lme4.0::isGLMM(object)) return("Can't handle a nonlinear mixed model") fcall = object@call recover.data(fcall, delete.response(terms(object)), attr(object@frame, "na.action"), ...) } # Does NOT support pbkrtest capabilities. Uses asymptotic methods lsm.basis.mer = function(object, trms, xlev, grid, ...) { V = as.matrix(.my.vcov(object, ...)) dfargs = misc = list() if (lme4.0::isLMM(object)) { dffun = function(k, dfargs) NA } else if (lme4.0::isGLMM(object)) { dffun = function(k, dfargs) NA # need to work harder as there is no 'family' method cfam = object@call$family if (is.name(cfam)) fam = eval(cfam)() else fam = eval(cfam) misc = .std.link.labels(fam, misc) } else stop("Can't handle a nonlinear mixed model") contrasts = attr(object@X, "contrasts") m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = contrasts) bhat = lme4.0::fixef(object) nbasis=estimability::all.estble list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=misc) } #-------------------------------------------------------------- ### lme objects (nlme package) recover.data.lme = function(object, data, ...) { fcall = object$call if (!is.null(fcall$weights)) fcall$weights = nlme::varWeights(object$modelStruct) if(is.null(data)) { # lme objects actually have the data, so use it! data = object$data if (!is.null(fcall$subset)) data = data[eval(fcall$subset, envir=), , drop = FALSE] if (!is.null(object$na.action)) data = data[-object$na.action, , drop = FALSE] } recover.data(fcall, delete.response(terms(object)), numeric(0), data = data, ...) } lsm.basis.lme = function(object, trms, xlev, grid, sigmaAdjust = TRUE, ...) { contrasts = object$contrasts m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = contrasts) bhat = nlme::fixef(object) V = .my.vcov(object, ...) if (sigmaAdjust && object$method == "ML") V = V * object$dims$N / (object$dims$N - nrow(V)) misc = list() if (!is.null(object$family)) { misc = .std.link.labels(object$family, misc) } nbasis = estimability::all.estble # Replaced by containment method##dffun = function(...) NA dfx = object$fixDF$X if (names(bhat[1]) == "(Intercept)") dfx[1] = length(levels(object$groups[[1]])) - 1#min(dfx) ### Correct apparent error in lme containment algorithm dffun = function(x, dfargs) { idx = which(abs(x) > 1e-4) ifelse(length(idx) > 0, min(dfargs$dfx[idx]), NA) } list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = list(dfx = dfx), misc = misc) } #-------------------------------------------------------------- ### gls objects (nlme package) recover.data.gls = function(object, ...) { fcall = object$call if (!is.null(fcall$weights)) fcall$weights = nlme::varWeights(object$modelStruct) recover.data(fcall, delete.response(nlme::getCovariateFormula(object)), object$na.action, ...) } lsm.basis.gls = function(object, trms, xlev, grid, ...) { contrasts = object$contrasts m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = contrasts) bhat = coef(object) V = .my.vcov(object, ...) nbasis = estimability::all.estble dfargs = list(df = object$dims$N - object$dims$p) dffun = function(k, dfargs) dfargs$df list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=list()) } #-------------------------------------------------------------- ### polr objects (MASS package) recover.data.polr = recover.data.lm lsm.basis.polr = function(object, trms, xlev, grid, mode = c("latent", "linear.predictor", "cum.prob", "prob", "mean.class"), rescale = c(0,1), ...) { mode = match.arg(mode) contrasts = object$contrasts m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = contrasts) # Strip out the intercept (borrowed code from predict.polr) xint = match("(Intercept)", colnames(X), nomatch = 0L) if (xint > 0L) X = X[, -xint, drop = FALSE] bhat = c(coef(object), object$zeta) V = .my.vcov(object, ...) k = length(object$zeta) if (mode == "latent") { X = rescale[2] * cbind(X, matrix(- 1/k, nrow = nrow(X), ncol = k)) bhat = c(coef(object), object$zeta - rescale[1] / rescale[2]) misc = list(offset.mult = rescale[2]) } else { j = matrix(1, nrow=k, ncol=1) J = matrix(1, nrow=nrow(X), ncol=1) X = cbind(kronecker(-j, X), kronecker(diag(1,k), J)) link = object$method if (link == "logistic") link = "logit" misc = list(ylevs = list(cut = names(object$zeta)), tran = link, inv.lbl = "cumprob", offset.mult = -1) if (mode != "linear.predictor") { # just use the machinery we already have for the 'ordinal' package misc$mode = mode misc$postGridHook = ".clm.postGrid" } } misc$respName = as.character(terms(object))[2] nbasis = estimability::all.estble dffun = function(...) NA list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=list(), misc=misc) } #-------------------------------------------------------------- ### survreg objects (survival package) recover.data.survreg = function(object, ...) { fcall = object$call trms = delete.response(terms(object)) # I'm gonna delete any terms involving strata(), cluster(), or frailty() mod.elts = dimnames(attr(trms, "factor"))[[2]] tmp = grep("strata\\(|cluster\\(|frailty\\(", mod.elts) if (length(tmp)) trms = trms[-tmp] recover.data(fcall, trms, object$na.action, ...) } # Seems to work right in a little testing. # However, it fails sometimes if I update the model # with a subset argument. Workaround: just fitting a new model lsm.basis.survreg = function(object, trms, xlev, grid, ...) { # Much of this code is adapted from predict.survreg bhat = object$coefficients k = length(bhat) V = .my.vcov(object, ...)[seq_len(k), seq_len(k), drop=FALSE] # ??? not used... is.fixeds = (k == ncol(object$var)) m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) # X = model.matrix(object, m) # This is what predict.survreg does # But I have manipulated trms, so need to make sure things are consistent X = model.matrix(trms, m, contrasts.arg = object$contrasts) nbasis = estimability::nonest.basis(model.matrix(object)) dfargs = list(df = object$df.residual) dffun = function(k, dfargs) dfargs$df if (object$dist %in% c("exponential","weibull","loglogistic","loggaussian","lognormal")) misc = list(tran = "log", inv.lbl = "response") else misc = list() list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=misc) } #-------------------------------------------------------------- ### coxph objects (survival package) recover.data.coxph = function(object, ...) recover.data.survreg(object, ...) lsm.basis.coxph = function (object, trms, xlev, grid, ...) { object$dist = "doesn't matter" result = lsm.basis.survreg(object, trms, xlev, grid, ...) result$dfargs$df = NA result$X = result$X[, -1, drop = FALSE] result$X = result$X - rep(object$means, each = nrow(result$X)) result$misc$tran = "log" result$misc$inv.lbl = "hazard" result } # Note: Very brief experimentation suggests coxph.penal also works. # This is an extension of coxph #-------------------------------------------------------------- ### coxme objects #### ### Greatly revised 6-15-15 (after version 2.18) recover.data.coxme = function(object, ...) recover.data.survreg(object, ...) lsm.basis.coxme = function(object, trms, xlev, grid, ...) { bhat = fixef(object) k = length(bhat) V = .my.vcov(object, ...)[seq_len(k), seq_len(k), drop = FALSE] m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m) X = X[, -1, drop = FALSE] # remove the intercept # scale the linear predictor for (j in seq_along(X[1, ])) X[, j] = (X[, j] - object$means[j]) ### / object$scale[j] nbasis = estimability::all.estble dffun = function(k, dfargs) NA misc = list(tran = "log", inv.lbl = "hazard") list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = list(), misc = misc) } ### special vcov prototype for cases where there are several vcov options ### e.g., gee, geeglm, geese .named.vcov = function(object, method, ...) UseMethod(".named.vcov") # default has optional idx of same length as valid and if so, idx indicating # which elt of valid to use if matched # Ex: valid = c("mammal", "fish", "rat", "dog", "trout", "perch") # idx = c( 1, 2, 1, 1, 2, 2) # -- so ultimately results can only be "mammal" or "fish" # nonmatches revert to 1st elt. .named.vcov.default = function(object, method, valid, idx = seq_along(valid), ...) { if (!is.character(method)) { # in case vcov. arg was matched by vcov.method { V = .my.vcov(object, method) method = "user-supplied" } else { i = pmatch(method, valid, 1) method = valid[idx[i]] V = object[[method]] } attr(V, "methMesg") = paste("Covariance estimate used:", method) V } # general-purpose lsm.basis function .lsmb.geeGP = function(object, trms, xlev, grid, vcov.method, valid, idx = seq_along(valid), ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) bhat = coef(object) V = .named.vcov(object, vcov.method, valid, idx, ...) if (sum(is.na(bhat)) > 0) nbasis = estimability::nonest.basis(object$qr) else nbasis = estimability::all.estble misc = .std.link.labels(object$family, list()) misc$initMesg = attr(V, "methMesg") dffun = function(k, dfargs) NA dfargs = list() list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=misc) } #--------------------------------------------------------------- ### gee objects #### recover.data.gee = recover.data.lm lsm.basis.gee = function(object, trms, xlev, grid, vcov.method = "robust.variance", ...) .lsmb.geeGP(object, trms, xlev, grid, vcov.method, valid = c("robust.variance", "naive.variance")) ### geepack objects #### recover.data.geeglm = recover.data.lm lsm.basis.geeglm = function(object, trms, xlev, grid, vcov.method = "vbeta", ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) bhat = coef(object) V = .named.vcov(object$geese, vcov.method, valid = c("vbeta", "vbeta.naiv","vbeta.j1s","vbeta.fij","robust","naive"), idx = c(1,2,3,4,1,2)) if (sum(is.na(bhat)) > 0) nbasis = estimability::nonest.basis(object$qr) else nbasis = estimability::all.estble misc = .std.link.labels(object$family, list()) misc$initMesg = attr(V, "methMesg") dffun = function(k, dfargs) NA dfargs = list() list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=misc) } recover.data.geese = function(object, ...) { fcall = object$call # what a pain - we need to reconstruct the terms component args = as.list(fcall[-1]) na.action = object$na.action #trms = terms.formula(fcall$formula) if (!is.null(args$data)) { data = eval(args$data, parent.frame()) trms = terms(model.frame(fcall$formula, data = data)) } else { trms = terms(model.frame(fcall$formula)) } recover.data(fcall, delete.response(trms), na.action, ...) } lsm.basis.geese = function(object, trms, xlev, grid, vcov.method = "vbeta", ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) bhat = object$beta V = .named.vcov(object, vcov.method, valid = c("vbeta", "vbeta.naiv","vbeta.j1s","vbeta.fij","robust","naive"), idx = c(1,2,3,4,1,2)) # We don't have the qr component - I'm gonna punt for now if (sum(is.na(bhat)) > 0) warning("There are non-estimable functions, but estimability is NOT being checked") # nbasis = estimability::nonest.basis(object$qr) # else nbasis = estimability::all.estble misc = list() if (!is.null(fam <- object$call$family)) misc = .std.link.labels(eval(fam)(), misc) misc$initMesg = attr(V, "methMesg") dffun = function(k, dfargs) NA dfargs = list() list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=misc) } #-------------------------------------------------------------- ### afex package - mixed objects ### # just need to provide an 'lsmeans' method here, assuming Henrik adds the 'data' item ### These are deprecated as of afex 0.14 - now afex has its own lsmeans support # recover.data.mixed = function(object, ...) { # recover.data.merMod(object$full.model, ...) # } # # lsm.basis.mixed = function(object, trms, xlev, grid, ...) { # lsm.basis.merMod(object$full.model, trms, xlev, grid, ...) # } #-------------------------------------------------------------- ### glmmADMB package recover.data.glmmadmb = recover.data.lm lsm.basis.glmmadmb = function (object, trms, xlev, grid, ...) { contrasts = object$contrasts m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = contrasts) bhat = glmmADMB::fixef(object) V = .my.vcov(object, ...) misc = list() if (!is.null(object$family)) { fam = object$family misc$tran = object$link misc$inv.lbl = "response" if (!is.na(pmatch(fam,"binomial"))) misc$inv.lbl = "prob" else if (!is.na(pmatch(fam,"poisson"))) misc$inv.lbl = "rate" } nbasis = estimability::all.estble dffun = function(...) NA list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = list(), misc = misc) } # -------------------------------------------------------------- ### Explicit non-support for 'gam' objects (runs, but results are wrong) lsm.basis.gam = function(object, trms, xlev, grid, ...) { stop("Can't handle an object of class", dQuote(class(object)[1]), "\n", .show_supported()) } #-------------------------------------------------------------- ### mgcv package -------------------------------- # gam - OK - inherits from glm # gamm # recover.data.gamm = function(object, ...) { # fcall = object$lme$call # recover.data(fcall, delete.response(terms(object$lme)), object$lme$na.action, ...) # } # # lsm.basis.gamm = function (object, trms, xlev, grid, sigmaAdjust = TRUE, ...) { # lsm.basis(object$lme, trms, xlev, grid, sigmaAdjust, ...) # # Doesn't work because needs the matrices in object$lme$data # } ### ----- Auxiliary routines ------------------------- # Provide for vcov. argument in ref.grid call, which could be a function or a matrix .my.vcov = function(object, vcov. = stats::vcov, ...) { if (is.function(vcov.)) vcov. = vcov.(object) else if (!is.matrix(vcov.)) stop("vcov. must be a function or a square matrix") vcov. } # Call this to do the standard stuff with link labels # Returns a modified misc .std.link.labels = function(fam, misc) { if (is.null(fam)) return(misc) misc$tran = fam$link misc$inv.lbl = "response" if (length(grep("binomial", fam$family)) == 1) misc$inv.lbl = "prob" else if (length(grep("poisson", fam$family)) == 1) misc$inv.lbl = "rate" misc } ## Alternative to all.vars, but keeps vars like foo$x and foo[[1]] as-is ## Passes ... to all.vars .all.vars = function(expr, retain = c("\\$", "\\[\\[", "\\]\\]"), ...) { if (!inherits(expr, "formula")) { expr = try(eval(expr), silent = TRUE) if(inherits(expr, "try-error")) { return(character(0)) } } repl = paste("_Av", seq_along(retain), "_", sep = "") for (i in seq_along(retain)) expr = gsub(retain[i], repl[i], expr) subs = switch(length(expr), 1, c(1,2), c(2,1,3)) vars = all.vars(as.formula(paste(expr[subs], collapse = "")), ...) retain = gsub("\\\\", "", retain) for (i in seq_along(retain)) vars = gsub(repl[i], retain[i], vars) if(length(vars) == 0) vars = "1" # no vars ---> intercept vars } ### Not-so-damn-smart replacement of diag() that will ### not be so quick to assume I want an identity matrix ### returns matrix(x) when x is a scalar .diag = function(x, nrow, ncol) { if(is.matrix(x)) diag(x) else if((length(x) == 1) && missing(nrow) && missing(ncol)) matrix(x) else diag(x, nrow, ncol) } lsmeans/R/lsmip.R0000644000175100001440000001254613013732335013412 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # lsmip code - interaction plots lsmip = function(object, formula, ...) UseMethod("lsmip") # object - a model object supported by lsmeans # formula - a formula of the form x.factors ~ trace.factors | panel.factors lsmip.default = function(object, formula, type, pch=c(1,2,6,7,9,10,15:20), lty=1, col=NULL, plotit = TRUE, ...) { if (!requireNamespace("lattice")) stop("This function requires the 'lattice' package be installed.") if (length(formula) < 3) formula = reformulate(as.character(formula)[[2]], response = ".single.") ###stop("'formula' must be two-sided, e.g. trace.factor ~ x.factor") ### NEW: Allow lhs to be empty, so then we get a single trace # Glean the parts of ... to use in lsmeans call # arguments allowed to be passed lsa.allowed = c("at","trend","cov.reduce","fac.reduce") xargs = list(...) lsmopts = list(...) for (arg in names(xargs)) { idx = pmatch(arg, lsa.allowed) if (!is.na(idx)) { opt = lsa.allowed[idx] lsmopts[[opt]] = xargs[[arg]] xargs[[arg]] = NULL } } allvars = setdiff(.all.vars(formula), ".single.") lsmopts$object = object lsmopts$specs = reformulate(allvars) lsmo = do.call("lsmeans", lsmopts) if(missing(type)) { type = get.lsm.option("summary")$predict.type if (is.null(type)) type = .get.predict.type(lsmo@misc) } type = .validate.type(type) lsm = predict(lsmo, type = type) lsms = cbind(lsmo@grid, lsmean = lsm) # Set up trace vars and key tvars = .all.vars(update(formula, . ~ 1)) if (all(tvars == ".single.")) { lsms$.single. = 1 my.key = function(tvars) list() } else { my.key = function(tvars) list(space="right", title = paste(tvars, collapse=" * "), points = TRUE, lines=length(lty) > 1, cex.title=1) } tv = do.call(paste, lsms[tvars]) lsms$tvar = factor(tv, levels=unique(tv)) # figure out 'x' and 'by' vars rhs = strsplit(as.character(formula[3]), "\\|")[[1]] xvars = .all.vars(reformulate(rhs[[1]])) xv = do.call(paste, lsms[xvars]) lsms$xvar = factor(xv, levels = unique(xv)) lsms = lsms[order(lsms$xvar), ] plotform = lsmean ~ xvar # see if we have any 'by' vars if (length(rhs) > 1) { byvars = .all.vars(reformulate(rhs[[2]])) plotform = as.formula(paste("lsmean ~ xvar |", paste(byvars, collapse="*"))) } # The strips the way I want them my.strip = lattice::strip.custom(strip.names = c(TRUE,TRUE), strip.levels = c(TRUE,TRUE), sep = " = ") TP = TP.orig = lattice::trellis.par.get() TP$superpose.symbol$pch = pch TP$superpose.line$lty = lty if (!is.null(col)) TP$superpose.symbol$col = TP$superpose.line$col = col lattice::trellis.par.set(TP) xlab = ifelse(is.null(xargs$xlab), paste("Levels of", paste(xvars, collapse=" * ")), xargs$xlab) rspLbl = paste("Predicted", ifelse(is.null(lsmo@misc$inv.lbl), "response", lsmo@misc$inv.lbl)) ylab = ifelse(is.null(xargs$ylab), ifelse(type == "response", rspLbl, "Linear prediction"), xargs$ylab) # remove the unneeded stuff from xlabs xargs = xargs[setdiff(names(xargs), c("xlab","ylab"))] plotspecs = list(x = plotform, data = lsms, groups = ~ tvar, xlab = xlab, ylab = ylab, strip = my.strip, auto.key = my.key(tvars), type=c("p","l")) grobj = do.call(lattice::xyplot, c(plotspecs, xargs)) if (plotit) print(grobj) attr(lsms, "lattice") = grobj lattice::trellis.par.set(TP.orig) invisible(lsms) } lsmeans/R/glht-support.R0000644000175100001440000001342213013732335014730 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### Code for an enhancement of 'glht' in 'multcomp' package ### Provides for using 'lsm' in similar way to 'mcp' ### This is implemented via the class "lsmlf" -- linear functions for lsmeans ### (also oddly reminiscent of an old Lucky Strike commercial, LSMFT) # lsm(specs) will be used as 'linfct' argument in glht # all we need to do is class it and save the arguments lsm <- function(...) { result <- list(...) class(result) <- "lsmlf" result } # New S3 method for lsmlf objects glht.lsmlf <- function(model, linfct, ...) { # Pass the arguments we should pass to ref.grid: args = linfct args[[1]] = model names(args)[1] = "object" # Now pass the ref.grid to lsmeans: linfct$object <- do.call("ref.grid", args) lsmo <- do.call("lsmeans", linfct) if (is.list(lsmo)) lsmo = lsmo[[length(lsmo)]] # Then call the method for lsmobj glht(model, lsmo, ...) } # S3 method for an lsmobj or ref.grid # Note: model is redundant, really, so can be omitted glht.ref.grid <- function(model, linfct, by, ...) { if (!requireNamespace("multcomp")) stop(sQuote("glht")," requires ", dQuote("multcomp"), " to be installed") object = linfct # so I don't get confused if (missing(model)) model = .cls.list("lsmwrap", object = object) args = list(model = model, ...) # add a df value if not supplied if (is.null(args$df)) { df = summary(linfct)$df if(any(!is.na(df))) { args$df = max(1, as.integer(mean(df, na.rm=TRUE) + .25)) message("Note: df set to ", args$df) } } if (missing(by)) by = object@misc$by.vars nms = setdiff(names(object@grid), c(by, ".offset.", ".freq.", ".wgt.")) if (is.null(object@misc$estHook)) lf = object@linfct else # custom estimation setup - use the grid itself as the parameterization lf = diag(1, nrow(object@linfct)) dimnames(lf)[[1]] = as.character(interaction(object@grid[, nms], sep=", ")) if (is.null(by)) { args$linfct = lf return(do.call("glht", args)) } # (else...) by.rows = .find.by.rows(object@grid, by) result = lapply(by.rows, function(r) { args$linfct = lf[r, , drop=FALSE] do.call("glht", args) }) bylevs = lapply(by, function(byv) unique(object@grid[[byv]])) names(bylevs) = by bygrid = do.call("expand.grid", bylevs) levlbls = lapply(by, function(byv) paste(byv, "=", bygrid[[byv]])) levlbls$sep = ", " names(result) = do.call("paste", levlbls) class(result) = c("glht.list", "list") result } ### as. glht -- convert my object to glht object as.glht <- function(object, ...) UseMethod("as.glht") as.glht.default <- function(object, ...) stop("Cannot convert an object of class ", sQuote(class(object)[1]), " to a ", sQuote("glht"), " object") as.glht.ref.grid <- function(object, ...) glht( , object, ...) as.glht.lsm.list <- function(object, ..., which = 1) as.glht(object[[which]], ...) # S3 modelparm method for lsmwrap (S3 wrapper for an lsmobj - see glht.lsmobj) modelparm.lsmwrap <- function(model, coef., vcov., df, ...) { object = model$object if (is.null(object@misc$estHook)) { bhat = object@bhat V = object@V } else { # Have custom vcov and est methods. Use the grid itself as parameterization bhat = predict(object) V = vcov(object) } if(missing(df) || is.na(df)) df = 0 .cls.list("modelparm", coef = bhat, vcov = V, df = df, estimable = !is.na(bhat)) # This is NOT what we mean by 'estimable', but it is what glht wants... } # S3 methods for glht.list ### Doesn't work so excluded... # cld.glht.list = function(object, ...) # lapply(object, cld, ...) coef.glht.list = function(object, ...) lapply(object, coef, ...) confint.glht.list = function(object, ...) lapply(object, confint, ...) plot.glht.list = function(x, ...) lapply(x, plot, ...) summary.glht.list = function(object, ...) lapply(object, summary, ...) vcov.glht.list = function(object, ...) lapply(object, vcov, ...) lsmeans/R/countreg-support.R0000644000175100001440000002666413013732335015634 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # Support for zeroinfl and hurdle models (pscl [& future countreg package?]) # We'll support two optional arguments: # mode -- type of result required # lin.pred -- TRUE: keep linear predictor and link # FALSE: back-transform (default) # # With lin.pred = FALSE and mode %in% c("response", "count", "zero"), we # will return comparable results to predict(..., type = mode) # with mode = "prob0", same results as predict(..., type = "prob")[, 1] # # lin.pred only affects results for mode %in% c("count", "zero"). # When lin.pred = TRUE, we get the actual linear predictor and link function # for that part of the model. # ----- zeroinfl objects ----- recover.data.zeroinfl = function(object, mode = c("response", "count", "zero", "prob0"), ...) { fcall = object$call mode = match.arg(mode) if (mode %in% c("count", "zero")) trms = delete.response(terms(object, model = mode)) else ### mode = %in% c("response", "prob0") trms = delete.response(object$terms$full) # Make sure there's an offset function available env = new.env(parent = attr(trms, ".Environment")) env$offset = function(x) x attr(trms, ".Environment") = env recover.data(fcall, trms, object$na.action, ...) } lsm.basis.zeroinfl = function(object, trms, xlev, grid, mode = c("response", "count", "zero", "prob0"), lin.pred = FALSE, ...) { mode = match.arg(mode) m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) if (mode %in% c("count", "zero")) { contr = object$contrasts[[mode]] X = model.matrix(trms, m, contrasts.arg = contr) bhat = coef(object, model = mode) V = .pscl.vcov(object, model = mode, ...) if (mode == "count") misc = list(tran = "log", inv.lbl = "count") else misc = list(tran = object$link, inv.lbl = "prob") if (!lin.pred) { # back-transform the results lp = as.numeric(X %*% bhat + .get.offset(trms, grid)) lnk = make.link(misc$tran) bhat = lnk$linkinv(lp) delta = .diag(lnk$mu.eta(lp)) %*% X V = delta %*% tcrossprod(V, delta) X = diag(1, length(bhat)) misc = list(offset.mult = 0) } } else { ## "response", "prob0" trms1 = delete.response(terms(object, model = "count")) off1 = .get.offset(trms1, grid) contr1 = object$contrasts[["count"]] X1 = model.matrix(trms1, m, contrasts.arg = contr1) b1 = coef(object, model = "count") lp1 = as.numeric(X1 %*% b1 + off1) mu1 = exp(lp1) trms2 = delete.response(terms(object, model = "zero")) off2 = .get.offset(trms2, grid) contr2 = object$contrasts[["zero"]] X2 = model.matrix(trms2, m, contrasts.arg = contr2) b2 = coef(object, model = "zero") lp2 = as.numeric(X2 %*% b2) + off2 mu2 = object$linkinv(lp2) mu2prime = stats::make.link(object$link)$mu.eta(lp2) if(mode == "response") { delta = .diag(mu1) %*% cbind(.diag(1 - mu2) %*% X1, .diag(-mu2prime) %*% X2) bhat = (1 - mu2) * mu1 } else { # mode = "prob0" p0 = 1 - .prob.gt.0(object$dist, mu1, object$theta) dp0 = - .dprob.gt.0(object$dist, mu1, object$theta, "log", lp1) bhat = (1 - mu2) * p0 + mu2 delta = cbind(.diag((1 - mu2) * dp0) %*% X1, .diag(mu2prime * (1 - p0)) %*% X2) } V = delta %*% tcrossprod(.pscl.vcov(object, model = "full", ...), delta) X = diag(1, length(bhat)) misc = list(offset.mult = 0) } nbasis = estimability::all.estble dffun = function(k, dfargs) NA dfargs = list() list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = dfargs, misc = misc) } #### Support for hurdle models recover.data.hurdle = function(object, mode = c("response", "count", "zero", "prob0"), ...) { fcall = object$call mode = match.arg(mode) if (mode %in% c("count", "zero")) trms = delete.response(terms(object, model = mode)) else ### mode = "mean" or "prob.ratio" trms = delete.response(object$terms$full) # Make sure there's an offset function available env = new.env(parent = attr(trms, ".Environment")) env$offset = function(x) x attr(trms, ".Environment") = env recover.data(fcall, trms, object$na.action, ...) } # see expl notes afterward for notations in some of this lsm.basis.hurdle = function(object, trms, xlev, grid, mode = c("response", "count", "zero", "prob0"), lin.pred = FALSE, ...) { mode = match.arg(mode) m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) if ((lin.pred && mode %in% c("count", "zero")) || (!lin.pred && mode %in% c("count", "prob0"))) { model = ifelse(mode == "count", "count", "zero") contr = object$contrasts[[model]] X = model.matrix(trms, m, contrasts.arg = contr) bhat = coef(object, model = model) V = .pscl.vcov(object, model = model, ...) misc = switch(object$dist[[model]], binomial = list(tran = object$link, inv.lbl = "prob"), list(tran = "log", inv.lbl = "count")) if (!lin.pred) { # back-transform lp = as.numeric(X %*% bhat + .get.offset(trms, grid)) lnk = make.link(misc$tran) bhat = lnk$linkinv(lp) if (mode != "prob0") { delta = .diag(lnk$mu.eta(lp)) %*% X } else { bhat = 1 - .prob.gt.0(object$dist$zero, bhat, object$theta["zero"]) db = - .dprob.gt.0(object$dist$zero, bhat, object$theta["zero"], misc$tran, lp) delta = .diag(db) %*% X } V = delta %*% tcrossprod(V, delta) X = diag(1, length(bhat)) misc = list(offset.mult = 0) } } else { ### "zero" or "response" with implied lin.pred = FALSE trms1 = delete.response(terms(object, model = "count")) off1 = .get.offset(trms1, grid) contr1 = object$contrasts[["count"]] X1 = model.matrix(trms1, m, contrasts.arg = contr1) b1 = coef(object, model = "count") mu1 = as.numeric(exp(X1 %*% b1 + off1)) theta1 = object$theta["count"] p1 = .prob.gt.0(object$dist$count, mu1, theta1) dp1 = .dprob.gt.0(object$dist$count, mu1, theta1, "", 0) # binomial won't happen trms2 = delete.response(terms(object, model = "zero")) off2 = .get.offset(trms2, grid) contr2 = object$contrasts[["zero"]] X2 = model.matrix(trms2, m, contrasts.arg = contr2) b2 = coef(object, model = "zero") lp2 = as.numeric(X2 %*% b2 + off2) mu2 = switch(object$dist$zero, binomial = object$linkinv(lp2), exp(lp2) ) theta2 = object$theta["zero"] p2 = .prob.gt.0(object$dist$zero, mu2, theta2) dp2 = .dprob.gt.0(object$dist$zero, mu2, theta2, object$link, lp2) if (mode == "response") { bhat = p2 * mu1 / p1 delta = cbind(.diag(bhat*(1 - mu1 * dp1 / p1)) %*% X1, .diag(mu1 * dp2 / p1) %*% X2) } else { ## mode == "zero" bhat = p2 / p1 delta = cbind(.diag(-p2 * dp1 / p1^2) %*% X1, .diag(dp2 / p1) %*% X2) } V = delta %*% tcrossprod(.pscl.vcov(object, model = "full", ...), delta) X = .diag(1, length(bhat)) misc = list(estName = mode, offset.mult = 0) } nbasis = estimability::all.estble dffun = function(k, dfargs) object$df.residual dfargs = list() list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = dfargs, misc = misc) } # utility for prob (Y > 0 | dist, mu, theta) .prob.gt.0 = function(dist, mu, theta) { switch(dist, binomial = mu, poisson = 1 - exp(-mu), negbin = 1 - (theta / (mu + theta))^theta, geometric = 1 - 1 / (1 + mu) ) } # utility for d/d(eta) prob (Y > 0 | dist, mu, theta) .dprob.gt.0 = function(dist, mu, theta, link, lp) { switch(dist, binomial = stats::make.link(link)$mu.eta(lp), poisson = mu * exp(-mu), negbin = mu * (theta /(mu + theta))^(1 + theta), geometric = mu / (1 + mu)^2 ) } # special version of .my.vcov that accepts (and requires!) model argument .pscl.vcov = function(object, model, vcov. = stats::vcov, ...) { if (is.function(vcov.)) vcov. = vcov.(object, model = model) else if (!is.matrix(vcov.)) stop("vcov. must be a function or a square matrix") vcov. } # Explanatory notes for hurdle models # ----------------------------------- # We have a linear predictor eta = X%*%beta + offset # mu = h(eta) where h is inverse link (usually exp but not always) # Define p = P(Y > 0 | mu). This comes out to... # binomial: mu # poisson: 1 - exp(-mu) # negbin: 1 - (theta/(mu+theta))^theta # geometric: 1 - 1/(mu+1) # Define dp = dp/d(eta). Note - when h(mu)=exp(mu) we have dp = mu*dp/d(mu) # binomial: h'(eta) # poisson: mu*exp(-mu) # negbin: mu*(theta/(mu+theta))^(theta+1) # geometric: mu/(mu+1)^2 # # This gives us what we need to find the estimates and apply the delta method # In the code we index these notations with 1 (count model) and 2 (zero model) # And we treat theta1 and theta2 as constants # #!!! In theory, above seems correct, and estimates match those from predict.hurdle. #!!! But SEs don't seem right. #!!! They do seem right though if I omit the factor of mu in dp #!!! when link is log lsmeans/R/cld.lsm.R0000644000175100001440000001216513013732335013617 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # Runs the function multicompLetters from the multcompView package # returns an error if not installed .mcletters = function(..., Letters=c("1234567890",LETTERS,letters), reversed=FALSE) { if(!requireNamespace("multcompView", quietly = TRUE)) { message("The 'multcompView' package must be installed to use cld methods") return (list(monospacedLetters = "?")) } # Expand strings to individual letters Letters = as.character(unlist(sapply(Letters, function(stg) { sapply(seq_len(nchar(stg)), function(i) substr(stg, i, i)) }))) result = multcompView::multcompLetters(..., Letters=Letters, reversed=reversed) if (is.null(result$monospacedLetters)) result$monospacedLetters = result$Letters result } # S3 method for ref.grid cld.ref.grid = function(object, details=FALSE, sort=TRUE, by, alpha=.05, Letters = c("1234567890",LETTERS,letters), reversed=FALSE, ...) { lsmtbl = summary(object, ...) if(missing(by)) by = object@misc$by.vars if (sort) { args = list() for (nm in by) args[[nm]] = lsmtbl[[nm]] args$.lsm. = lsmtbl[[attr(lsmtbl, "estName")]] ord = do.call("order", args) lsmtbl = lsmtbl[ord, ] object@grid = object@grid[ord, , drop=FALSE] object@linfct = object@linfct[ord, , drop = FALSE] } attr(lsmtbl, "by.vars") = by object@misc$by.vars = by prwise = contrast(object, "revpairwise", by=by) pwtbl = test(prwise, ...) p.boo = (pwtbl$p.value < alpha) if(is.null(by)) { by.rows = list(seq_len(nrow(pwtbl))) by.out = list(seq_len(nrow(lsmtbl))) } else { by.rows = .find.by.rows(pwtbl, by) by.out = .find.by.rows(lsmtbl, by) } # Create comps matrix reflecting order generated by pairwise.lsmc icol = jcol = numeric(0) # create fake row indexes in revpairwise order for use by .mcletters k = length(by.out[[1]]) for (i in 2:k) { icol = c(icol, seq_len(i-1)) jcol = c(jcol, rep(i, i-1)) } na.p = which(is.na(p.boo)) # Take care of non-est cases. This is surprisingly complicated, # because it's possible we have some lsmeans that are non-est # but comparisons are est'ble. So cases to exclude must be missing in # the table of means, AND appar somewhere in the indexes of NA p values # All that said, it still messes up because I didn't track the indexes correctly # excl.rows = intersect(which(is.na(lsmtbl$SE)), union(icol[na.p], jcol[na.p])) # So I'll just go with which est's are missing excl.rows = which(is.na(lsmtbl$SE)) p.boo[na.p] = FALSE labs = paste(icol,jcol,sep="-") ltrs = rep("", nrow(lsmtbl)) for (i in seq_len(length(by.rows))) { pb = p.boo[by.rows[[i]]] names(pb) = labs mcl = .mcletters(pb, Letters = Letters, reversed = reversed)$monospacedLetters ltrs[by.out[[i]]] = paste(" ", mcl, sep="") } # any missing estimates get blanks... ltrs[excl.rows] = "" lsmtbl[[".group"]] = ltrs if(sort && reversed) for (i in seq_len(length(by.out))) { r = by.out[[i]] lsmtbl[r, ] = lsmtbl[rev(r), ] } attr(lsmtbl, "mesg") = c(attr(lsmtbl,"mesg"), attr(pwtbl, "mesg"), paste("significance level used: alpha =", alpha)) if (details) list(lsmeans = lsmtbl, comparisons = pwtbl) else lsmtbl } lsmeans/R/pmmeans.R0000644000175100001440000000521313013732335013717 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### Support for overriding "ls" with "pm" in names ### general-purpose wrapper for creating pmxxxxx functions .pmwrap = function(lsfcn, ...) { result = lsfcn(...) if (inherits(result, "ref.grid")) result = .sub.ls.pm(result) else if(inherits(result, "lsm.list")) { for (i in seq_along(result)) result[[i]] = .sub.ls.pm(result[[i]]) names(result) = gsub("^ls", "pm", names(result)) } result } # returns an updated ref.grid or lsmobj with setName "ls..." replaced by "pm..." .sub.ls.pm = function(object) { nm = object@misc$estName update(object, estName = gsub("^ls", "pm", nm)) } ### Exported implementations pmmeans = function(...) .pmwrap(lsmeans, ...) # uh, maybe not... pmms = pmmeans pmtrends = function(...) .pmwrap(lstrends, ...) pmmip = function(...) lsmip(...) pmm = function(...) lsm(...) pmmobj = function(...) .pmwrap(lsmobj, ...) pmm.options = function(...) lsm.options(...) get.pmm.option = function(...) get.lsm.option(...) lsmeans/R/plot.lsm.R0000644000175100001440000002763213013732335014040 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # S3 plot method for lsmobj objects (NOT ref.grid as relies on pri.fac attribute etc.) # ... are arguments sent to update() plot.lsmobj = function(x, y, type, intervals = TRUE, comparisons = FALSE, alpha = .05, adjust = "tukey", int.adjust = "none", ...) { if(!missing(type)) object = update(x, predict.type = type, ..., silent = TRUE) else object = update(x, ..., silent = TRUE) if (missing(int.adjust)) { int.adjust = object@misc$adjust if (is.null(int.adjust)) int.adjust = "none" } summ = summary(object, infer = c(TRUE, FALSE), adjust = int.adjust) estName = attr(summ, "estName") extra = NULL if(comparisons) { extra = object extra@misc$comp.alpha = alpha extra@misc$comp.adjust = adjust } .plot.srg(x=summ, intervals = intervals, extra = extra, ...) } # May use in place of plot.lsmobj but no control over level etc. # extra is a placeholder for comparison-interval stuff plot.summary.ref.grid = function(x, y, horizontal = TRUE, xlab, ylab, layout, ...) { .plot.srg (x, y, horizontal, xlab, ylab, layout, ...) } # Workhorse for plot.summary.ref.grid .plot.srg = function(x, y, horizontal = TRUE, xlab, ylab, layout, intervals = TRUE, extra = NULL, ...) { if (!requireNamespace("lattice")) stop("This function requires the 'lattice' package be installed.") summ = x # so I don't get confused estName = attr(summ, "estName") clNames = attr(summ, "clNames") if (is.null(clNames)) { warning("No information available to display confidence limits") lcl = ucl = summ[[estName]] } else { lcl = summ[[clNames[1]]] ucl = summ[[clNames[2]]] } # Panel functions... prepanel.ci = function(x, y, horizontal=TRUE, intervals=TRUE, lcl, ucl, subscripts, ...) { x = as.numeric(x) lcl = as.numeric(lcl[subscripts]) ucl = as.numeric(ucl[subscripts]) if (!intervals) # no special scaling needed list() else if (horizontal) list(xlim = range(x, ucl, lcl, finite = TRUE)) else list(ylim = range(y, ucl, lcl, finite = TRUE)) } panel.ci <- function(x, y, horizontal=TRUE, intervals=TRUE, lcl, ucl, lcmpl, rcmpl, subscripts, pch = 16, lty = dot.line$lty, lwd = dot.line$lwd, col = dot.symbol$col, col.line = dot.line$col, ...) { dot.line <- lattice::trellis.par.get("dot.line") dot.symbol <- lattice::trellis.par.get("dot.symbol") x = as.numeric(x) y = as.numeric(y) lcl = as.numeric(lcl[subscripts]) ucl = as.numeric(ucl[subscripts]) compare = !is.null(lcmpl) if(compare) { lcmpl = as.numeric(lcmpl[subscripts]) rcmpl = as.numeric(rcmpl[subscripts]) } if(horizontal) { lattice::panel.abline(h = unique(y), col = col.line, lty = lty, lwd = lwd) if(intervals) lattice::panel.arrows(lcl, y, ucl, y, col = col, length = .6, unit = "char", angle = 90, code = 3) if(compare) { s = (x > min(x)) lattice::panel.arrows(lcmpl[s], y[s], x[s], y[s], length = .5, unit = "char", code = 1, col = "red", type = "closed", fill="red") s = (x < max(x)) lattice::panel.arrows(rcmpl[s], y[s], x[s], y[s], length = .5, unit = "char", code = 1, col = "red", type = "closed", fill="red") } } else { lattice::panel.abline(v = unique(x), col = col.line, lty = lty, lwd = lwd) if(intervals) lattice::panel.arrows(x, lcl, x, ucl, col=col, length = .6, unit = "char", angle = 90, code = 3) if(compare) { s = (y > min(y)) lattice::panel.arrows(x[s], lcmpl[s], x[s], y[s], length = .5, unit = "char", code = 1, col = "red", type = "closed", fill="red") s = (y < max(y)) lattice::panel.arrows(x[s], rcmpl[s], x[s], y[s], length = .5, unit = "char", code = 1, col = "red", type = "closed", fill="red") } } lattice::panel.xyplot(x, y, pch=16, ...) } my.strip = lattice::strip.custom(strip.names = c(TRUE,TRUE), strip.levels = c(TRUE,TRUE), sep = " = ") priv = attr(summ, "pri.vars") pf = do.call(paste, summ[priv]) summ$pri.fac = factor(pf, levels=unique(pf)) chform = ifelse(horizontal, paste("pri.fac ~", estName), paste(estName, "~ pri.fac")) byv = attr(summ, "by.vars") if (!is.null(byv)) { chform = paste(chform, "|", paste(byv, collapse="*")) lbv = do.call("paste", summ[byv]) # strings for matching by variables ubv = unique(lbv) } else { lbv = rep(1, nrow(summ)) ubv = 1 } # Obtain comparison limits if (!is.null(extra)) { # we need to work on the linear predictor scale # typeid = 1 -> response, 2 -> other typeid = pmatch(extra@misc$predict.type, "response", nomatch = 2) if(length(typeid) < 1) typeid = 2 if (typeid == 1) est = predict(extra, type = "lp") else est = summ[[estName]] alpha = extra@misc$comp.alpha adjust = extra@misc$comp.adjust psumm = confint(pairs(extra), level = 1 - alpha, type = "lp", adjust = adjust) k = ncol(psumm) del = (psumm[[k]] - psumm[[k-1]]) / 4 # half the halfwidth, on lp scale diff = psumm[[attr(psumm, "estName")]] overlap = apply(psumm[ ,(k-1):k], 1, function(x) 2*min(-x[1],x[2])/(x[2]-x[1])) # figure out by variables and indexes (lbv, ubv already defined) if(is.null(byv)) pbv = rep(1, nrow(psumm)) else pbv = do.call("paste", psumm[byv]) neach = length(lbv) / length(ubv) # indexes for pairs results -- est[id1] - est[id2] id1 = rep(seq_len(neach-1), rev(seq_len(neach-1))) id2 = unlist(sapply(seq_len(neach-1), function(x) x + seq_len(neach-x))) # list of psumm row numbers involved in each summ row involved = lapply(seq_len(neach), function(x) union(which(id2==x), which(id1==x))) # initialize arrays mind = numeric(length(lbv)) # for minima of del llen = rlen = numeric(neach) # for left and right arrow lengths npairs = length(id1) iden = diag(rep(1, 2*neach)) for (by in ubv) { d = del[pbv == by] rows = which(lbv == by) for(i in seq_len(neach)) mind[rows[i]] = min(d[involved[[i]]]) # Set up regression equations to match arrow overlaps with interval overlaps # We'll add rows later (with weights 1) to match with mind values lmat = rmat = matrix(0, nrow = npairs, ncol = neach) y = numeric(npairs) v1 = 1 - overlap[pbv == by] dif = diff[pbv == by] for (i in seq_len(npairs)) { #wgt = 6 * max(0, ifelse(v1[i] < 1, v1[i], 2-v1[i])) wgt = 3 + 20 * max(0, .5 - (1 - v1[i])^2) # really this is sqrt of weight if (dif[i] > 0) # id2 <-----> id1 lmat[i, id1[i]] = rmat[i, id2[i]] = wgt*v1[i] else # id1 <-----> id2 rmat[i, id1[i]] = lmat[i, id2[i]] = wgt*v1[i] y[i] = wgt * abs(dif[i]) } X = rbind(cbind(lmat, rmat),iden) y = c(y, rep(mind[rows], 2)) soln = qr.coef(qr(X), y) ll = llen[rows] = soln[seq_len(neach)] rl = rlen[rows] = soln[neach + seq_len(neach)] # Perhaps put some kind of a check here? for (i in seq_len(npairs)) { v = 1 - v1[i] obsv = 1 - abs(dif[i]) / ifelse(dif[i] > 0, ll[id1[i]] + rl[id2[i]], rl[id1[i]] + ll[id2[i]]) if (v*obsv < 0) message("Comparison discrepancy in group ", by, ", ", psumm[i, 1], ":\n Target overlap = ", round(v, 4), ", overlap on graph = ", round(obsv, 4)) } } # shorten arrows that go past the data range rng = range(est) ii = which(est - llen < rng[1]) llen[ii] = est[ii] - rng[1] ii = which(est + rlen > rng[2]) rlen[ii] = rng[2] - est[ii] invtran = I if (typeid == 1) { tran = extra@misc$tran if(is.character(tran)) { link = try(make.link(tran), silent=TRUE) if (!inherits(link, "try-error")) invtran = link$linkinv } else if (is.list(tran)) invtran = tran$linkinv } lcmpl = invtran(est - llen) rcmpl = invtran(est + rlen) } else lcmpl = rcmpl = NULL if (missing(layout)) { layout = c(1, length(ubv)) if(!horizontal) layout = rev(layout) } facName = paste(priv, collapse=":") form = as.formula(chform) if (horizontal) { if (missing(xlab)) xlab = estName if (missing(ylab)) ylab = facName lattice::dotplot(form, prepanel=prepanel.ci, panel=panel.ci, strip = my.strip, horizontal = TRUE, ylab = ylab, xlab = xlab, data = summ, intervals = intervals, lcl=lcl, ucl=ucl, lcmpl=lcmpl, rcmpl=rcmpl, layout = layout, ...) } else { if (missing(xlab)) xlab = facName if (missing(ylab)) ylab = estName lattice::dotplot(form, prepanel=prepanel.ci, panel=panel.ci, strip = my.strip, horizontal = FALSE, xlab = xlab, ylab = ylab, data = summ, intervals = intervals, lcl=lcl, ucl=ucl, lcmpl=lcmpl, rcmpl=rcmpl, layout = layout, ...) } } lsmeans/R/lsmeans.R0000644000175100001440000006072213013732335013727 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # lsmeans and related functions # Utility to pick out the args that can be passed to a function .args.for.fcn = function(fcn, args) { oknames = names(as.list(args(fcn))) mat = pmatch(names(args), oknames) args = args[!is.na(mat)] mat = mat[!is.na(mat)] names(args) = oknames[mat] args } # Create a list and give it class class.name .cls.list <- function(class.name, ...) { result <- list(...) class(result) <- c(class.name, "list") result } setMethod("show", "lsmobj", function(object) print(summary(object)) ) ### lsmeans S3 generics ... ### I am opting to use S3 methods, cascaded for two arguments ### rather than messing with S4 methods lsmeans = function(object, specs, ...) UseMethod("lsmeans", specs) # lsmeans.default = function(object, specs, ...) { rgargs = list(object = object, ...) ####.args.for.fcn(ref.grid, list(object=object, ...)) rgargs$options = NULL # don't pass options to ref.grid RG = do.call("ref.grid", rgargs) lsargs = list(object = RG, specs = specs, ...) #for (nm in names(rgargs)[-1]) lsargs[[nm]] = NULL do.call("lsmeans", lsargs)###lsmeans(RG, specs, ...) } lsmeans.formula = function(object, specs, contr.list, trend, ...) { if (!missing(trend)) return(lstrends(object, specs, var=trend, ...)) if(length(specs) == 2) { # just a rhs by = .find.by(as.character(specs[2])) lsmeans(object, .all.vars(specs), by = by, ...) } else { # lsms = lsmeans(object, .all.vars(specs[-2]), ...) contr.spec = .all.vars(specs[-3])[1] by = .find.by(as.character(specs[3])) # Handle old-style case where contr is a list of lists if (!missing(contr.list)) { cmat = contr.list[[contr.spec]] if (!is.null(cmat)) contr.spec = cmat } lsmeans(object, specs = .all.vars(specs[-2]), by = by, contr = contr.spec, ...) } } # List of specs lsmeans.list = function(object, specs, ...) { result = list() nms = names(specs) # Format a string describing the results .make.desc = function(meth, pri, by) { pri = paste(pri, collapse = ", ") desc = paste(meth, "of", pri) if (!is.null(by)) { by = paste(by, collapse = ", ") desc = paste(desc, "|", by) } desc } for (i in seq_len(length(specs))) { res = lsmeans(object=object, specs = specs[[i]], ...) nm = nms[i] if (is.data.frame(res)) { # happens e.g. when cld is used if (is.null(nm)) nm = .make.desc("summary", attr(res, "pri.vars"), attr(res, "by.vars")) result[[nm]] = res } else if (is.list(res)) { for (j in seq_len(length(res))) { m = res[[j]]@misc if (is.null(nm)) names(res)[j] = .make.desc(m$methDesc, m$pri.vars, m$by.vars) else names(res)[j] = paste(nm, m$methDesc) } result = c(result,res) } else{ if (is.null(nm)) nm = .make.desc(res@misc$methDesc, res@misc$pri.vars, res@misc$by.vars) result[[nm]] = res } } class(result) = c("lsm.list", "list") result } # Generic for after we've gotten specs in character form lsmeans.character = function(object, specs, ...) { UseMethod("lsmeans.character") } # Needed for model objects lsmeans.character.default = function(object, specs, trend, ...) { if (!missing(trend)) lstrends(object, specs, var = trend, ...) else lsmeans.default(object, specs, ...) } # Method for a ref.grid -- all methods will get us here eventually lsmeans.character.ref.grid = function(object, specs, by = NULL, fac.reduce = function(coefs) apply(coefs, 2, mean), contr, options = getOption("lsmeans")$lsmeans, weights, trend, ...) { if (!missing(trend)) { stop("Trend results require supplying the model object itself, not a reference grid") # message("Re-fitting the model. Avoid this by calling lsmeans with the model object.") # cl = match.call() # cl[[1]] = as.name("lstrends") # cl$model = object@model.info$call # cl$var = trend # cl$object = cl$trend = NULL # return(eval(cl)) } RG = object facs = union(specs, by) if ((length(facs) == 1) && (facs == "1")) { ### just want grand mean RG@levels[["1"]] = "overall" RG@grid[ ,"1"] = 1 } # Figure out the structure of the grid wgt = RG@grid[[".wgt."]] if(all(zapsmall(wgt) == 0)) wgt = wgt + 1 ### repl all zero wgts with 1 dims = sapply(RG@levels, length) row.idx = array(seq_len(nrow(RG@linfct)), dims) use.mars = match(facs, names(RG@levels)) # which margins to use avgd.mars = setdiff(seq_along(dims)[dims>1], use.mars) # margins that we average over # Reconcile weights, if there are any margins left if ((length(avgd.mars) > 0) && !missing(weights)) { if (is.character(weights)) { if (is.null(wgt)) message("Weighting information not available -- deferring to fac.reduce") else { wopts = c("equal","proportional","outer","cells","show.levels","invalid") weights = switch(wopts[pmatch(weights, wopts, 5)], equal = rep(1, prod(dims[avgd.mars])), proportional = as.numeric(plyr::aaply(row.idx, avgd.mars, function(idx) sum(wgt[idx]))), outer = { ftbl = plyr::aaply(row.idx, avgd.mars, function(idx) sum(wgt[idx]), .drop = FALSE) w = N = sum(ftbl) for (d in seq_along(dim(ftbl))) w = outer(w, plyr::aaply(ftbl, d, sum) / N) as.numeric(w) }, cells = "fq", show.levels = { cat("lsmeans are obtained by averaging over these factor combinations\n") return(do.call(expand.grid, RG@levels[avgd.mars])) }, invalid = stop("Invalid 'weights' option: '", weights, "'") ) } } if (is.matrix(weights)) { wtrow = 0 fac.reduce = function(coefs) { wtmat = .diag(weights[wtrow+1, ]) / sum(weights[wtrow+1, ]) ans = apply(wtmat %*% coefs, 2, sum) wtrow <<- (1 + wtrow) %% nrow(weights) ans } } else if (is.numeric(weights)) { wtmat = .diag(weights) wtsum = sum(weights) if (wtsum <= 1e-8) wtsum = NA fac.reduce = function(coefs) { if (nrow(coefs) != nrow(wtmat)) stop("Nonconforming number of weights -- need ", nrow(coefs)) apply(wtmat %*% coefs, 2, sum) / wtsum } } } # Get the required factor combs levs = list() for (f in facs) { levs[[f]] = RG@levels[[f]] if (!hasName(levs, f)) stop(paste("No variable named", f, "in the reference grid")) } combs = do.call("expand.grid", levs) if (!missing(weights) && (weights == "fq")) K = plyr::alply(row.idx, use.mars, function(idx) { fq = RG@grid[[".wgt."]][idx] apply(.diag(fq) %*% RG@linfct[idx, , drop=FALSE], 2, sum) / sum(fq) }) else K = plyr::alply(row.idx, use.mars, function(idx) { fac.reduce(RG@linfct[idx, , drop=FALSE]) }) linfct = t(as.matrix(as.data.frame(K))) row.names(linfct) = NULL if(.some.term.contains(union(facs, RG@roles$trend), RG@model.info$terms)) message("NOTE: Results may be misleading due to involvement in interactions") # Figure offset, if any if (hasName(RG@grid, ".offset.")) { combs[[".offset."]] = as.numeric(plyr::aaply(row.idx, use.mars, function(idx) fac.reduce(as.matrix(RG@grid[idx, ".offset.", drop=FALSE])))) } avgd.over = names(RG@levels[avgd.mars]) # Update .wgt column of grid, if it exists if (!is.null(wgt)) { combs[[".wgt."]] = as.numeric(plyr::aaply(row.idx, use.mars, function(idx) sum(wgt[idx]))) } RG@roles$responses = character() RG@misc$famSize = nrow(linfct) if(RG@misc$estName == "prediction") RG@misc$estName = "lsmean" RG@misc$adjust = "none" RG@misc$infer = c(TRUE,FALSE) RG@misc$pri.vars = setdiff(facs, by) RG@misc$by.vars = by RG@misc$avgd.over = union(RG@misc$avgd.over, avgd.over) RG@misc$methDesc = "lsmeans" RG@roles$predictors = names(levs) result = new("lsmobj", RG, linfct = linfct, levels = levs, grid = combs) if(!is.null(options)) { options$object = result result = do.call("update.ref.grid", options) } if (missing(contr)) result else { # return a list with lsmeans and contrasts if (is.character(contr) && contr == "cld") { # TO DO: provide for passing dots to cld return(cld(result, by = by)) } ctrs = contrast(result, method = contr, by = by, ...) .cls.list("lsm.list", lsmeans = result, contrasts = ctrs) } } # utility to parse 'by' part of a formula .find.by = function(rhs) { b = strsplit(rhs, "\\|")[[1]] if (length(b) > 1) .all.vars(as.formula(paste("~",b[2]))) else NULL } ### 'contrast' S3 generic and method contrast = function(object, ...) UseMethod("contrast") contrast.ref.grid = function(object, method = "eff", interaction = FALSE, by, offset = NULL, name = "contrast", options = getOption("lsmeans")$contrast, adjust, ...) { if(missing(by)) by = object@misc$by.vars if(length(by) == 0) # character(0) --> NULL by = NULL if (is.logical(interaction) && interaction) interaction = method if (!is.logical(interaction)) { # i.e., interaction is not FALSE if (!is.character(interaction)) stop("interaction requires named contrast function(s)") if(missing(adjust)) adjust = "none" by = NULL vars = names(object@levels) k = length(vars) if(!is.null(by)) { vars = c(setdiff(vars, by), by) k = k - length(by) } interaction = rep(interaction, k)[1:k] for (i in k:1) { nm = paste(vars[i], interaction[i], sep = "_") object = contrast.ref.grid(object, interaction[i], by = vars[-i], name = nm) vars[i] = nm } object = update(object, by = by, adjust = adjust, ...) if(!is.null(options)) { options$object = object object = do.call(update.ref.grid, options) } return(object) } # else args = object@grid args[[".offset."]] = NULL args[[".wgt."]] = NULL # ignore auxiliary stuff in labels, etc. if (!is.null(by)) { by.rows = .find.by.rows(args, by) bylevs = args[, by, drop=FALSE] args = args[by.rows[[1]], , drop=FALSE] for (nm in by) args[[nm]] = NULL } args$sep = "," levs = do.call("paste", args) if (is.list(method)) { cmat = as.data.frame(method, optional = TRUE) # I have no clue why they named that argument 'optional', # but setting it to TRUE keeps it from messing up the names method = function(levs) cmat } else if (is.character(method)) { fn = paste(method, "lsmc", sep=".") method = if (exists(fn, mode="function")) get(fn) else stop(paste("Contrast function '", fn, "' not found", sep="")) } # case like in old lsmeans, contr = list else if (!is.function(method)) stop("'method' must be a function or the basename of an '.lsmc' function") # Get the contrasts; this should be a data.frame cmat = method(levs, ...) if (!is.data.frame(cmat)) stop("Contrast function must provide a data.frame") else if(ncol(cmat) == 0) warning("No contrasts were generated! Perhaps only one lsmean is involved.\n", " This can happen, for example, when your predictors are not factors.") else if (nrow(cmat) != nrow(args)) stop("Nonconforming number of contrast coefficients") if (is.null(by)) { linfct = t(cmat) %*% object@linfct grid = data.frame(.contrast.=names(cmat)) if (hasName(object@grid, ".offset.")) grid[[".offset."]] = t(cmat) %*% object@grid[[".offset."]] } # NOTE: The kronecker thing here is nice and efficient but depends # on the grid being regular -- same number of rows for each 'by' case # If you ever want to expand to irregular grids, this block will # have to change, but everything else is probably OK. else { tcmat = kronecker(.diag(rep(1,length(by.rows))), t(cmat)) linfct = tcmat %*% object@linfct[unlist(by.rows), ] tmp = expand.grid(con= names(cmat), by = seq_len(length(by.rows)))###unique(by.id)) grid = data.frame(.contrast. = tmp$con) n.each = ncol(cmat) row.1st = sapply(by.rows, function(x) x[1]) xlevs = list() for (v in by) xlevs[[v]] = rep(bylevs[row.1st, v], each=n.each) grid = cbind(grid, as.data.frame(xlevs)) if (hasName(object@grid, ".offset.")) grid[[".offset."]] = tcmat %*% object@grid[unlist(by.rows), ".offset."] } # Rename the .contrast. column -- ordinarily to "contrast", # but otherwise a unique variation thereof con.pat = paste("^", name, "[0-p]?", sep = "") n.prev.con = length(grep(con.pat, names(grid))) con.col = grep("\\.contrast\\.", names(grid)) con.name = paste(name, ifelse(n.prev.con == 0, "", n.prev.con), sep="") names(grid)[con.col] = con.name row.names(linfct) = NULL misc = object@misc misc$initMesg = NULL # initial annotation likely will no longer apply misc$estName = "estimate" if (!is.null(et <- attr(cmat, "type"))) misc$estType = et else { is.con = all(abs(sapply(cmat, sum)) < .001) misc$estType = ifelse(is.con, "contrast", "prediction") } misc$methDesc = attr(cmat, "desc") misc$famSize = size = nrow(args) misc$pri.vars = setdiff(names(grid), c(".offset.",".wgt.")) if (missing(adjust)) adjust = attr(cmat, "adjust") if (is.null(adjust)) adjust = "none" if (!is.null(attr(cmat, "offset"))) offset = attr(cmat, "offset") if (!is.null(offset)) { if(!hasName(grid, ".offset.")) grid[[".offset."]] = 0 grid[[".offset."]] = grid[[".offset."]] + rep(offset, length(by.rows)) } misc$adjust = adjust misc$infer = c(FALSE, TRUE) misc$by.vars = by # zap the transformation info except in very special cases if (!is.null(misc$tran)) { misc$orig.tran = misc$tran # --- previous code # # anything other than (-1,0,1)? # non.comp = setdiff(zapsmall(unique(as.matrix(cmat))), c(-1,0,1)) # if(length(non.comp) == 0 && (misc$tran %in% c("log", "logit"))) { # misc$orig.inv.lbl = misc$inv.lbl # misc$inv.lbl = ifelse(misc$tran == "logit", "odds.ratio", # paste(misc$inv.lbl,"ratio",sep=".")) # misc$tran = "log" # } # Replacement 2016-10-19: true.con = all(zapsmall(apply(cmat, 2, sum)) == 0) # each set of coefs sums to 0 if (true.con && misc$tran == "log") { misc$orig.inv.lbl = misc$inv.lbl misc$inv.lbl = paste(misc$inv.lbl,"ratio",sep=".") misc$tran = "log" } else if (true.con && misc$tran == "logit") { misc$orig.inv.lbl = misc$inv.lbl misc$inv.lbl = "odds.ratio" misc$tran = "log.o.r." } else misc$tran = misc$tran.mult = NULL } # ensure we don't inherit inappropriate settings misc$null = misc$delta = misc$side = NULL object@roles$predictors = "contrast" levels = list() for (nm in setdiff(names(grid), ".offset.")) levels[[nm]] = unique(grid[[nm]]) result = new("lsmobj", object, linfct=linfct, levels=levels, grid=grid, misc=misc) if(!is.null(options)) { options$object = result result = do.call("update.ref.grid", options) } result } # return list of row indexes in tbl for each combination of by # tbl should be a data.frame .find.by.rows = function(tbl, by) { if (is.null(by)) return(list(seq_len(nrow(tbl)))) if (any(is.na(match(by, names(tbl))))) stop("'by' variables are not all in the grid") bylevs = tbl[ , by, drop = FALSE] by.id = do.call("paste", bylevs) uids = unique(by.id) result = lapply(uids, function(id) which(by.id == id)) names(result) = uids result } # confint method confint.ref.grid = function(object, parm, level=.95, ...) { summary(object, infer=c(TRUE,FALSE), level=level, ...) } # test S3 generic and method test = function(object, null, ...) { UseMethod("test") } test.ref.grid = function(object, null = 0, joint = FALSE, verbose = FALSE, rows, by, ...) { # if joint = FALSE, this is a courtesy method for 'contrast' # else it computes the F test or Wald test of H0: L*beta = null # where L = object@linfct if (!joint) { if (missing(by)) summary(object, infer=c(FALSE,TRUE), null = null, ...) else summary(object, infer=c(FALSE,TRUE), null = null, by = by, ...) } else { if(verbose) { cat("Joint test of the following linear predictions\n") print(cbind(object@grid, equals = null)) } L = object@linfct bhat = object@bhat estble.idx = which(!is.na(object@bhat)) bhat = bhat[estble.idx] est.flag = !is.na(object@nbasis[1]) ### L = L[, estble.idx, drop = FALSE] if (!missing(rows)) by.rows = list(sel.rows = rows) else { by.rows = list(all = seq_len(nrow(L))) if(missing(by)) by = object@misc$by.vars if (!is.null(by)) by.rows = .find.by.rows(object@grid, by) } lindep = nonest = FALSE result = lapply(by.rows, function(rows) { LL = L[rows, , drop = FALSE] # estract est'ble rows if(est.flag) { erows = estimability::is.estble(LL, object@nbasis) nonest <<- nonest || (sum(erows) < nrow(LL)) LL = LL[erows, estble.idx, drop = FALSE] } # Check rank qrLt = qr(t(LL)) # this will work even if LL has 0 rows r = qrLt$rank if (r == 0) return(c(df1 = 0, df2 = NA, F = NA, p.value = NA)) if (r < nrow(LL)) { if(!all(null == 0)) stop("Rows are linearly dependent - cannot do the test when 'null' != 0") else lindep <<- TRUE } tR = t(qr.R(qrLt))[1:r, 1:r, drop = FALSE] tQ = t(qr.Q(qrLt))[1:r, , drop = FALSE] if(length(null) < r) null = rep(null, r) z = tQ %*% bhat - solve(tR, null[1:r]) zcov = tQ %*% object@V %*% t(tQ) F = sum(z * solve(zcov, z)) / r df2 = object@dffun(tQ, object@dfargs) if (is.na(df2)) p.value = pchisq(F*r, r, lower.tail = FALSE) else p.value = pf(F, r, df2, lower.tail = FALSE) c(round(c(df1 = r, df2 = df2), 2), F = round(F, 3), p.value = p.value) }) result = as.data.frame(t(as.data.frame(result))) if (!missing(by)) { fbr = sapply(by.rows, "[", 1) result = cbind(object@grid[fbr, by, drop = FALSE], result) } class(result) = c("summary.ref.grid", "data.frame") if (lindep) message("There are linearly dependent rows - df are reduced accordingly") if (nonest) message("Some rows are non-estimable and were excluded") result } } # pairs method pairs.ref.grid = function(x, reverse = FALSE, ...) { object = x # for my sanity if (reverse) contrast(object, method = "revpairwise", ...) else contrast(object, method = "pairwise", ...) } # Check if model contains a term containing all elts of facs # Note: if an lstrends call, we want to include trend var in facs # terms is terms() component of model .some.term.contains = function(facs, terms) { for (trm in attr(terms, "term.labels")) { if(all(sapply(facs, function(f) length(grep(f,trm))>0))) if (length(.all.vars(as.formula(paste("~",trm)))) > length(facs)) return(TRUE) } return(FALSE) } # Construct a new lsmobj with given arguments lsmobj = function(bhat, V, levels, linfct, df = NA, post.beta = matrix(NA), ...) { if ((nrow(V) != ncol(V)) || (nrow(V) != ncol(linfct)) || (length(bhat) != ncol(linfct))) stop("bhat, V, and linfct are incompatible") if (!is.list(levels)) levels = list(level = levels) grid = do.call(expand.grid, levels) if (nrow(grid) != nrow(linfct)) stop("linfct should have ", nrow(grid), "rows") model.info = list(call = match.call(), xlev = levels) roles = list(predictors= names(grid), responses=character(0), multresp=character(0)) if (is.function(df)) { dffun = df dfargs = list(...)$dfargs } else { dffun = function(x, dfargs) dfargs$df dfargs = list(df = df) } misc = list(estName = "estimate", estType = "prediction", infer = c(TRUE,FALSE), level = .95, adjust = "none", famSize = nrow(linfct), avgd.over = character(0), pri.vars = names(grid), methDesc = "lsmobj") result = new("lsmobj", model.info=model.info, roles=roles, grid=grid, levels = levels, matlevs=list(), linfct=linfct, bhat=bhat, nbasis=all.estble, V=V, dffun=dffun, dfargs=dfargs, misc=misc, post.beta=post.beta) update(result, ..., silent=TRUE) } lsmeans/R/betareg.support.R0000644000175100001440000001326013013732335015404 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # Support for 'betareg' class # mode is same as 'type' in predict.betareg, PLUS # mode = "phi.link" refers to link function before back-transforming to "precision" recover.data.betareg = function(object, mode = c("response", "link", "precision", "phi.link", "variance", "quantile"), ...) { fcall = object$call mode = match.arg(mode) if (mode %in% c("response", "link")) mode = "mean" if (mode == "phi.link") mode = "precision" if(mode %in% c("response", "link", "precision")) trms = delete.response(terms(object, model = mode)) else trms = delete.response(object$terms$full) # Make sure there's an offset function available env = new.env(parent = attr(trms, ".Environment")) env$offset = function(x) x attr(trms, ".Environment") = env recover.data(fcall, trms, object$na.action, ...) } # PRELIMINARY... # Currently works correctly only for "resp", "link", "precision", "phi" modes lsm.basis.betareg = function(object, trms, xlev, grid, mode = c("response", "link", "precision", "phi.link", "variance", "quantile"), quantile = .5, ...) { mode = match.arg(mode) # if (mode %in% c("variance", "quantile")) # stop(paste0('"', mode, '" mode is not yet supported.')) # figure out which parameters we need model = if (mode %in% c("response", "link")) "mean" else if (mode %in% c("precision", "phi.link")) "precision" else "full" V = .pscl.vcov(object, model = model) # borrowed from pscl methods bhat = coef(object, model = model) nbasis = estimability::all.estble dffun = function(k, dfargs) NA dfargs = list() if (mode %in% c("response", "link", "precision", "phi.link")) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts[[model]]) misc = list(tran = object$link[[model]]$name) if (mode %in% c("response", "precision")) { misc$postGridHook = ".betareg.pg" } } else { ### (mode %in% c("variance", "quantile")) m.trms = delete.response(terms(object, "mean")) m.m = model.frame(m.trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(m.trms, m.m, contrasts.arg = object$contrasts$mean) m.idx = seq_len(ncol(X)) m.lp = as.numeric(X %*% bhat[m.idx] + .get.offset(m.trms, grid)) mu = object$link$mean$linkinv(m.lp) p.trms = delete.response(terms(object, "precision")) p.m = model.frame(m.trms, grid, na.action = na.pass, xlev = xlev) Z = model.matrix(p.trms, p.m, contrasts.arg = object$contrasts$precision) p.lp = as.numeric(Z %*% bhat[-m.idx] + .get.offset(p.trms, grid)) phi = object$link$precision$linkinv(p.lp) if (mode == "variance") { bhat = mu * (1 - mu) / (1 + phi) dbhat.dm = (1 - 2 * mu) / (1 + phi) dbhat.dp = -bhat / (1 + phi) delta = cbind(diag(dbhat.dm) %*% X, diag(dbhat.dp) %*% Z) V = delta %*% tcrossprod(V, delta) misc = list() } else { ### (mode = "quantile") bhat = as.numeric(sapply(quantile, function(q) stats::qbeta(q, phi * mu, phi * (1 - mu)))) V = matrix(NA, nrow = length(bhat), ncol = length(bhat)) misc = list(ylevs = list(quantile = quantile)) } X = diag(1, length(bhat)) } list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=misc) } # Post-grid hook for simple back-transforming .betareg.pg = function(object) { object@misc$postGridHook = NULL regrid(object, transform = TRUE) } ### predict methods # link: X%*%beta + off_m # response: mu = h_m(link) # # phi.link: Z%*%gamma + off_p # precision: phi = h_p(phi.link) # # variance: mu*(1 - mu) / (1 + phi) # quantile: qbeta(p, mu*phi, (1 - mu)*phi) # # Defns: # phi = a + b # mu = a / (a + b) # so that phi*mu = a and phi*(1 - mu) = b, # Variance = ab / [(a + b)^2 * (a + b + 1)] lsmeans/R/lsm.list.R0000644000175100001440000000503413013732335014025 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # Methods for lsm.list objects # Summary method for an lsm.list summary.lsm.list <- function(object, ...) lapply(object, function(x) { if (inherits(x, "summary.ref.grid")) x else summary(x, ...) }) print.lsm.list <- function(x, ...) print(summary(x, ...)) str.lsm.list = function(object, ...) { for(nm in names(object)) { cat(paste("$", nm, "\n", sep="")) str(object[[nm]]) cat("\n") } } # Courtesy methods to make it more friendly for follow-ups contrast.lsm.list = function(object, ... , which = 1) { contrast(object[[which]], ...) } pairs.lsm.list = function(x, ..., which = 1) { pairs(x[[which]], ...) } test.lsm.list = function(object, ..., which = 1) { test(object[[which]], ...) } confint.lsm.list = function(object, ..., which = 1) { confint(object[[which]], ...) } cld.lsm.list = function(object, ..., which = 1) { cld(object[[which]], ...) } lsmeans/R/rms-support.R0000644000175100001440000001231713013732335014575 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # Support for objects in the *rms* package recover.data.rms = function(object, ...) { fcall = object$call recover.data(fcall, delete.response(terms(object)), object$na.action$omit, ...) } # TODO: # 1. If multivariate - like mlm method? # 2. orm cases? lsm.basis.rms = function(object, trms, xlev, grid, mode = c("middle", "latent", "linear.predictor", "cum.prob", "exc.prob", "prob", "mean.class"), vcov., ...) { mode = match.arg(mode) bhat = coef(object) if (missing(vcov.)) V = vcov(object, intercepts = "all") else V = .my.vcov(object, vcov.) misc = list() X = predict(object, newdata = grid, type = "x") #xnames = dimnames(X)[[2]] #intcpts = setdiff(names(bhat), xnames) nint = length(bhat) - ncol(X) intcpts = names(bhat)[seq_len(nint)] xnames = setdiff(names(bhat), intcpts) if (length(intcpts) == 1) mode = "single" # stealth mode for ordinary single-intercept case if (mode %in% c("single", "middle", "latent")) { X = cbind(1, X) mididx = ifelse(mode != "middle", 1, as.integer((1 + length(intcpts)) / 2)) dimnames(X)[[2]][1] = switch(mode, single = intcpts, middle = intcpts[mididx], latent = "avg.intercept") if (mode == "middle") { nms = c(intcpts[mididx], xnames) bhat = bhat[nms] V = V[nms, nms, drop = FALSE] } else if (mode == "latent") { bhat = c(mean(bhat[intcpts]), bhat[xnames]) nx = length(xnames) J1 = rbind(rep(1/nint, nint), matrix(0, nrow = nx, ncol = nint)) J2 = rbind(0, diag(1, nx)) J = cbind(J1, J2) V = J %*% V %*% t(J) } ### else mode == "single" and all is OK as it is } else { # mode %in% c("linear.predictor", "cum.prob", "exc.prob", "prob", "mean.class") misc$ylevs = list(cut = intcpts) I = diag(1, nint) J = matrix(1, nrow = nrow(X)) JJ = matrix(1, nrow=nint) X = cbind(kronecker(I, J), kronecker(JJ, X)) # Note V is correct as-is dimnames(X)[[2]] = c(intcpts, xnames) if (mode != "linear.predictor") { misc$mode = mode misc$postGridHook = .clm.postGrid misc$respName = as.character(object$terms)[2] } } # I think rms does not allow rank deficiency... nbasis = estimability::all.estble if (!is.null(object$family)) { if (!is.character(object$family)) misc = .std.link.labels(object$family, misc) else { misc$tran = object$family if (misc$tran == "logistic") misc$tran = "logit" misc$inv.lbl = switch(class(object)[1], orm = "exc.prob", lrm = ifelse(nint == 1, "prob", "exc.prob"), "response") } dffun = function(k, dfargs) NA dfargs = list() } else { dfargs = list(df = object$df.residual) if (is.null(dfargs$df)) dfargs$df = NA dffun = function(k, dfargs) dfargs$df } list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs, misc=misc) } ## Courtesy method to prevent masking this generic in rms package # Excluded because this just causes headaches with CRAN's # dependency checking. # contrast.rms = function(object, ...) { # if (requireNamespace("rms")) # rms::contrast(object, ...) # else # stop ("The 'rms' package is not installed.") # # } lsmeans/R/lstrends.R0000644000175100001440000001065113013732335014117 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### Code for lstrends ### lstrends function lstrends = function(model, specs, var, delta.var=.01*rng, data, transform = c("none", "response"), ...) { estName = paste(var, "trend", sep=".") # Do now as I may replace var later if (missing(data)) { data = try(recover.data (model, data = NULL)) if (inherits(data, "try-error")) stop("Possible remedy: Supply the data used in the 'data' argument") } else # attach needed attributes to given data data = recover.data(model, data = data) x = data[[var]] fcn = NULL # differential if (is.null(x)) { fcn = var var = .all.vars(as.formula(paste("~",var))) if (length(var) > 1) stop("Can only support a function of one variable") else { x = data[[var]] if (is.null(x)) stop("Variable '", var, "' is not in the dataset") } } rng = diff(range(x)) if (delta.var == 0) stop("Provide a nonzero value of 'delta.var'") RG = orig.rg = ref.grid(model, data = data, ...) grid = RG@grid if (!is.null(mr <- RG@roles$multresp)) { # use the grid value only for the 1st mult resp (no dupes) if (length(mr) > 0) grid = grid[grid[[mr]] == RG@levels[[mr]][1], ] } grid[[var]] = grid[[var]] + delta.var basis = lsm.basis(model, attr(data, "terms"), RG@roles$xlev, grid, ...) if (is.null(fcn)) newlf = (basis$X - RG@linfct) / delta.var else { y0 = with(RG@grid, eval(parse(text = fcn))) yh = with(grid, eval(parse(text = fcn))) diffl = (yh - y0) if (any(diffl == 0)) warning("Some differentials are zero") newlf = (basis$X - RG@linfct) / diffl } transform = match.arg(transform) # Now replace linfct w/ difference quotient RG@linfct = newlf RG@roles$trend = var if(hasName(RG@misc, "tran")) { tran = RG@misc$tran if (is.list(tran)) tran = tran$name if (transform == "response") { prd = .est.se.df(orig.rg, do.se = FALSE) lnk = attr(prd, "link") deriv = lnk$mu.eta(prd[[1]]) RG@linfct = diag(deriv) %*% RG@linfct RG@misc$initMesg = paste("Trends are obtained after back-transforming from the", tran, "scale") } else RG@misc$initMesg = paste("Trends are based on the", tran, "(transformed) scale") } RG@misc$tran = RG@misc$tran.mult = NULL RG@misc$estName = estName .save.ref.grid(RG) # save in .Last.ref.grid, if enabled # args for lsmeans calls args = list(object=RG, specs=specs, ...) args$at = args$cov.reduce = args$mult.levs = args$vcov. = args$data = args$trend = NULL do.call("lsmeans", args) } lsmeans/R/summary.R0000644000175100001440000005730413013732335013764 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### This file has summary.ref.grid S3 method and related functions # Computes the quadratic form y'Xy after subsetting for the nonzero elements of y .qf.non0 = function(X, y) { ii = (zapsmall(y) != 0) if (any(ii)) sum(y[ii] * (X[ii, ii, drop = FALSE] %*% y[ii])) else 0 } # utility fcn to get est's, std errors, and df # new arg: do.se -- if FALSE, just do the estimates and return 0 for se and df # returns a data.frame with an add'l "link" attribute if misc$tran is non-null # .est.se.df = function(linfct, bhat, nbasis, V, dffun, dfargs, misc, do.se=TRUE, # tol=getOption("lsmeans")$estble.tol) { # 2.13: Revised to call w/ just object instead of all those args (except linfct) # Also moved offset comps to here, and provided for misc$estHook .est.se.df = function(object, do.se=TRUE, tol = get.lsm.option("estble.tol")) { if (nrow(object@grid) == 0) { result = data.frame(NA, NA, NA) names(result) = c(object@misc$estName, "SE", "df") return(result[-1, ]) } misc = object@misc if (!is.null(hook <- misc$estHook)) { if (is.character(hook)) hook = get(hook) result = hook(object, do.se=do.se, tol=tol) } else { active = which(!is.na(object@bhat)) bhat = object@bhat[active] result = t(apply(object@linfct, 1, function(x) { if (estimability::is.estble(x, object@nbasis, tol)) { x = x[active] est = sum(bhat * x) if(do.se) { se = sqrt(.qf.non0(object@V, x)) df = object@dffun(x, object@dfargs) } else # if these unasked-for results are used, we're bound to get an error! se = df = 0 c(est, se, df) } else c(NA,NA,NA) })) if (!is.null(object@grid$.offset.)) result[, 1] = result[, 1] + object@grid$.offset. } result[1] = as.numeric(result[1]) # silly bit of code to avoid getting a data.frame of logicals if all are NA result = as.data.frame(result) names(result) = c(misc$estName, "SE", "df") if (!is.null(misc$tran) && (misc$tran != "none")) { link = if(is.character(misc$tran)) .make.link(misc$tran) else if (is.list(misc$tran)) misc$tran else NULL if (is.list(link)) { # See if multiple of link is requested if (!is.null(misc$tran.mult)) link$mult = misc$tran.mult if (!is.null(link$mult)) link = with(link, list( linkinv = function(eta) linkinv(eta / mult), mu.eta = function(eta) mu.eta(eta / mult) / mult, name = paste0(round(mult, 3), "*", name))) } if (!is.null(link) && is.null(link$name)) link$name = "linear-predictor" attr(result, "link") = link } result } # utility to compute an adjusted p value # tail is -1, 0, 1 for left, two-sided, or right # Note fam.info is c(famsize, ncontr, estTypeIndex) # 2.14: added corrmat arg, dunnettx & mvt adjustments # NOTE: corrmat is NULL unless adjust == "mvt" .adj.p.value = function(t, df, adjust, fam.info, tail, corrmat) { fam.size = fam.info[1] n.contr = fam.info[2] if (n.contr == 1) # Force no adjustment when just one test adjust = "none" # do a pmatch of the adjust method, case insensitive adj.meths = c("sidak", "tukey", "scheffe", "dunnettx", "mvt", p.adjust.methods) k = pmatch(tolower(adjust), adj.meths) if(is.na(k)) stop("Adjust method '", adjust, "' is not recognized or not valid") adjust = adj.meths[k] if ((tail != 0) && (adjust %in% c("tukey", "scheffe", "dunnettx"))) # meth not approp for 1-sided adjust = "sidak" if ((fam.info[3] != 3) && adjust == "tukey") # not pairwise adjust = "sidak" # asymptotic results when df is NA df[is.na(df)] = Inf # if estType is "prediction", use #contrasts + 1 as family size # (produces right Scheffe CV; Tukey ones are a bit strange) scheffe.dim = ifelse(fam.info[3] == 1, fam.size, fam.size - 1) abst = abs(t) if (tail == 0) unadj.p = 2*pt(abst, df, lower.tail=FALSE) else unadj.p = pt(t, df, lower.tail = (tail<0)) if (adjust %in% p.adjust.methods) { if (n.contr == length(unadj.p)) pval = p.adjust(unadj.p, adjust, n = n.contr) else pval = as.numeric(apply(matrix(unadj.p, nrow=n.contr), 2, function(pp) p.adjust(pp, adjust, n=sum(!is.na(pp))))) } else pval = switch(adjust, sidak = 1 - (1 - unadj.p)^n.contr, # NOTE: tukey, scheffe, dunnettx all assumed 2-sided! tukey = ptukey(sqrt(2)*abst, fam.size, zapsmall(df), lower.tail=FALSE), scheffe = pf(t^2/scheffe.dim, scheffe.dim, df, lower.tail=FALSE), dunnettx = 1 - .pdunnx(abst, n.contr, df), mvt = 1 - .my.pmvt(t, df, corrmat, -tail) # tricky - reverse the tail because we're subtracting from 1 ) chk.adj = match(adjust, c("none", "tukey", "scheffe"), nomatch = 99) do.msg = (chk.adj > 1) && (n.contr > 1) && !((fam.size == 2) && (chk.adj < 10)) if (do.msg) { # xtra = if(chk.adj < 10) paste("a family of", fam.size, "tests") # else paste(n.contr, "tests") xtra = switch(adjust, tukey = paste("for comparing a family of", fam.size, "estimates"), scheffe = paste("with dimensionality", scheffe.dim), paste("for", n.contr, "tests") ) mesg = paste("P value adjustment:", adjust, "method", xtra) } else mesg = NULL list(pval=pval, mesg=mesg, adjust=adjust) } # Code needed for an adjusted critical value # returns a list similar to .adj.p.value # 2.14: Added tail & corrmat args, dunnettx & mvt adjustments # NOTE: corrmat is NULL unless adjust == "mvt" .adj.critval = function(level, df, adjust, fam.info, tail, corrmat) { mesg = NULL fam.size = fam.info[1] n.contr = fam.info[2] if (n.contr == 1) # Force no adjustment when just one interval adjust = "none" adj.meths = c("sidak", "tukey", "scheffe", "dunnettx", "mvt", "bonferroni", "none") k = pmatch(tolower(adjust), adj.meths) if(is.na(k)) { k = which(adj.meths == "bonferroni") ###none") ###mesg = paste("\"", adjust, "\" adjustment is not valid for CIs: used bonferroni instead", sep = "") ###mesg = "Confidence levels are NOT adjusted for multiplicity" } adjust = adj.meths[k] if ((fam.info[3] != 3) && adjust == "tukey") # not pairwise adjust = "sidak" if ((tail != 0) && (adjust %in% c("tukey", "scheffe", "dunnettx"))) # meth not approp for 1-sided adjust = "sidak" if ((fam.info[3] != 3) && adjust == "tukey") # not pairwise adjust = "sidak" # asymptotic results when df is NA df[is.na(df)] = Inf scheffe.dim = ifelse(fam.info[3] == 1, fam.size, fam.size - 1) chk.adj = match(adjust, c("none", "tukey", "scheffe"), nomatch = 99) do.msg = (chk.adj > 1) && (n.contr > 1) && !((fam.size == 2) && (chk.adj < 10)) if (do.msg) { # xtra = if(chk.adj < 10) paste("a family of", fam.size, "estimates") # else paste(n.contr, "estimates") xtra = switch(adjust, tukey = paste("for comparing a family of", fam.size, "estimates"), scheffe = paste("with dimensionality", scheffe.dim), paste("for", n.contr, "estimates") ) mesg = paste("Conf-level adjustment:", adjust, "method", xtra) } adiv = ifelse(tail == 0, 2, 1) # divisor for alpha where needed cv = switch(adjust, none = -qt((1-level)/adiv, df), sidak = -qt((1 - level^(1/n.contr))/adiv, df), bonferroni = -qt((1-level)/n.contr/adiv, df), tukey = qtukey(level, fam.size, df) / sqrt(2), scheffe = sqrt(scheffe.dim * qf(level, scheffe.dim, df)), dunnettx = .qdunnx(level, n.contr, df), mvt = .my.qmvt(level, df, corrmat, tail) ) list(cv = cv, mesg = mesg, adjust = adjust) } ### My own functions to ease access to mvt functions ### These use one argument at a time and expands each (lower, upper) or p to a k-vector ### Use tailnum = -1, 0, or 1 ### NOTE: corrmat needs "by.rows" attribute to tell which rows ### belong to which submatrix. .my.pmvt = function(x, df, corrmat, tailnum) { lower = switch(tailnum + 2, -Inf, -abs(x), x) upper = switch(tailnum + 2, x, abs(x), Inf) by.rows = attr(corrmat, "by.rows") if (is.null(by.rows)) by.rows = list(seq_len(length(x))) by.sel = numeric(length(x)) for (i in seq_along(by.rows)) by.sel[by.rows[[i]]] = i df = .fix.df(df) apply(cbind(lower, upper, df, by.sel), 1, function(z) { idx = by.rows[[z[4]]] k = length(idx) pval = try(mvtnorm::pmvt(rep(z[1], k), rep(z[2], k), df = as.integer(z[3]), corr = corrmat[idx, idx]), silent = TRUE) if (inherits(pval, "try-error")) NA else pval }) } # Vectorized for df but needs p to be scalar .my.qmvt = function(p, df, corrmat, tailnum) { tail = c("lower.tail", "both.tails", "lower.tail")[tailnum + 2] df = .fix.df(df) by.rows = attr(corrmat, "by.rows") if (is.null(by.rows)) by.rows = list(seq_len(length(df))) by.sel = numeric(length(df)) for (i in seq_along(by.rows)) by.sel[by.rows[[i]]] = i # If df all equal, compute just once for each by group eq.df = (diff(range(df)) == 0) i1 = if (eq.df) sapply(by.rows, function(r) r[1]) else seq_along(df) result = apply(cbind(p, df[i1], by.sel[i1]), 1, function(z) { idx = by.rows[[z[3]]] cv = try(mvtnorm::qmvt(z[1], tail = tail, df = as.integer(z[2]), corr = corrmat[idx, idx])$quantile, silent = TRUE) if (inherits(cv, "try-error")) NA else cv }) if (eq.df) { res = result result = numeric(length(df)) for(i in seq_along(by.rows)) result[by.rows[[i]]] = res[i] } result } # utility to get appropriate integer df .fix.df = function(df) { sapply(df, function(d) { if (d > 0) d = max(1, d) if (is.infinite(d) || (d > 9999)) d = 0 floor(d + .25) # tends to round down }) } ### My approximate dunnett distribution ### - a mix of the Tukey cdf and Sidak-corrected t .pdunnx = function(x, k, df, twt = (k - 1)/k) { tukey = ptukey(sqrt(2)*x, (1 + sqrt(1 + 8*k))/2, df) sidak = (pf(x^2, 1, df))^k twt*tukey + (1 - twt)*sidak } # Uses linear interpolation to get quantile .qdunnx = function(p, k, df, ...) { if (k < 1.005) return(qt(1 - .5*(1 - p), df)) xtuk = qtukey(p, (1 + sqrt(1 + 8*k))/2, df) / sqrt(2) xsid = sqrt(qf(p^(1/k), 1, df)) fcn = function(x, d) .pdunnx(x, k, d, ...) - p apply(cbind(xtuk, xsid, df), 1, function(r) { if (abs(diff(r[1:2])) < .0005) return (r[1]) x = try(uniroot(fcn, r[1:2], tol = .0005, d = r[3]), silent = TRUE) if (inherits(x, "try-error")) { warning("Root-finding failed; using qtukey approximation for Dunnett quantile") return(xtuk) } else x$root }) } ### Support for different prediction types ### # Valid values for type arg or predict.type option .valid.types = c("link","response","lp","linear") # get "predict.type" option from misc, and make sure it's legal .get.predict.type = function(misc) { type = misc$predict.type if (is.null(type)) .valid.types[1] else .validate.type(type) } # check a "type" arg to make it legal .validate.type = function (type) { .valid.types[pmatch(type, .valid.types, 1)] } # S3 predict method predict.ref.grid <- function(object, type, ...) { # update with any "summary" options opt = get.lsm.option("summary") if(!is.null(opt)) { opt$object = object object = do.call("update.ref.grid", opt) } if (missing(type)) type = .get.predict.type(object@misc) else type = .validate.type(type) pred = .est.se.df(object, do.se=FALSE) result = pred[[1]] # MOVED TO .EST.SE.DF # if (".offset." %in% names(object@grid)) # result = result + object@grid[[".offset."]] if (type == "response") { link = attr(pred, "link") if (!is.null(link)) result = link$linkinv(result) } result } # S3 summary method summary.ref.grid <- function(object, infer, level, adjust, by, type, df, null, delta, side, ...) { ### For missing arguments, get from misc, else default if(missing(infer)) infer = object@misc$infer if(missing(level)) level = object@misc$level if(missing(adjust)) adjust = object@misc$adjust if(missing(by)) by = object@misc$by.vars if (missing(type)) type = .get.predict.type(object@misc) else type = .validate.type(type) if(missing(df)) df = object@misc$df if(!is.null(df)) object@dffun = function(k, dfargs) df # for missing args that default to zero unless provided or in misc slot .nul.eq.zero = function(val) { if(is.null(val)) 0 else val } if(missing(null)) null = .nul.eq.zero(object@misc$null) if(missing(delta)) delta = .nul.eq.zero(object@misc$delta) if(missing(side)) side = .nul.eq.zero(object@misc$side) # update with any "summary" options opt = get.lsm.option("summary") if(!is.null(opt)) { opt$object = object object = do.call("update.ref.grid", opt) } # reconcile all the different ways we could specify the alternative # ... and map each to one of the first 3 subscripts side.opts = c("left","both","right","two-sided","noninferiority","nonsuperiority","equivalence","superiority","inferiority","0","2","-1","1","+1","<",">","!=","=") side.map = c( 1, 2, 3, 2, 3, 1, 2, 3, 1, 2, 2, 1, 3, 3, 1, 3, 2, 2) side = side.map[pmatch(side, side.opts, 2)[1]] - 2 delta = abs(delta) result = .est.se.df(object) lblnms = setdiff(names(object@grid), c(object@roles$responses, ".offset.", ".wgt.")) lbls = object@grid[lblnms] zFlag = (all(is.na(result$df))) inv = (type == "response") # flag to inverse-transform link = attr(result, "link") if (inv && is.null(link)) inv = FALSE if ((length(infer) == 0) || !is.logical(infer)) infer = c(FALSE, FALSE) if(length(infer == 1)) infer = c(infer,infer) if(inv && !is.null(object@misc$tran)) { if (!is.null(object@misc$inv.lbl)) names(result)[1] = object@misc$inv.lbl else names(result)[1] = "lsresponse" } attr(result, "link") = NULL estName = names(result)[1] mesg = object@misc$initMesg ### Add an annotation when we show results on lp scale and ### there is a transformation if (!inv && !is.null(link)) { mesg = c(mesg, paste("Results are given on the", link$name, "(not the response) scale.")) } if (inv && !is.null(link$unknown)) { mesg = c(mesg, paste0('Unknown transformation "', link$name, '": no transformation done')) inv = FALSE link = NULL } # et = 1 if a prediction, 2 if a contrast (or unmatched or NULL), 3 if pairs et = pmatch(c(object@misc$estType, "c"), c("prediction", "contrast", "pairs"), nomatch = 2)[1] by.size = nrow(object@grid) if (!is.null(by)) for (nm in by) by.size = by.size / length(unique(object@levels[[nm]])) fam.info = c(object@misc$famSize, by.size, et) cnm = NULL # get vcov matrix only if needed (adjust == "mvt") corrmat = NULL if (!is.na(pmatch(adjust, "mvt"))) { corrmat = cov2cor(vcov(object)) attr(corrmat, "by.rows") = .find.by.rows(object@grid, by) } if(infer[1]) { # add CIs acv = .adj.critval(level, result$df, adjust, fam.info, side, corrmat) ###adjust = acv$adjust # in older versions, I forced same adj method for tests cv = acv$cv cv = switch(side + 2, cbind(-Inf, cv), cbind(-cv, cv), cbind(-cv, Inf)) cnm = if (zFlag) c("asymp.LCL", "asymp.UCL") else c("lower.CL","upper.CL") result[[cnm[1]]] = result[[1]] + cv[, 1]*result$SE result[[cnm[2]]] = result[[1]] + cv[, 2]*result$SE mesg = c(mesg, paste("Confidence level used:", level), acv$mesg) if (inv) { clims = with(link, cbind(linkinv(result[[cnm[1]]]), linkinv(result[[cnm[2]]]))) tmp = apply(clims, 1, function(x) { z = diff(x); ifelse(is.na(z), 0, z) }) idx = if (all(tmp >= 0)) 1:2 else 2:1 result[[cnm[1]]] = clims[, idx[1]] result[[cnm[2]]] = clims[, idx[2]] mesg = c(mesg, paste("Intervals are back-transformed from the", link$name, "scale")) } } if(infer[2]) { # add tests if (!all(null == 0)) { result[["null"]] = null if (!is.null(link)) result[["null"]] = link$linkinv(result[["null"]]) } tnm = ifelse (zFlag, "z.ratio", "t.ratio") tail = ifelse(side == 0, -sign(abs(delta)), side) if (side == 0) { if (delta == 0) # two-sided sig test t.ratio = result[[tnm]] = (result[[1]] - null) / result$SE else t.ratio = result[[tnm]] = (abs(result[[1]] - null) - delta) / result$SE } else { t.ratio = result[[tnm]] = (result[[1]] - null + side * delta) / result$SE } apv = .adj.p.value(t.ratio, result$df, adjust, fam.info, tail, corrmat) adjust = apv$adjust # in case it was abbreviated result$p.value = apv$pval mesg = c(mesg, apv$mesg) if (delta > 0) mesg = c(mesg, paste("Statistics are tests of", c("nonsuperiority","equivalence","noninferiority")[side+2], "with a threshold of", delta)) if(tail != 0) mesg = c(mesg, paste("P values are ", ifelse(tail<0,"left-","right-"),"tailed", sep="")) if (inv) mesg = c(mesg, paste("Tests are performed on the", link$name, "scale")) } if (inv) { result[["SE"]] = with(link, abs(mu.eta(result[[1]]) * result[["SE"]])) result[[1]] = with(link, linkinv(result[[1]])) } if (length(object@misc$avgd.over) > 0) { qual = attr(object@misc$avgd.over, "qualifier") if (is.null(qual)) qual = "" mesg = c(paste0("Results are averaged over", qual, " the levels of: ", paste(object@misc$avgd.over, collapse = ", ")), mesg) } summ = cbind(lbls, result) attr(summ, "estName") = estName attr(summ, "clNames") = cnm # will be NULL if infer[1] is FALSE attr(summ, "pri.vars") = setdiff(union(object@misc$pri.vars, object@misc$by.vars), by) attr(summ, "by.vars") = by attr(summ, "mesg") = unique(mesg) class(summ) = c("summary.ref.grid", "data.frame") summ } # left-or right-justify column labels for m depending on "l" or "R" in just .just.labs = function(m, just) { nm = dimnames(m)[[2]] for (j in seq_len(length(nm))) { if(just[nm[j]] == "L") nm[j] = format(nm[j], width = nchar(m[1,j]), just="left") } dimnames(m) = list(rep("", nrow(m)), nm) m } # Format a data.frame produced by summary.ref.grid print.summary.ref.grid = function(x, ..., digits=NULL, quote=FALSE, right=TRUE) { x.save = x if (!is.null(x$df)) x$df = round(x$df, 2) if (!is.null(x$t.ratio)) x$t.ratio = round(x$t.ratio, 3) if (!is.null(x$z.ratio)) x$z.ratio = round(x$z.ratio, 3) if (!is.null(x$p.value)) { fp = x$p.value = format(round(x$p.value,4), nsmall=4, sci=FALSE) x$p.value[fp=="0.0000"] = "<.0001" } just = sapply(x.save, function(col) if(is.numeric(col)) "R" else "L") xc = as.matrix(format.data.frame(x, digits=digits, na.encode=FALSE)) m = apply(rbind(just, names(x), xc), 2, function(x) { w = max(sapply(x, nchar)) if (x[1] == "R") format(x[-seq_len(2)], width = w, justify="right") else format(x[-seq_len(2)], width = w, justify="left") }) if(!is.matrix(m)) m = t(as.matrix(m)) by.vars = attr(x, "by.vars") if (is.null(by.vars)) { m = .just.labs(m, just) print(m, quote=FALSE, right=TRUE) cat("\n") } else { # separate listing for each by variable m = .just.labs(m[, setdiff(names(x), by.vars)], just) pargs = as.list(x[,by.vars, drop=FALSE]) pargs$sep = ", " lbls = do.call(paste, pargs) for (lb in unique(lbls)) { rows = which(lbls==lb) levs = paste(by.vars, "=", xc[rows[1], by.vars]) cat(paste(paste(levs, collapse=", ")), ":\n", sep="") print(m[rows, , drop=FALSE], ..., quote=quote, right=right) cat("\n") } } msg = unique(attr(x, "mesg")) if (!is.null(msg)) for (j in seq_len(length(msg))) cat(paste(msg[j], "\n")) invisible(x.save) } lsmeans/R/ref.grid.R0000644000175100001440000005552513013732335013772 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # Reference grid code # Change to cov.reduce specification: can be... # a function: is applied to all covariates # named list of functions: applied to those covariates (else mean is used) # TRUE - same as mean # FALSE - same as function(x) sort(unique(x)) ref.grid <- function(object, at, cov.reduce = mean, mult.name, mult.levs, options = get.lsm.option("ref.grid"), data, df, type, transform = c("none", "response", "log"), ...) { transform = match.arg(transform) if (!missing(df)) { if(is.null(options)) options = list() options$df = df } # recover the data if (missing(data)) { data = try(recover.data (object, data = NULL, ...)) if (inherits(data, "try-error")) stop("Perhaps a 'data' or 'params' argument is needed") } else # attach needed attributes to given data data = recover.data(object, data = data, ...) if(is.character(data)) # 'data' is in fact an error message stop(data) trms = attr(data, "terms") # find out if any variables are coerced to factors or vice versa coerced = .find.coerced(trms, data) # now list with members 'factors' and 'covariates' # convenience function sort.unique = function(x) sort(unique(x)) # Ensure cov.reduce is a function or list thereof dep.x = list() # list of formulas to fit later fix.cr = function(cvr) { # cvr is TRUE or FALSE if(is.logical(cvr)) if(cvr[1]) cvr = mean else cvr = sort.unique else if (inherits(cvr, "formula")) { if (length(cvr) < 3) stop("Formulas in 'cov.reduce' must be two-sided") lhs = .all.vars(cvr)[1] dep.x[[lhs]] <<- cvr cvr = mean } else if (!inherits(cvr, c("function","list"))) stop("Invalid 'cov.reduce' argument") cvr } # IMPORTANT: following stmts may also affect x.dep if (is.list(cov.reduce)) cov.reduce = lapply(cov.reduce, fix.cr) else cov.reduce = fix.cr(cov.reduce) # zap any formulas that are also in 'at' if (!missing(at)) for (xnm in names(at)) dep.x[[xnm]] = NULL # local cov.reduce function that works with function or named list cr = function(x, nm) { if (is.function(cov.reduce)) cov.reduce(x) else if (hasName(cov.reduce, nm)) cov.reduce[[nm]](x) else mean(x) } # initialize empty lists ref.levels = matlevs = xlev = list() for (nm in attr(data, "responses")) { y = data[[nm]] if (is.matrix(y)) matlevs[[nm]] = apply(y, 2, mean) else ref.levels[[nm]] = mean(y) } for (nm in attr(data, "predictors")) { x = data[[nm]] # Save the original levels of factors, no matter what if (is.factor(x) && !(nm %in% coerced$covariates)) xlev[[nm]] = levels(factor(x)) # (applying factor drops any unused levels) # Now go thru and find reference levels... # mentioned in 'at' list but not coerced factor if (!(nm %in% coerced$factors) && !missing(at) && (hasName(at, nm))) ref.levels[[nm]] = at[[nm]] # factors not in 'at' else if (is.factor(x) && !(nm %in% coerced$covariates)) ref.levels[[nm]] = levels(factor(x)) else if (is.character(x)) ref.levels[[nm]] = sort.unique(x) # matrices else if (is.matrix(x)) { # Matrices -- reduce columns thereof, but don't add to baselevs matlevs[[nm]] = apply(x, 2, cr, nm) # if cov.reduce returns a vector, average its columns if (is.matrix(matlevs[[nm]])) matlevs[[nm]] = apply(matlevs[[nm]], 2, mean) } # covariate coerced, or not mentioned in 'at' else { # single numeric pred but coerced to a factor - use unique values # even if in 'at' list. We'll fix this up later if (nm %in% coerced$factors) ref.levels[[nm]] = sort.unique(x) # Ordinary covariates - summarize else ref.levels[[nm]] = cr(as.numeric(x), nm) } } # Now create the reference grid grid = do.call(expand.grid, ref.levels) # add any matrices for (nm in names(matlevs)) grid[[nm]] = matrix(rep(matlevs[[nm]], each=nrow(grid)), nrow=nrow(grid)) # resolve any covariate formulas for (xnm in names(dep.x)) { if (!all(.all.vars(dep.x[[xnm]]) %in% names(grid))) stop("Formulas in 'cov.reduce' must predict covariates actually in the model") xmod = lm(dep.x[[xnm]], data = data) grid[[xnm]] = predict(xmod, newdata = grid) ref.levels[[xnm]] = NULL } basis = lsm.basis(object, trms, xlev, grid, ...) misc = basis$misc ### Figure out if there is a response transformation... # next stmt assumes that model formula is 1st argument (2nd element) in call. # if not, we probably get an error or something that isn't a formula # and it is silently ignored lhs = try(eval(attr(data, "call")[[2]][-3]), silent = TRUE) if (is.null(misc$tran) && (inherits(lhs, "formula"))) { # No link fcn, but response may be transformed tran = setdiff(.all.vars(lhs, functions = TRUE), c(.all.vars(lhs), "~", "cbind")) if(length(tran) > 0) { tran = paste(tran, collapse = ".") # length > 1: Almost certainly unsupported, but facilitates a more informative error message # Look for a multiplier, e.g. 2*sqrt(y) tst = strsplit(strsplit(as.character(lhs[2]), "\\(")[[1]][1], "\\*")[[1]] if(length(tst) > 1) { mul = suppressWarnings(as.numeric(tst[1])) if(!is.na(mul)) misc$tran.mult = mul tran = gsub("\\*\\.", "", tran) } if (tran == "linkfun") tran = as.list(environment(trms)) misc$tran = tran misc$inv.lbl = "response" } } # Take care of multivariate response multresp = character(0) ### ??? was list() ylevs = misc$ylevs if(!is.null(ylevs)) { # have a multivariate situation if (missing(mult.levs)) { if (missing(mult.name)) mult.name = names(ylevs)[1] ref.levels[[mult.name]] = ylevs[[1]] multresp = mult.name MF = data.frame(ylevs) names(MF) = mult.name } else { k = prod(sapply(mult.levs, length)) if (k != length(ylevs[[1]])) stop("supplied 'mult.levs' is of different length than that of multivariate response") for (nm in names(mult.levs)) ref.levels[[nm]] = mult.levs[[nm]] multresp = names(mult.levs) MF = do.call("expand.grid", mult.levs) } ###grid = do.call("expand.grid", ref.levels) grid = merge(grid, MF) # add any matrices for (nm in names(matlevs)) grid[[nm]] = matrix(rep(matlevs[[nm]], each=nrow(grid)), nrow=nrow(grid)) } # Here's a complication. If a numeric predictor was coerced to a factor, we had to # include all its levels in the reference grid, even if altered in 'at' # Moreover, whatever levels are in 'at' must be a subset of the unique values # So we now need to subset the rows of the grid and linfct based on 'at' problems = if (!missing(at)) intersect(c(multresp, coerced$factors), names(at)) else character(0) if (length(problems > 0)) { incl.flags = rep(TRUE, nrow(grid)) for (nm in problems) { if (is.numeric(ref.levels[[nm]])) { at[[nm]] = round(at[[nm]], 3) ref.levels[[nm]] = round(ref.levels[[nm]], 3) } # get only "legal" levels at[[nm]] = at[[nm]][at[[nm]] %in% ref.levels[[nm]]] # Now which of those are left out? excl = setdiff(ref.levels[[nm]], at[[nm]]) for (x in excl) incl.flags[grid[[nm]] == x] = FALSE ref.levels[[nm]] = at[[nm]] } if (!any(incl.flags)) stop("Reference grid is empty due to mismatched levels in 'at'") grid = grid[incl.flags, , drop=FALSE] basis$X = basis$X[incl.flags, , drop=FALSE] } # Any offsets??? (misc$offset.mult might specify removing or reversing the offset) if(!is.null(attr(trms,"offset"))) { om = 1 if (!is.null(misc$offset.mult)) om = misc$offset.mult if (any(om != 0)) grid[[".offset."]] = om * .get.offset(trms, grid) } ### --- Determine weights for each grid point --- (added ver.2.11), updated ver.2.14 to include weights if (!hasName(data, "(weights)")) data[["(weights)"]] = 1 nms = union(names(xlev), coerced$factors) # only factors, no covariates or mult.resp # originally, I used 'plyr::count', but there are probs when there is a 'freq' variable id = plyr::id(data[, nms, drop = FALSE], drop = TRUE) uid = !duplicated(id) key = do.call(paste, data[uid, nms, drop = FALSE]) key = key[order(id[uid])] #frq = tabulate(id, attr(id, "n")) tgt = do.call(paste, grid[, nms, drop = FALSE]) wgt = rep(0, nrow(grid)) for (i in seq_along(key)) wgt[tgt == key[i]] = sum(data[["(weights)"]][id==i]) grid[[".wgt."]] = wgt misc$ylevs = NULL # No longer needed misc$estName = "prediction" misc$estType = "prediction" misc$infer = c(FALSE,FALSE) misc$level = .95 misc$adjust = "none" misc$famSize = nrow(grid) misc$avgd.over = character(0) post.beta = basis$post.beta if (is.null(post.beta)) post.beta = matrix(NA) result = new("ref.grid", model.info = list(call = attr(data,"call"), terms = trms, xlev = xlev), roles = list(predictors = attr(data, "predictors"), responses = attr(data, "responses"), multresp = multresp), grid = grid, levels = ref.levels, matlevs = matlevs, linfct = basis$X, bhat = basis$bhat, nbasis = basis$nbasis, V = basis$V, dffun = basis$dffun, dfargs = basis$dfargs, misc = misc, post.beta = post.beta) if (!missing(type)) { if (is.null(options)) options = list() options$predict.type = type } if(!is.null(options)) { options$object = result result = do.call("update.ref.grid", options) } if(!is.null(hook <- misc$postGridHook)) { if (is.character(hook)) hook = get(hook) result@misc$postGridHook = NULL result = hook(result) } if(transform != "none") result = regrid(result, transform = transform) .save.ref.grid(result) result } #### End of ref.grid ------------------------------------------ # local utility to identify ref.grid that is not an lsmobj .is.true.ref.grid = function(object) { is(object, "ref.grid") && !is(object, "lsmobj") } # local utility to save each newly constructed ref.grid, if enabled # Goes into global environment unless .Last.ref.grid is found further up .save.ref.grid = function(object) { if(get.lsm.option("save.ref.grid") && .is.true.ref.grid(object)) assign(".Last.ref.grid", object, inherits = TRUE) } # This function figures out which covariates in a model # have been coerced to factors. And also which factors have been coerced # to be covariates .find.coerced = function(trms, data) { if (ncol(data) == 0) return(list(factors = integer(0), covariates = integer(0))) isfac = sapply(data, function(x) inherits(x, "factor")) # Character vectors of factors and covariates in the data... facs.d = names(data)[isfac] covs.d = names(data)[!isfac] lbls = attr(trms, "term.labels") M = model.frame(trms, utils::head(data, 2)) #### just need a couple rows isfac = sapply(M, function(x) inherits(x, "factor")) # Character vector of terms in the model frame that are factors ... facs.m = names(M)[as.logical(isfac)] covs.m = setdiff(names(M), facs.m) # Exclude the terms that are already factors # What's left will be things like "factor(dose)", "interact(dose,treat)", etc cfac = setdiff(facs.m, facs.d) if(length(cfac) != 0) { cvars = lapply(cfac, function(x) .all.vars(reformulate(x))) # Strip off the function calls cfac = intersect(unique(unlist(cvars)), covs.d) # Exclude any variables that are already factors } # Do same with covariates ccov = setdiff(covs.m, covs.d) if(length(ccov) > 0) { cvars = lapply(ccov, function(x) .all.vars(reformulate(x))) ccov = intersect(unique(unlist(cvars)), facs.d) } list(factors = cfac, covariates = ccov) } # calculate the offset for the given grid .get.offset = function(terms, grid) { off.idx = attr(terms, "offset") offset = rep(0, nrow(grid)) tvars = attr(terms, "variables") for (i in off.idx) offset = offset + eval(tvars[[i+1]], grid) offset } ### =========== Methods for ref.grid class ============================= # (note: summary-related methods moved to a new file) str.ref.grid <- function(object, ...) { showlevs = function(x) { # internal convenience function if (is.null(x)) cat("(predicted by other variables)") else cat(paste(format(x, digits = 5, justify = "none"), collapse=", ")) } #cat("responses: ") #showlevs(object@roles$responses) levs = object@levels cat(paste("'", class(object)[1], "' object with variables:\n", sep="")) for (nm in union(object@roles$predictors, union(object@roles$multresp, object@roles$responses))) { cat(paste(" ", nm, " = ", sep = "")) if (hasName(object@matlevs, nm)) { if (nm %in% object@roles$responses) cat("multivariate response with means: ") else cat("matrix with column means: ") cat("\n ") showlevs(object@matlevs[[nm]]) } else if (nm %in% object@roles$multresp) { cat("multivariate response levels: ") showlevs(levs[[nm]]) } else if (nm %in% object@roles$responses) { cat("response variable with mean ") showlevs(levs[[nm]]) } else showlevs(levs[[nm]]) cat("\n") } if(!is.null(tran <- object@misc$tran)) { if (is.list(tran)) tran = ifelse(is.null(tran$name), "custom - see slot(, \"misc\")$tran", tran$name) if (!is.null(mul <- object@misc$tran.mult)) tran = paste0(mul, "*", tran) cat(paste("Transformation:", dQuote(tran), "\n")) } } print.ref.grid = function(x,...) print(summary.ref.grid(x, ...)) # vcov method vcov.ref.grid = function(object, ...) { tol = get.lsm.option("estble.tol") if (!is.null(hook <- object@misc$vcovHook)) { if (is.character(hook)) hook = get(hook) hook(object, tol = tol, ...) } else { X = object@linfct estble = estimability::is.estble(X, object@nbasis, tol) ###apply(X, 1, .is.estble, object@nbasis, tol) X[!estble, ] = NA X = X[, !is.na(object@bhat), drop = FALSE] X %*% tcrossprod(object@V, X) } } # Method to alter contents of misc slot update.ref.grid = function(object, ..., silent = FALSE) { args = list(...) valid.misc = c("adjust","alpha","avgd.over","by.vars","delta","df", "initMesg","estName","estType","famSize","infer","inv.lbl", "level","methdesc","null","predict.type","pri.vars","side","tran","tran.mult") valid.slots = slotNames(object) valid.choices = union(valid.misc, valid.slots) misc = object@misc for (nm in names(args)) { fullname = try(match.arg(nm, valid.choices), silent=TRUE) if(inherits(fullname, "try-error")) { if (!silent) message("Argument ", sQuote(nm), " was ignored. Valid choices are:\n", paste(valid.choices, collapse=", ")) } else { if (fullname %in% valid.slots) slot(object, fullname) = args[[nm]] else { if (fullname == "by.vars") { allvars = union(misc$pri.vars, misc$by.vars) misc$pri.vars = setdiff(allvars, args[[nm]]) } if (fullname == "pri.vars") { allvars = union(misc$pri.vars, misc$by.vars) misc$by.vars = setdiff(allvars, args[[nm]]) } misc[[fullname]] = args[[nm]] } } } object@misc = misc object } ### set or change lsmeans options lsm.options = function(...) { opts = getOption("lsmeans", list()) # if (is.null(opts)) opts = list() newopts = list(...) for (nm in names(newopts)) opts[[nm]] = newopts[[nm]] options(lsmeans = opts) if (length(newopts) > 0) invisible(opts) else opts } # equivalent of getOption() get.lsm.option = function(x, default = defaults.lsm[[x]]) { opts = getOption("lsmeans", list()) if(is.null(default) || hasName(opts, x)) opts[[x]] else default } ### Exported defaults for certain options defaults.lsm = list( estble.tol = 1e-8, # tolerance for estimability checks lmer.df = "satterth", # Use Satterthwaite method for df disable.pbkrtest = FALSE, # whether to bypass pbkrtest routines for lmerMod pbkrtest.limit = 3000, # limit on N for enabling adj V save.ref.grid = TRUE # save new ref.grid in .Last.ref.grid ) # Utility that returns TRUE if getOption("lsmeans")[[opt]] is TRUE .lsm.is.true = function(opt) { x = get.lsm.option(opt) if (is.logical(x)) x else FALSE } ### Utility to change the internal structure of a ref.grid ### Returned ref.grid object has linfct = I and bhat = estimates ### Primary reason to do this is with transform = TRUE, then can ### work with linear functions of the transformed predictions regrid = function(object, transform = c("response", "log", "none"), inv.log.lbl = "response", predict.type) { if (is.logical(transform)) # for backward-compatibility transform = ifelse(transform, "response", "none") else transform = match.arg(transform) est = .est.se.df(object, do.se = TRUE) ###FALSE) estble = !(is.na(est[[1]])) object@V = vcov(object)[estble, estble, drop=FALSE] object@bhat = est[[1]] object@linfct = diag(1, length(estble)) if(all(estble)) object@nbasis = estimability::all.estble else object@nbasis = object@linfct[, !estble, drop = FALSE] # override the df function df = est$df test.df = diff(range(df)) if (is.na(test.df) || test.df < .001) { object@dfargs = list(df = mean(df)) object@dffun = function(k, dfargs) dfargs$df } else { # use containment df object@dfargs = list(df = df) object@dffun = function(k, dfargs) { idx = which(zapsmall(k) != 0) ifelse(length(idx) == 0, NA, min(dfargs$df[idx])) } } if(transform %in% c("response", "log") && !is.null(object@misc$tran)) { link = attr(est, "link") D = .diag(link$mu.eta(object@bhat[estble])) object@bhat = link$linkinv(object@bhat) object@V = D %*% tcrossprod(object@V, D) inm = object@misc$inv.lbl if (!is.null(inm)) object@misc$estName = inm object@misc$tran = object@misc$tran.mult = object@misc$inv.lbl = NULL } if (transform == "log") { # from prev block, we now have stuff on response scale incl = which(object@bhat > 0) if (length(incl) < length(object@bhat)) { message("Non-positive response predictions are flagged as non-estimable") tmp = seq_along(object@bhat) excl = tmp[-incl] object@bhat[excl] = NA object@nbasis = sapply(excl, function(ii) 0 + (tmp == ii)) } D = .diag(1/object@bhat[incl]) object@V = D %*% tcrossprod(object@V[incl, incl, drop = FALSE], D) object@bhat = log(object@bhat) object@misc$tran = "log" object@misc$inv.lbl = inv.log.lbl } # Nix out things that are no longer needed or valid object@grid$.offset. = object@misc$offset.mult = object@misc$estHook = object@misc$vcovHook = NULL if(!missing(predict.type)) object = update(object, predict.type = predict.type) object } ### S4 methods ## use S3 for this setMethod("summary", "ref.grid", summary.ref.grid) setMethod("show", "ref.grid", function(object) str.ref.grid(object)) lsmeans/R/lsm-contr.R0000644000175100001440000001575113013732335014205 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### functions to implement different families of contrasts ### All return a matrix or data frame whose columns are the desired contrasts coefs ### with appropriate row and column names ### Also they have two attributes: ### "desc" is an expanded description of the family, ### "adjust" is the default multiplicity adjustment (used if adjust="auto" in lsmeans) # all pairwise trt[i] - trt[j], i < j pairwise.lsmc = function(levs,...) { k = length(levs) M = data.frame(levs=levs) for (i in seq_len(k-1)) { for (j in (i + seq_len(k-i))) { ###for (j in (i+1):k) { con = rep(0,k) con[i] = 1 con[j] = -1 nm = paste(levs[i], levs[j], sep = " - ") M[[nm]] = con } } row.names(M) = levs M = M[-1] attr(M, "desc") = "pairwise differences" attr(M, "adjust") = "tukey" attr(M, "type") = "pairs" M } # all pairwise trt[j] - trt[i], j > i revpairwise.lsmc = function(levs,...) { k = length(levs) M = data.frame(levs=levs) for (i in 2:k) { for (j in seq_len(i-1)) { con = rep(0,k) con[i] = 1 con[j] = -1 nm = paste(levs[i], levs[j], sep = " - ") M[[nm]] = con } } row.names(M) = levs M = M[-1] attr(M, "desc") = "pairwise differences" attr(M, "adjust") = "tukey" attr(M, "type") = "pairs" M } # pseudonym tukey.lsmc = function(levs, reverse = FALSE) { if (reverse) revpairwise.lsmc(levs) else pairwise.lsmc(levs) } # Poly contrasts - scaled w/ integer levels like most tables # ad hoc scaling works for up to 13 levels poly.lsmc = function(levs, max.degree=min(6,k-1)) { nm = c("linear", "quadratic", "cubic", "quartic", paste("degree",5:20)) k = length(levs) M = as.data.frame(poly(seq_len(k), min(20,max.degree))) for (j in seq_len(ncol(M))) { con = M[ ,j] pos = which(con > .01) con = con / min(con[pos]) z = max(abs(con - round(con))) while (z > .05) { con = con / z z = max(abs(con - round(con))) } M[ ,j] = round(con) } row.names(M) = levs names(M) = nm[seq_len(ncol(M))] attr(M, "desc") = "polynomial contrasts" attr(M, "adjust") = "none" M } # All comparisons with a control; ref = index of control group # New version -- allows more than one control group (ref is a vector) trt.vs.ctrl.lsmc = function(levs, ref=1) { if ((min(ref) < 1) || (max(ref) > length(levs))) stop("Reference levels are out of range") k = length(levs) cnm = ifelse(length(ref)==1, levs[ref], paste("avg(", paste(levs[ref], collapse=","), ")", sep="")) templ = rep(0, length(levs)) templ[ref] = -1 / length(ref) M = data.frame(levs=levs) for (i in seq_len(k)) { if (i %in% ref) next con = templ con[i] = 1 nm = paste(levs[i], cnm, sep = " - ") M[[nm]] = con } row.names(M) = levs M = M[-1] attr(M, "desc") = "differences from control" attr(M, "adjust") = "dunnettx" M } # control is 1st level trt.vs.ctrl1.lsmc = function(levs, ...) { trt.vs.ctrl.lsmc(levs, ref = 1) } # control is last level trt.vs.ctrlk.lsmc = function(levs, ...) { trt.vs.ctrl.lsmc(levs, ref = length(levs)) } # pseudonym dunnett.lsmc = function(levs, ref = 1) { trt.vs.ctrl.lsmc(levs, ref = ref) } # effects contrasts. Each mean versus the average of all eff.lsmc = function(levs, ...) { k = length(levs) M = data.frame(levs=levs) for (i in seq_len(k)) { con = rep(-1/k, k) con[i] = (k-1)/k nm = paste(levs[i], "effect") M[[nm]] = con } row.names(M) = levs M = M[-1] attr(M, "desc") = "differences from grand mean" attr(M, "adjust") = "fdr" M } # "deleted" effects contrasts. # Each mean versus the average of all others del.eff.lsmc = function(levs, ...) { k = length(levs) M = as.matrix(eff.lsmc(levs,...)) * k / (k-1) M = as.data.frame(M) attr(M, "desc") = "differences from mean of others" attr(M, "adjust") = "fdr" M } # Contrasts to compare consecutive levels: # (-1,1,0,0,...), (0,-1,1,0,...), ..., (0,...0,-1,1) consec.lsmc = function(levs, reverse = FALSE, ...) { sgn = ifelse(reverse, -1, 1) k = length(levs) M = data.frame(levs=levs) for (i in seq_len(k-1)) { con = rep(0, k) con[i] = -sgn con[i+1] = sgn nm = ifelse(reverse, paste(levs[i], "-", levs[i+1]), paste(levs[i+1], "-", levs[i])) M[[nm]] = con } row.names(M) = levs M = M[-1] attr(M, "desc") = "changes between consecutive levels" attr(M, "adjust") = "mvt" M } # Mean after minus mean before # e.g., (-1, 1/3,1/3,1/3), (-1/2,-1/2, 1/2,1/2), (-1/3,-1/3,-1/3, 1) mean_chg.lsmc = function(levs, reverse = FALSE, ...) { sgn = ifelse(reverse, -1, 1) k = length(levs) M = data.frame(levs=levs) for (i in seq_len(k-1)) { kmi = k - i con = rep(c(-sgn/i, sgn/kmi), c(i, kmi)) nm = paste(levs[i], levs[i+1], sep="|") M[[nm]] = con } row.names(M) = levs M = M[-1] attr(M, "desc") = "mean after minus mean before" attr(M, "adjust") = "mvt" M } lsmeans/R/rbind.R0000644000175100001440000000712213013732335013356 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # rbind method for ref.grid objects rbind.ref.grid = function(..., deparse.level = 1, adjust = "mvt") { objs = list(...) if (!all(sapply(objs, inherits, "ref.grid"))) stop("All objects must inherit from 'ref.grid'") bhats = lapply(objs, function(o) o@bhat) bhat = bhats[[1]] if(!all(sapply(bhats, function(b) (length(b) == length(bhat)) && (sum((b - bhat)^2, na.rm = TRUE) == 0)))) stop("All objects must have the same fixed effects") Vs = lapply(objs, function(o) o@V) V = Vs[[1]] if(!all(sapply(Vs, function(v) sum((v - V)^2) == 0))) stop("All objects must have the same covariances") obj = objs[[1]] linfcts = lapply(objs, function(o) o@linfct) obj@linfct = do.call(rbind, linfcts) bnms = unlist(lapply(objs, function(o) o@misc$by.vars)) grids = lapply(objs, function(o) o@grid) gnms = unique(c(bnms, unlist(lapply(grids, names)))) gnms = setdiff(gnms, c(".wgt.", ".offset.")) # exclude special names grid = data.frame(.tmp. = seq_len(n <- nrow(obj@linfct))) for (g in gnms) grid[[g]] = rep(".", n) grid$.tmp. = NULL n.before = 0 for (g in grids) { rows = n.before + seq_along(g[[1]]) n.before = max(rows) for (nm in names(g)) grid[rows, nm] = as.character(g[[nm]]) } avgd.over = unique(unlist(lapply(objs, function(o) o@misc$avgd.over))) attr(avgd.over, "qualifier") = " some or all of" obj@grid = grid update(obj, pri.vars = gnms, by.vars = NULL, adjust = adjust, famSize = round((1 + sqrt(1 + 8*n)) / 2, 3), estType = "contrast", infer = c(FALSE, TRUE), avgd.over = avgd.over) } ### Subset a reference grid "[.ref.grid" = function(x, i, adjust = "mvt", ...) { x@linfct = x@linfct[i, , drop=FALSE] x@grid = x@grid[i, , drop = FALSE] x = update(x, pri.vars = names(x@grid), famSize = 2, adjust = adjust) x@misc$by.vars = NULL x } lsmeans/R/MCMC-support.R0000644000175100001440000001377213013732335014521 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # Support for MCMCglmm class and possibly more MCMC-based models # Method to create a coda 'mcmc' object from a ref.grid # (dots not supported, unfortunately) as.mcmc.ref.grid = function(x, names = TRUE, ...) { object = x if (is.na(x@post.beta[1])) stop("No posterior sample -- can't make an 'mcmc' object") mat = x@post.beta %*% t(x@linfct) nm = setdiff(names(x@grid), c(".wgt.",".offset.")) if (any(names)) { names = rep(names, length(nm)) for (i in seq_along(nm)) if(names[i]) x@grid[nm[i]] = paste(nm[i], x@grid[[nm[i]]]) } dimnames(mat)[[2]] = do.call(paste, c(x@grid[, nm, drop = FALSE], sep=", ")) coda::mcmc(mat, ...) } # Currently, data is required, as call is not stored recover.data.MCMCglmm = function(object, data, ...) { # if a multivariate response, stack the data with `trait` variable yvars = .all.vars(update(object$Fixed$formula, ". ~ 1")) if(length(yvars) > 1) { # for (v in yvars) data[[v]] = NULL dat = data for (i in seq_len(length(yvars) - 1)) data = rbind(data, dat) data$trait = factor(rep(yvars, each = nrow(dat))) } attr(data, "call") = object$Fixed attr(data, "terms") = trms = delete.response(terms(object$Fixed$formula)) attr(data, "predictors") = .all.vars(delete.response(trms)) attr(data, "responses") = yvars data } lsm.basis.MCMCglmm = function(object, trms, xlev, grid, vcov., ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = NULL) Sol = as.matrix(object$Sol)[, seq_len(object$Fixed$nfl)] # toss out random effects if included bhat = apply(Sol, 2, mean) if (missing(vcov.)) V = cov(Sol) else V = .my.vcov(object, vcov.) misc = list() list(X = X, bhat = bhat, nbasis = matrix(NA), V = V, dffun = function(k, dfargs) NA, dfargs = list(), misc = misc, post.beta = Sol) } ### Support for MCMCpack , maybe others that produce mcmc objects ### Whether it works depends on: ### 1. if there is a "call" attribute with a formula or fixed member ### 2. if it's right, even then ### Alternatively, maybe providing formula and data will do the trick recover.data.mcmc = function(object, formula, data, ...) { if (missing(formula)) { cl = attr(object, "call") if (is.null(cl$formula)) cl$formula = cl$fixed if (is.null(cl$formula)) return("No fixed-effects formula found") data = NULL } else { if (missing(formula) || missing(data)) return("Requires both formula and data to proceed") cl = call("mcmc.proxy", formula = formula, data = quote(data)) } trms = delete.response(terms(eval(cl$formula, parent.frame()))) recover.data(cl, trms, NULL, data, ...) } lsm.basis.mcmc = function(object, trms, xlev, grid, vcov., ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = NULL) samp = as.matrix(object)[, seq_len(ncol(X)), drop = FALSE] bhat = apply(samp, 2, mean) if (missing(vcov.)) V = cov(samp) else V = .my.vcov(object, vcov.) misc = list() list(X = X, bhat = bhat, nbasis = matrix(NA), V = V, dffun = function(k, dfargs) NA, dfargs = list(), misc = misc, post.beta = samp) } ### support for CARBayes package - currently MUST supply data and have ### default contrasts matching what was used in fitting the mdoel recover.data.carbayes = function(object, data, ...) { if(is.null(data)) # Try to recover data from parent frame data = model.frame(object$formula, data = parent.frame()) cl = call("carbayes.proxy", formula = object$formula, data = quote(data)) trms = delete.response(terms(eval(object$formula, parent.frame()))) recover.data(cl, trms, NULL, data, ...) } lsm.basis.carbayes = function(object, trms, xlev, grid, ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = attr(object$X, "contrasts")) samp = as.matrix(object$samples$beta) bhat = apply(samp, 2, mean) V = cov(samp) misc = list() list(X = X, bhat = bhat, nbasis = matrix(NA), V = V, dffun = function(k, dfargs) NA, dfargs = list(), misc = misc, post.beta = samp) } lsmeans/R/S4-classes.R0000644000175100001440000000547313013732335014210 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### S4 class definitions for lsmeans package ### ref.grid object -- for a reference grid setClass("ref.grid", slots = c( model.info = "list", roles = "list", grid = "data.frame", levels = "list", matlevs = "list", linfct = "matrix", bhat = "numeric", nbasis = "matrix", V = "matrix", dffun = "function", dfargs = "list", misc = "list", post.beta = "matrix" )) # Note: misc will hold various extra params, # including at least the following req'd by the summary method # estName: column name for the estimate in the summary ["prediction"] # infer: booleans (CIs?, tests?) [(FALSE,FALSE)] # level: default conf level [.95] # adjust: default adjust method ["none"] # famSize: number of means in family ### lsmobj class -- almost trivial ext of ref.grid, structurally # But origin can be very different from those of a reference grid # In general its 'grid' will correspond to some set of # linear functions of grid points setClass("lsmobj", contains="ref.grid") # ### lstobj class -- for objects from lstrends # # Additional slot 'deriv' holds the values from mu.eta() from the associated lsmeans # setClass("lstobj", slots = c(deriv = "numeric"), contains = "lsmobj") lsmeans/R/zzz.R0000644000175100001440000000421513013732335013115 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # Namespace hooks for lsmeans # .onLoad = function(libname, pkgname) { # # Function to check for existence of a variable # # This will be in the base package of R > 3.3.0 # if (!exists("hasName", envir = getNamespace("utils"), inherits = FALSE)) { # assign("hasName", function(x, name) # match(name, names(x), nomatch = 0L) > 0L, # envir = getNamespace(pkgname)) # } # } # Just define the function for now. Maybe in a year we require R >= 3.4 hasName = function(x, name) match(name, names(x), nomatch = 0L) > 0L lsmeans/R/multinom-support.R0000644000175100001440000001003113013732335015627 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## ### Multinomial modeling ### Example for testing ### From: http://www.ats.ucla.edu/stat/r/dae/mlogit.htm # library(foreign) # ml <- read.dta("http://www.ats.ucla.edu/stat/data/hsbdemo.dta") # library(nnet) # ml$prog2 <- relevel(ml$prog, ref = "academic") # test <- multinom(prog2 ~ ses + write, data = ml) # # same as recover.data.lm recover.data.multinom = function(object, ...) { fcall = object$call recover.data(fcall, delete.response(terms(object)), object$na.action, ...) } lsm.basis.multinom = function(object, trms, xlev, grid, mode = c("prob", "latent"), ...) { mode = match.arg(mode) bhat = t(coef(object)) V = .my.vcov(object, ...) k = ncol(bhat) m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) # recenter for latent predictions pat = (rbind(0, diag(k + 1, k)) - 1) / (k + 1) X = kronecker(pat, X) nbasis = estimability::all.estble nbasis = kronecker(rep(1,k), nbasis) misc = list(tran = "log", inv.lbl = "e^y") dfargs = list(df = object$edf) dffun = function(k, dfargs) dfargs$df ylevs = list(class = object$lev) if (is.null(ylevs)) ylevs = list(class = seq_len(k)) names(ylevs) = as.character(object$call$formula[[2]]) misc$ylevs = ylevs if (mode == "prob") misc$postGridHook = .multinom.postGrid list(X = X, bhat = as.numeric(bhat), nbasis = nbasis, V = V, dffun = dffun, dfargs = dfargs, misc = misc) } # post-processing of ref.grid for "prob" mode .multinom.postGrid = function(object) { # will replace portions of these as we go bhat = object@bhat linfct = object@linfct misc = object@misc # grid will have multresp as slowest-varying factor... idx = matrix(seq_along(linfct[, 1]), ncol = length(object@levels[[object@roles$multresp]])) for (i in 1:nrow(idx)) { rows = idx[i, ] exp.psi = exp(linfct[rows, , drop = FALSE] %*% object@bhat) p = as.numeric(exp.psi / sum(exp.psi)) bhat[rows] = p A = .diag(p) - outer(p, p) # partial derivs linfct[rows, ] = A %*% linfct[rows, ] } misc$postGridHook = misc$tran = misc$inv.lbl = NULL misc$estName = "prob" object@bhat = bhat object@V = linfct %*% tcrossprod(object@V, linfct) object@linfct = diag(1, length(bhat)) object@misc = misc object }lsmeans/R/nonlin-support.R0000644000175100001440000001023713013732335015270 0ustar hornikusers############################################################################## # Copyright (c) 2012-2016 Russell V. Lenth # # # # This file is part of the lsmeans package for R (*lsmeans*) # # # # *lsmeans* is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 2 of the License, or # # (at your option) any later version. # # # # *lsmeans* is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with R and *lsmeans*. If not, see # # and/or # # . # ############################################################################## # experimental support for nls, nlme objects recover.data.nls = function(object, ...) { fcall = object$call trms = terms(reformulate(names(object$dataClasses))) recover.data(fcall, trms, object$na.action, ...) } lsm.basis.nls = function(object, trms, xlev, grid, ...) { Vbeta = .my.vcov(object, ...) env = object$m$getEnv() for (nm in names(grid)) env[[nm]] = grid[[nm]] pars = object$m$getAllPars() DD = deriv(object$m$formula(), names(pars)) ests = eval(DD, env) bhat = as.numeric(ests) grad = attr(ests, "gradient") V = grad %*% Vbeta %*% t(grad) X = diag(1, nrow(grid)) list(X=X, bhat=bhat, nbasis=all.estble, V=V, dffun=function(k, dfargs) NA, dfargs=list(), misc=list()) } ### For nlme objects, we can do stuff with the fixed part of the model ### Additional REQUIRED argument is 'param' - parameter name to explore recover.data.nlme = function(object, param, ...) { if(missing(param)) return("'param' argument is required for nlme objects") fcall = object$call if (!is.null(fcall$weights)) fcall$weights = nlme::varWeights(object$modelStruct) fixed = fcall$fixed if (is.call(fixed)) fixed = eval(fixed, envir = parent.frame()) if(!is.list(fixed)) fixed = list(fixed) form = NULL for (x in fixed) if (param %in% all.names(x)) form = x if (is.null(form)) return(paste("Can't find '", param, "' among the fixed parameters", sep = "")) fcall$weights = NULL #trms = delete.response(terms(update(terms(object), form))) trms = delete.response(terms(form)) if (length(.all.vars(trms)) == 0) return(paste("No predictors for '", param, "' in fixed model", sep = "")) recover.data(fcall, trms, object$na.action, ...) } lsm.basis.nlme = function(object, trms, xlev, grid, param, ...) { idx = object$map$fmap[[param]] V = object$varFix[idx, idx, drop = FALSE] bhat = object$coefficients$fixed[idx] contr = attr(object$plist[[param]]$fixed, "contrasts") m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = contr) dfx = object$fixDF$X[idx] dfx[1] = min(dfx) # I'm assuming 1st one is intercept dffun = function(k, dfargs) { # containment df idx = which(abs(k) > 1e-6) ifelse(length(idx) > 0, min(dfargs$dfx[idx]), NA) } list(X = X, bhat = bhat, nbasis = estimability::all.estble, V = V, dffun = dffun, dfargs = list(dfx = dfx), misc = list(estName = param)) } lsmeans/vignettes/0000755000175100001440000000000013013732364013744 5ustar hornikuserslsmeans/vignettes/lsmeans.bib0000644000175100001440000001613113013732335016064 0ustar hornikusers;;; .bib file for lsmeans @book{Box05, author = "Box, G. and Hunter, J. and Hunter, W.", year = 2005, title = "Statistics for Experimenters", edition = "2nd", publisher = "Wiley" } @article{Bra08, author = "Brader, T. and Valentino, N. and Suhay, E.", year = 2008, title = "What triggers public opposition to immigration? Anxiety, group cues, and immigration threat", journal = "American Journal of Political Science", volume = 52, number = 4, pages = "959-978" } @inproceedings{Har76, author = "Harvey, W.", year = 1976, title = "Use of the {HARVEY} Procedure", booktitle = "{SUGI} Proceedings", url = "http://www.sascommunity.org/sugi/SUGI76/Sugi-76-20%20Harvey.pdf" } @techreport{Har60, author = "W. Harvey", year = 1960, title = "Least-squares analysis of data with unequal subclass numbers", institution = "{USDA} National Agricultural Library", number = "ARS-20-8" } @manual{Har77, author = "Harvey, W. R.", year = 1977, title = "User's guide for {LSML}~76. Mixed model least-squares and maximum likelihood computer program", organization = "Ohio State University" } @techreport{Goo97, author = "J. H. Goodnight and W. R. Harvey", year = 1997, title = "Least squares means in the fixed effects general model", number = "{SAS} Technical Report R-103", institution = "{SAS} Institute Inc." } @article{Sea80, author = "S. R. Searle and F. M. Speed and G. A. Milliken", year = "1980", title = "Population marginal means in the linear model: An alternative to least squares means", journal = "The American Statistician", volume = 34, number = 4, pages = "216-221" } @article{Les04, author = "Lesnoff, M. and Laval, G. and Bonnet, P. and Abdicho, S. and Workalemahu, A. and Kifle, D. and Peyraud, A. and Lancelot, R. and Thiaucourt, F.", year = 2004, title = "Within-Herd Spread of Contagious Bovine Pleuropneumonia in {Ethiopian} Highlands", journal = "Preventive Veterinary Medicine", volume = 64, pages = "27-40" } @book{Mil92, author = "Milliken, G. A. and Johnson, D. E.", year = 1992, title = "Analysis of Messy Data -- Volume I: Designed Experiments", publisher = "Chapman \&{} Hall/CRC", isbn = "0-412-99081-4" } @book{Oeh00, author = "Oehlert, G.", year = 2000, title = "A First Course in Design and Analysis of Experiments", publisher = "{W.~H.~Freeman}" } ;;This is out-of-print, but now available under a Creative Commons license via \url{http://users.stat.umn.edu/~gary/Book.html} (accessed August 23, 2012). @inproceedings{SAS12, author= "{SAS Institute Inc.}", year = 2012, title = "\code{LSMEANS} Statement", booktitle = "{\proglang{SAS/STAT}(R)} 9.3 User's Guide", url = "http://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#statug_introcom_a0000003362.htm" } @article{Sch87, author = "Schuirmann, Donald", year = 1987, title = "A Comparison of the Two One-Sided Tests Procedure and the Power Approach for Assessing the Equivalence of Average Bioavailability", journal = "Journal of Pharmacokinetics and Biopharmaceutics", volume = 15, number = 6, pages = "657-680" } ;;\item[Tukey, J.~W.~(1977)] \emph{Exploratory Data Analysis}. Addison-Wesley. @article{urq82, author = "N. Scott Urquhart", year = 1982, title = "Adjustment in covariates when one factor affects the covariate", journal = "Biometrics", volume = 38, pages = "651-660" } @article{Yat35, author = "Yates, F", year = 1935, title = "Complex Experiments", journal = "{Journal of the Royal Statistical Society} (Supplement)", volume = 2, pages = "181-247" } ;;; R packages... @article{effects, author = "John Fox", year = 2003, title = "Effect Displays in \proglang{R} for Generalised Linear Models", journal = "{Journal of Statistical Software}", volume = 8, number = 15, pages = "1-27", url = "http://www.jstatsoft.org/v08/i15/" } @article{fox09, author = "John Fox and Jangman Hong", year = 2009, title = "Effect Displays in \proglang{R} for Multinomial and Proportional-Odds Logit Models: Extensions to the \pkg{effects} Package", journal = "{Journal of Statistical Software}", volume = 32, number = 1, pages = "1-24", url = "http://www.jstatsoft.org/v32/i01/" } @Manual{doBy, title = {\pkg{doBy}: Groupwise summary statistics, LSmeans, general linear contrasts, various utilities}, author = {S{\o}ren H{\o}jsgaard and Ulrich Halekoh and Jim Robison-Cox and Kevin Wright and Alessandro A. Leidi}, year = {2013}, note = {\R{} package version 4.5-10}, url = {http://CRAN.R-project.org/package=doBy}, } @Manual{lsmea, title = {\pkg{lsmeans}: Least-Squares Means}, author = {Russell V. Lenth}, year = {2014}, note = {\R{} package version 2.10} } @Manual{lme4, title = {\pkg{lme4}: Linear Mixed-effects Models Using \pkg{Eigen} and \proglang{S4}}, author = {Douglas Bates and Martin Maechler and Ben Bolker and Steven Walker}, year = {2013}, note = {\R{} package version 1.1-0}, url = {http://lme4.r-forge.r-project.org/}, } @Manual{pbkrt, title = {\pkg{pbkrtest}: Parametric Bootstrap and Kenward Roger Based Methods for Mixed Model Comparison}, author = {Ulrich Halekoh and S{\o}ren H{\o}jsgaard}, year = {2013}, note = {\R{} package version 0.3-8}, url = {http://CRAN.R-project.org/package=pbkrtest}, } @Manual{multc, title = {\pkg{multcomp}: Simultaneous Inference in General Parametric Models}, author = {Torsten Hothorn and Frank Bretz and Peter Westfall}, year = {2013}, note = {\R{} package version 1.3-1}, url = {http://CRAN.R-project.org/package=multcomp}, } @Manual{mcview, title = {\pkg{multcompView}: Visualizations of Paired Comparisons}, author = {Spencer Graves and Hans-Peter Piepho and Luciano Selzer and Sundar Dorai-Raj}, year = {2012}, note = {\R{} package version 0.1-5}, url = {http://CRAN.R-project.org/package=multcompView}, } @Manual{latti, title = {\pkg{lattice}: Lattice Graphics}, author = {Deepayan Sarkar}, year = {2013}, note = {\R{} package version 0.20-24}, url = {http://CRAN.R-project.org/package=lattice}, } @Manual{lmert, title = {lmerTest: Tests in Linear Mixed Effects Models}, author = {Alexandra Kuznetsova and Per {Bruun Brockhoff} and Rune {Haubo Bojesen Christensen}}, year = {2016}, note = {R package version 2.0-32}, url = {https://CRAN.R-project.org/package=lmerTest}, } @Manual{nlme, title = {\pkg{nlme}: Linear and Nonlinear Mixed Effects Models}, author = {Jos? Pinheiro and Douglas Bates and {R-core}}, year = {2013}, note = {\R{} package version 3.1-113}, url = {http://CRAN.R-project.org/package=nlme}, } @Manual{xtable, title = {\pkg{xtable}: Export Tables to LaTeX or HTML}, author = {David B. Dahl}, year = {2016}, note = {R package version 1.8-2/r110}, url = {https://R-Forge.R-project.org/projects/xtable/}, } lsmeans/vignettes/extending.rnw0000644000175100001440000006213713013732335016470 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % % % \documentclass[11pt]{article} \usepackage[margin=1in]{geometry} \usepackage{mathpazo} \usepackage{fancyvrb} \usepackage{natbib} \usepackage{hyperref} \let\dq=" \DefineShortVerb{\"} \def\pkg#1{\textsf{#1}} \def\lsm{\pkg{lsmeans}} \def\code{\texttt} \def\proglang{\textsf} % double-quoted text \def\dqt#1{\code{\dq{}#1\dq{}}} % The objects I want to talk about \def\rg{\dqt{ref.grid}} \def\lsmo{\dqt{lsmobj}} \def\R{\proglang{R}} \def\SAS{\proglang{SAS}} \def\Fig#1{Figure~\ref{#1}} \def\bottomfraction{.5} \title{Extending \lsm{}} \author{Russell V.~Lenth} %\VignetteIndexEntry{Extending lsmeans} %\VignetteDepends{lsmeans} %\VignetteKeywords{least-squares means} %\VignettePackage{lsmeans} % Initialization <>= options(show.signif.stars=FALSE, prompt="R> ", continue=" ") set.seed(271828) @ \begin{document} \SweaveOpts{concordance=TRUE} \maketitle{} \section{Introduction} Suppose you want to use \lsm{} for some type of model that it doesn't (yet) support. Or, suppose you have developed a new package with a fancy model-fitting function, and you'd like it to work with \lsm{}. What can you do? Well, there is hope because \lsm{} is designed to be extended. The first thing to do is to look at the help page for extending the package: <>= help("extending-lsmeans", package="lsmeans") @ It gives details about the fact that you need to write two S3 methods, "recover.data" and "lsm.basis", for the class of object that your model-fitting function returns. The "recover.data" method is needed to recreate the dataset so that the reference grid can be identified. The "lsm.basis" method then determines the linear functions needed to evaluate each point in the reference grid and to obtain associated information---such as the variance-covariance matrix---needed to do estimation and testing. This vignette presents an example where suitable methods are developed, and discusses a few issues that arise. \section{Data example} The \pkg{MASS} package contains various functions that do robust or outlier-resistant model fitting. We will cobble together some \lsm{} support for these. But first, let's create a suitable dataset (a simulated two-factor experiment) for testing.\footnote{I unapologetically use \code{=} as the assignment operator. It is good enough for C and Java, and supported by R.} <<>>= fake = expand.grid(rep = 1:5, A = c("a1","a2"), B = c("b1","b2","b3")) fake$y = c(11.46,12.93,11.87,11.01,11.92,17.80,13.41,13.96,14.27,15.82, 23.14,23.75,-2.09,28.43,23.01,24.11,25.51,24.11,23.95,30.37, 17.75,18.28,17.82,18.52,16.33,20.58,20.55,20.77,21.21,20.10) @ The $y$ values were generated using predetermined means and Cauchy-distributed errors. There are some serious outliers in these data. \section{Supporting \code{rlm}} The \pkg{MASS} package provides an "rlm" function that fits robust-regression models using $M$~estimation. We'll fit a model using the default settings for all tuning parameters: <<>>= library(MASS) fake.rlm = rlm(y ~ A * B, data = fake) library(lsmeans) lsmeans(fake.rlm, ~B | A) @ The first lesson to learn about extending \lsm{} is that sometimes, it already works! It works here because "rlm" objects inherit from "lm", which is supported by the \lsm{} package, and "rlm" objects aren't enough different to create any problems. \section{Supporting \code{lqs} objects} The \pkg{MASS} resistant-regression functions "lqs", "lmsreg", and "ltsreg" are another story, however. They create "lqs" objects that are not extensions of any other class, and have other issues, including not even having a "vcov" method. So for these, we really do need to write new methods for "lqs" objects. First, let's fit a model. <<>>= fake.lts = ltsreg(y ~ A * B, data = fake) @ \subsection{The \code{recover.data} method} It is usually an easy matter to write a "recover.data" method. Look at the one for "lm" objects: <<>>= lsmeans:::recover.data.lm @ Note that all it does is obtain the "call" component and call the method for class \dqt{call}, with additional arguments for its "terms" component and "na.action". It happens that we can access these attributes in exactly the same way as for "lm" objects; so, \ldots <<>>= recover.data.lqs = lsmeans:::recover.data.lm @ Let's test it: <<>>= rec.fake = recover.data(fake.lts) head(rec.fake) @ Our recovered data excludes the response variable "y" (owing to the "delete.response" call), and this is fine. \paragraph{Special arguments} By the way, there are two special arguments "data" and "params" that may be handed to "recover.data" via "ref.grid" or "lsmeans" or a related function; and you may need to provide for if you don't use the "recover.data.call" function. The "data" argument is needed to cover a desperate situation that occurs with certain kinds of models where the underlying data information is not saved with the object---e.g., models that are fitted by iteratively modifying the data. In those cases, the only way to recover the data is to for the user to give it explicitly, and "recover.data" just adds a few needed attributes to it. The "params" argument is needed when the model formula refers to variables besides predictors. For example, a model may include a spline term, and the knots are saved in the user's environment as a vector and referred to in the call to fit the model. In trying to recover the data, we try to construct a data frame containing all the variables present on the right-hand side of the model, but if some of those are scalars or of different lengths than the number of observations, an error occurs. So you need to exclude any names in "params" when reconstructing the data. \paragraph{Error handling} If you check for any error conditions in "recover.data", simply have it return a character string with the desired message, rather than invoking "stop". This provides a cleaner exit. The reason is that whenever "recover.data" throws an error, an informative message suggesting that "data" or "params" be provided is displayed. But a character return value is tested for and throws a different error with your string as the message. \subsection{The \code{lsm.basis} method} The "lsm.basis" method has four required arguments: <<>>= args(lsmeans:::lsm.basis.lm) @ These are, respectively, the model object, its "terms" component (at least for the right-hand side of the model), a "list" of levels of the factors, and the grid of predictor combinations that specify the reference grid. The function must obtain six things and return them in a named "list". They are the matrix "X" of linear functions for each point in the reference grid, the regression coefficients "bhat"; the variance-covariance matrix "V"; a matrix "nbasis" for non-estimable functions; a function "dffun(k,dfargs)" for computing degrees of freedom for the linear function "sum(k*bhat)"; and a list "dfargs" of arguments to pass to "dffun". To write your own "lsm.basis" function, examining some of the existing methods can help; but the best resource is the "predict" method for the object in question, looking carefully to see what it does to predict values for a new set of predictors (e.g., "newdata" in "predict.lm"). Following this advice, let's take a look at it: <<>>= MASS:::predict.lqs @ \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=left} Based on this, here is a listing of an "lsm.basis" method for "lqs" objects: <<>>= lsm.basis.lqs = function(object, trms, xlev, grid, ...) { m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) bhat = coef(object) Xmat = model.matrix(trms, data=object$model) V = rev(object$scale)[1]^2 * solve(t(Xmat) %*% Xmat) nbasis = matrix(NA) dfargs = list(df = nrow(Xmat) - ncol(Xmat)) dffun = function(k, dfargs) dfargs$df list(X=X, bhat=bhat, nbasis=nbasis, V=V, dffun=dffun, dfargs=dfargs) } @ \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=none} Before explaining it, let's verify that it works: <<>>= lsmeans(fake.lts, ~ B | A) @ Hooray! Note the results are comparable to those we had for "fake.rlm", albeit the standard errors are quite a bit smaller. (In fact, the SEs could be misleading; a better method for estimating covariances should probably be implemented, but that is beyond the scope of this vignette.) \subsection{Dissecting \code{lsm.basis.lqs}} Let's go through the listing of this method, by line numbers. \begin{itemize} \item[2--3:] Construct the linear functions, "X". This is a pretty standard standard two-step process: First obtain a model frame, "m", for the grid of predictors, then pass it as data to "model.data" to create the associated design matrix. As promised, this code is essentially identical to what you find in "predict.lqs". \item[4:] Obtain the coefficients, "bhat". Most model objects have a "coef" method. \item[5--6:] Obtain the covariance matrix, "V", of "bhat". In many models, this can be obtained using the object's "vcov" method. But not in this case. Instead, I cobbled one together using what it would be for ordinary regression: $\hat\sigma^2(\mathbf{X}'\mathbf{X})^{-1}$, where $\mathbf{X}$ is the design matrix for the whole dataset (not the reference grid). Here, $\hat\sigma$ is obtained using the last element of the "scale" element of the object (depending on the method, there are one or two scale estimates). This probably under-estimates the variances and distorts the covariances, because robust estimators have some efficiency loss. \item[7:] Compute the basis for non-estimable functions. This applies only when there is a possibility of rank deficiency in the model, and "lqs" methods cannot handle that. All linear functions are estimable, and we signal that by setting "nbasis" equal to a $1\times1$ matrix of "NA". If rank deficiency were possible, the \pkg{estimability} package (which is required by \lsm{}) provides a "nonest.basis" function that makes this fairly painless---I would have coded: <>= nbasis = estimability::nonest.basis(Xmat) @ On the other hand, if rank-deficient cases are not possible, set "nbasis" equal to "all.estble", a constant in the \pkg{estimability} package. There is a subtlety you need to know regarding estimability. Suppose the model is rank-deficient, so that the design matrix $\mathbf{X}$ has $p$ columns but rank $r>= form = ~ data$x + data[[5]] base::all.vars(form) lsmeans::.all.vars(form) @ The "retain" argument may be used to specify regular expressions for patterns to retain as parts of variable names. \item[\code{.diag(x, nrow, ncol)}] The base "diag" function has a booby trap whereby, for example, "diag(57.6)" returns a $57\times 57$ identity matrix rather than a $1\times1$ matrix with $57.6$ as its only element. But "lsmeans::.diag(57.6)" will return the latter. The function works identically to "diag" except for the identity-matrix trap. \item[\code{.aovlist.dffun(k, dfargs)}] This function is exported because it is needed for computing degrees of freedom for models fitted using "aov", but it may be useful for other cases where Satterthwaite degrees-of-freedom calculations are needed. It requires the "dfargs" slot to contain analogous contents. \item[\code{.get.offset(terms, grid)}] If "terms" is a model formula containing an "offset" call, this is will compute that offset in the context of "grid" (a "data.frame"). <<>>= .get.offset(terms(~ speed + offset(.03*breaks)), head(warpbreaks)) @ \item[\code{.my.vcov(object, ...)}] In a call to "ref.grid", "lsmeans", etc., the user may use "vcov." to specify an alternative function or matrix to use as the covariance matrix of the fixed-effects coefficients. This function supports that feature. Calling ".my.vcov" in place of the "vcov" method will substitute the user's "vcov." when it is present in "...". \end{description} \section{Support for \code{rsm} objects} \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=left, fontsize=\small} As an example of how an existing package supports \pkg{lsmeans}, we show the support offered by the \pkg{rsm} package. Its "rsm" function returns an \dqt{rsm} object which is an extension of the \dqt{lm} class. Part of that extension has to do with "coded.data" structures whereby, as is typical in response-surface analysis, models are fitted to variables that have been linearly transformed (coded) so that $\pm1$ on the coded scale represents the scope of each predictor. Without any extra support in \pkg{rsm}, "lsmeans" will work just fine with \dqt{rsm} objects; but if the data are coded, it becomes awkward to present results in terms of the original predictors on their original, uncoded scale. The "lsmeans"-related methods in \pkg{rsm} provide a "mode" argument that may be used to specify whether we want to work with coded or uncoded data. The possible values for "mode" are \dqt{asis} (ignore any codings, if present), \dqt{coded} (use the coded scale), and \dqt{decoded} (use the decoded scale). The first two are actually the same in that no decoding is done; but it seems clearer to provide separate options because they represent two different situations. \subsection{The \code{recover.data} method} Note that coding is a \emph{predictor} transformation, not a response transformation (we could have that, too, as it's already supported by the \pkg{lsmeans} infrastructure). So, to handle the \dqt{decode} mode, we will need to actually decode the predictors used to construct he reference grid. That means we need to make "recover.data" a lot fancier! Here it is: %%%\begin{figure} <>= recover.data.rsm = function(object, data, mode = c("asis", "coded", "decoded"), ...) { mode = match.arg(mode) cod = codings(object) fcall = object$call if(is.null(data)) data = lsmeans::recover.data(fcall, delete.response(terms(object)), object$na.action, ...) if (!is.null(cod) && (mode == "decoded")) { pred = cpred = attr(data, "predictors") trms = attr(data, "terms") data = decode.data(as.coded.data(data, formulas = cod)) for (form in cod) { vn = all.vars(form) if (!is.na(idx <- grep(vn[1], pred))) { pred[idx] = vn[2] cpred = setdiff(cpred, vn[1]) } } attr(data, "predictors") = pred new.trms = update(trms, reformulate(c("1", cpred))) # excludes coded variables attr(new.trms, "orig") = trms # save orig terms as an attribute attr(data, "terms") = new.trms } data } @ %%%\end{figure} Lines 2--6 ensure that "mode" is legal, retrieves the codings from the object, and obtain the results we would get from "recover.data" had it been an \dqt{lm} object. If "mode" is not \dqt{decoded}, \emph{or} if no codings were used, that's all we need. Otherwise, we need to return the decoded data. However, it isn't quite that simple, because the model equation is still defined on the coded scale. Rather than to try to translate the model coefficients and covariance matrix to the decoded scale, we elected to remember what we will need to do later to put things back on the coded scale. In lines~8--9, we retrieve the attributes of the recovered data that provide the predictor names and "terms" object on the coded scale. In line~10, we replace the recovered data with the decoded data. By the way, the codings comprise a list of formulas with the coded name on the left and the original variable name on the right. It is possible that only some of the predictors are coded (for example, blocking factors will not be). In the "for" loop in lines~11--17, the coded predictor names are replaced with their decoded names. For technical reasons to be discussed later, we also remove these coded predictor names from a copy, "cpred", of the list of all predictors in the coded model. In line~18, the \dqt{predictors} attribute of "data" is replaced with the modified version. Now, there is a nasty technicality. The "ref.grid" function in \pkg{lsmeans} has a few lines of code after "recover.data" is called that determine if any terms in the model convert covariates to factors or vice versa; and this code uses the model formula. That formula involves variables on the coded scale, and those variables are no longer present in the data, so an error will occur if it tries to access them. Luckily, if we simply take those terms out of the formula, it won't hurt because those coded predictors would not have been converted in that way. So in line~19, we update "trms" with a simpler model with the coded variables excluded (the intercept is explicitly included to ensure there will be a right-hand side even is "cpred" is empty). We save that as the \dqt{terms} attribute, and the original terms as a new \dqt{orig} attribute to be retrieved later. The "data" object, modified or not, is returned. If data have been decoded, "ref.grid" will construct its grid using decoded variables. \subsection{The \code{lsm.basis} method} Now comes the "lsm.basis" method that will be called after the grid is defined. It is listed below: <>= lsm.basis.rsm = function(object, trms, xlev, grid, mode = c("asis", "coded", "decoded"), ...) { mode = match.arg(mode) cod = codings(object) if(!is.null(cod) && mode == "decoded") { grid = coded.data(grid, formulas = cod) trms = attr(trms, "orig") # get back the original terms we saved } m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = object$contrasts) bhat = as.numeric(object$coefficients) V = lsmeans::.my.vcov(object, ...) if (sum(is.na(bhat)) > 0) nbasis = estimability::nonest.basis(object$qr) else nbasis = estimability::all.estble dfargs = list(df = object$df.residual) dffun = function(k, dfargs) dfargs$df list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = dfargs, misc = list()) } @ This is much simpler. All we have to do is determine if decoding was done (line~5); and, if so, convert the grid back to the coded scale (line~6) and recover the original \dqt{terms} attribute (line~7). The rest is borrowed directly from the "lsm.basis.lm" method in \pkg{lsmeans}. Note that line~13 uses one of the exported functions we described in the preceding section. Lines 15--18 use functions from the \pkg{estimability} package to handle the possibility that the model is rank-deficient. \subsection{Exporting the methods} \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=none, fontsize=\small} To make the methods available to users of the \pkg{rsm} package, the following code appears in the "NAMESPACE" file: <>= if (requireNamespace("lsmeans", quietly = TRUE)) { importFrom("lsmeans", "recover.data", "lsm.basis") importFrom("estimability", "all.estble", "nonest.basis") S3method(recover.data, rsm) S3method(lsm.basis, rsm) } @ This only has an effect if the user has the \pkg{lsmeans} package installed (in which case \pkg{estimability} is also installed, as it is required); otherwise the code is skipped. We need to import the prototypes for "recover.data" and "lsm.basis", and register our new methods. Also, packages "lsmeans" and "estimability" are included in the "Imports" section of the "DESCRIPTION" file. Alternatively, we could simply export the functions "recover.data.rsm" and "lsm.basis.rsm" without any need to import anything or register methods. It's simpler to do, but makes those functions user-visible and thus they require documentation. \subsection{A demonstration} Here's a demonstration of this new support. The standard example for "rsm" fits a second-order model "CR.rs2" to a dataset organized in two blocks and with two coded predictors. \RecustomVerbatimEnvironment{Sinput}{Verbatim}{numbers=none, fontsize=\normalsize} <>= library("rsm") example("rsm") ### (output is not shown) ### @ First, let's look at some results on the coded scale---which are the same as for an ordinary \dqt{lm} object. <<>>= lsmeans(CR.rs2, ~ x1 * x2, mode = "coded", at = list(x1 = c(-1, 0, 1), x2 = c(-2, 2))) @ Now, the coded variables "x1" and "x2" are derived from these coding formulas for predictors "Time" and "Temp": <<>>= codings(CR.rs1) @ Thus, for example, a coded value of $x_1=1$ corresponds to a time of $85 + 1\times5 = 90$. Here are some results working with decoded predictors. Note that the "at" list must now be given in terms of "Time" and "Temp": <<>>= lsmeans(CR.rs2, ~ Time * Temp, mode = "decoded", at = list(Time = c(80, 85, 90), Temp = c(165, 185))) @ Since the supplied settings are the same on the decoded scale as were used on the coded scale, the LS~means are identical to those in the previous output. \section{Conclusions} It is relatively simple to write appropriate methods that work with \lsm{} for model objects it does not support. I hope this vignette is helpful for understanding how. Furthermore, if you are the developer of a package that fits linear models, I encourage you to include "recover.data" and "lsm.basis" methods for those classes of objects, so that users have access to \pkg{lsmeans} support. \end{document}lsmeans/vignettes/using-lsmeans.rnw0000644000175100001440000022551713013732335017273 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % % % \documentclass[11pt]{article} \usepackage[margin=1in]{geometry} \usepackage{mathpazo} \usepackage{hyperref} \usepackage{fancyvrb} \usepackage{multicol} \usepackage{natbib} \usepackage{Sweave} \usepackage{makeidx} \makeindex \hypersetup{colorlinks=true,allcolors=black,urlcolor=blue} \let\dq=" \DefineShortVerb{\"} \def\pkg{\textbf} \def\proglang{\textsf} \def\lsm{\pkg{lsmeans}} % double-quoted text \def\dqt#1{\code{\dq{}#1\dq{}}} % The objects I want to talk about \def\rg{\dqt{ref.grid}} \def\lsmo{\dqt{lsmobj}} % for use in place of \item in a description env where packages are listed \def\pitem#1{\item[\pkg{#1}]} \def\R{\proglang{R}} \def\SAS{\proglang{SAS}} \def\code{\texttt} \def\Fig#1{Figure~\ref{#1}} \def\bottomfraction{.5} % For indexing... % Naming: \[w]ix[fmt]{#1} -- always use lower-case for alphabetization. % fmt defines format, use w prefix for in-text refs (word is included) \def\ix#1{\index{#1@\MakeUppercase#1}} \def\wix#1{#1\ix{#1}} \def\ixcode#1{\index{#1@\texttt{#1}}} \def\wixcode#1{\texttt{#1}\ixcode{#1}} \def\ixpkg#1{\index{#1@\textbf{#1} package}} \def\wixpkg#1{\textbf{#1}\ixpkg{#1}} % Add subheadings... \def\ixsub#1#2{\index{#1@\MakeUppercase#1!#2}} \def\wixsub#1#2{#1\ixsub{#1}{#2}} \def\ixcodesub#1#2{\index{#1@\texttt{#1}!#2}} \def\wixcodesub#1#2{\texttt{#1}\ixcodesub{#1}{#2}} %\VignetteIndexEntry{Using lsmeans} %\VignetteDepends{lsmeans} %\VignetteKeywords{least-squares means} %\VignettePackage{lsmeans} % Initialization <>= options(show.signif.stars=FALSE, prompt="R> ", continue=" ", useFancyQuotes=FALSE, width=100, digits=6) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% declarations for jss.cls %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% almost as usual \author{Russell V.~Lenth\\The University of Iowa} \title{Using \lsm{}} %% without formatting \ifx %xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx %% for pretty printing and a nice hypersummary also set: \Plainauthor{Russell V.~Lenth} %% comma-separated \Plaintitle{Least-squares Means: The R Package lsmeans} %% without formatting \Shorttitle{The R Package lsmeans} %% a short title (if necessary) \fi %xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx \ifx % IGNORE xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx %% The address of (at least) one author should be given %% in the following format: \Address{ Russell V.~Lenth, Professor Emeritus\\ Department of Statistics and Actuarial Science\\ % 241 Schaeffer Hall\\ The University of Iowa\\ Iowa City, IA 52242 ~ USA\\ E-mail: \email{russell-lenth@uiowa.edu} %\\ % URL: \url{http://www.stat.uiowa.edu/~rlenth/} } %% It is also possible to add a telephone and fax number %% before the e-mail in the following format: %% Telephone: +43/1/31336-5053 %% Fax: +43/1/31336-734 \fi %xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx %% for those who use Sweave please include the following line (with % symbols): %% need no \usepackage{Sweave.sty} %% end of declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{document} \SweaveOpts{concordance=TRUE} \maketitle{} \begin{abstract} Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects of factors, and for testing linear contrasts among predictions. The \lsm{} package provides a simple way of obtaining least-squares means and contrasts thereof. It supports many models fitted by \R{} core packages (as well as a few key contributed ones) that fit linear or mixed models, and provides a simple way of extending it to cover more model classes. \end{abstract} \section{Introduction} \wix{least-squares means} (\wix{LS~means} for short) for a linear model are simply predictions---or averages thereof---over a regular grid of predictor settings which I call the \emph{\wix{reference grid}}. They date back at least to \cite{Har60} and his associated computer program \proglang{LSML} \citep{Har77} and the contributed \SAS{} procedure named \pkg{HARVEY}\index{SAS!PROC HARVEY} \citep{Har76}. Later, they were incorporated via \code{LSMEANS}\index{SAS!LSMEANS} statements for various linear model procedures such as \pkg{GLM} in the regular \SAS{} releases. See also \cite{Goo97} and \cite{SAS12} for more information about the \SAS{} implementation. In simple \wix{analysis-of-covariance models}, LS~means are the same as covariate-\wix{adjusted means}. In unbalanced factorial experiments, LS~means for each factor mimic the main-effects means but are adjusted for imbalance. The latter interpretation is quite similar to the ``\wix{unweighted means}'' method for unbalanced data, as presented in old design books. LS~means are not always well understood, in part because the term itself is confusing. \cite{Sea80} discusses exactly how they are defined for various factorial, nested, and covariance models. \citeauthor{Sea80} suggest the term ``\wix{predicted marginal means}'' (or \wix{PMMs}) as a better descriptor. However, the term ``least-squares means'' was already well established in the \SAS{} software, and it has stuck. The most important things to remember are:\ixsub{least-squares means}{defined} \begin{itemize} \item LS~means are computed relative to a \emph{reference grid}. \item Once the reference grid is established, LS~means are simply predictions on this grid, or marginal averages of a table of these predictions. \end{itemize} A user who understands these points will know what is being computed, and thus can judge whether or not LS~means are appropriate for the analysis. \section{The reference grid} Since the reference grid\ixsub{reference grid}{defined} is fundamental, it is our starting point. For each predictor in the model, we define a set of one or more \emph{reference levels}. The reference grid is then the set of all combinations of reference levels. If not specified explicitly, the default reference levels are obtained as follows: \begin{itemize} \item For each predictor that is a factor, its reference levels are the unique levels of that factor. \item Each numeric predictor has just one reference level---its mean over the dataset. \end{itemize} So the reference grid depends on both the model and the dataset. \subsection{Example: Orange sales}\index{Examples!orange sales} To illustrate, consider the \wixcode{oranges} data provided with \lsm{}. This dataset has sales of two varieties of oranges (response variables "sales1" and "sales2") at 6 stores (factor "store"), over a period of 6 days (factor "day"). The prices of the oranges (covariates "price1" and "price2") fluctuate in the different stores and the different days. There is just one observation on each store on each day. For starters, let's consider an additive covariance model for sales of the first variety, with the two factors and both "price1" and "price2" as covariates (since the price of the other variety could also affect sales). <<>>= library("lsmeans") oranges.lm1 <- lm(sales1 ~ price1 + price2 + day + store, data = oranges) anova(oranges.lm1) @ The \wixcode{ref.grid} function in \lsm{} may be used to establish the reference grid. Here is the default one: <<>>= ( oranges.rg1 <- ref.grid(oranges.lm1) ) @ As outlined above, the two covariates "price1" and "price2" have their means as their sole reference level; and the two factors have their levels as reference levels. The reference grid thus consists of the $1\times1\times6\times6=36$ combinations of these reference levels. LS~means are based on predictions on this reference grid, which we can obtain using "predict" or "summary": <<>>= summary(oranges.rg1) @ \subsection{LS means as \wix{marginal averages} over the reference grid} The ANOVA indicates there is a significant "day" effect after adjusting for the covariates, so we might want to do a follow-up analysis that involves comparing the days. The \wixcode{lsmeans} function provides a starting point: <<>>= lsmeans(oranges.rg1, "day") ## or lsmeans(oranges.lm1, "day") @ These results, as indicated in the annotation in the output, are in fact the averages of the predictions shown earlier, for each day, over the 6 stores. The above LS~means (often called ``\wix{adjusted means}'') are not the same as the overall means for each day: <<>>= with(oranges, tapply(sales1, day, mean)) @ These \wix{unadjusted means} are not comparable with one another because they are affected by the differing "price1" and "price2" values on each day, whereas the LS~means are comparable because they use predictions at uniform "price1" and "price2" values. Note that one may call "lsmeans" with either the reference grid or the model. If the model is given, then the first thing it does is create the reference grid; so if the reference grid is already available, as in this example, it's more efficient to make use of it. For users who dislike the term ``LS~means,'' there is also a \wixcode{pmmeans} function (for \wix{predicted marginal means}) which is an alias for "lsmeans" but relabels the "lsmean" column in the summary. \subsection{Altering the reference grid}\ixsub{reference grid}{altering} The wixcode{at} argument may be used to override defaults in the reference grid. The user may specify this argument either in a "ref.grid" call or an "lsmeans" call; and should specify a "list" with named sets of reference levels. Here is a silly example: <<>>= lsmeans(oranges.lm1, "day", at = list(price1 = 50, price2 = c(40,60), day = c("2","3","4")) ) @ Here, we restricted the results to three of the days, and used different prices. One possible surprise is that the predictions are averaged over the two "price2" values. That is because "price2" is no longer a single reference level, and we average over the levels of all factors not used to split-out the LS~means. This is probably not what we want.\footnote{% The \emph{default} reference grid produces LS~means exactly as described in \cite{Sea80}. However, an altered reference grid containing more than one value of a covariate, such as in this example, departs from (or generalizes, if you please) their definition by averaging with equal weights over those \wix{covariate levels}. It is not a good idea here, but there is an example later in this vignette where it makes sense.} To get separate sets of predictions for each "price2", one must specify it as another factor or as a \wixcode{by} factor in the "lsmeans" call (we will save the result for later discussion): <<>>= org.lsm <- lsmeans(oranges.lm1, "day", by = "price2", at = list(price1 = 50, price2 = c(40,60), day = c("2","3","4")) ) org.lsm @ Note: We could have obtained the same results using any of these: <>= lsmeans(oranges.lm1, ~ day | price, at = ... ) # Ex 1 lsmeans(oranges.lm1, c("day","price2"), at = ... ) # Ex 2 lsmeans(oranges.lm1, ~ day * price, at = ... ) # Ex 3 @ Ex~1 illustrates the formula method for \wix{specifying factors}\ixsub{factors}{specifying}, which is more compact. The "|" character replaces the "by" specification. Ex~2 and Ex~3 produce the same results, but their results are displayed as one table (with columns for "day" and "price") rather than as two separate tables.\ixsub{formula specs}{one-sided} \section{Working with the results}\index{ref.grid@\dqt{ref.grid} class} \subsection{Objects} The "ref.grid" function produces an object of class \rg{}, and the "lsmeans" function produces an object of class \lsmo{},\index{lsmobj@\dqt{lsmobj} class} which is a subclass of \rg. There is really no practical difference between these two classes except for their "show" methods---what is displayed by default---and the fact that an \lsmo{} is not (necessarily) a true reference grid as defined earlier in this article. Let's use the \wixcode{str} function to examine the \lsmo{} object just produced: <<>>= str(org.lsm) @ We no longer see the reference levels for all predictors in the model---only the levels of "day" and "price2". These \emph{act} like reference levels, but they do not define the reference grid upon which the predictions are based. \subsection{Summaries} There are several methods for \rg{} (and hence also for \lsmo{}) objects. One already seen is \wixcode{summary}. It has a number of arguments---see its help page. In the following call, we summarize "days.lsm" differently than before. We will also save the object produced by "summary" for further discussion. <<>>= ( org.sum <- summary(org.lsm, infer = c(TRUE,TRUE), level = .90, adjust = "bon", by = "day") ) @ The \wixcode{infer} argument causes both \wix{confidence intervals} and \wix{tests} to be produced; the default confidence level of $.95$ was overridden; a Bonferroni adjustment\ixcode{adjust}\ixcode{level}\ix{multiplicity adjustment}\ixsub{multiplicity adjustment}{Bonferroni} was applied to both the intervals and the $P$~values; and the tables are organized the opposite way from what we saw before. What kind of object was produced by "summary"? Let's see: <<>>= class(org.sum) @ The \dqt{\wixcode{summary.ref.grid}} class is an extension of \dqt{data.frame}. It includes some attributes that, among other things, cause additional messages to appear when the object is displayed. But it can also be used as a \dqt{data.frame} if the user just wants to use the results computationally. For example, suppose we want to convert the LS~means from dollars to Russian rubles (at the July 13, 2014 exchange rate): {\small <<>>= transform(org.sum, lsrubles = lsmean * 34.2) @ } Observe also that the summary is just one data frame with six rows, rather than a collection of three data frames; and it contains a column for all reference variables, including any "by" variables. Besides "str" and "summary", there is also a \wixcode{confint} method, which is the same as "summary" with "infer=c(TRUE,FALSE)", and a \wixcode{test} method (same as "summary" with "infer=c(FALSE,TRUE)", by default). The "test" method may in addition be called with "joint=TRUE"\ixsub{tests}{joint} to obtain a joint test that all or some of the linear functions are equal to zero or some other value. There is also an \wixcode{update} method which may be used for changing the object's display settings. For example: <<>>= org.lsm2 <- update(org.lsm, by.vars = NULL, level = .99) org.lsm2 @ \subsection{Plots}\ix{plots}\ix{graphical displays} Confidence intervals for LS~means may be displayed graphically, using the \wixcode{plot} method. For example: <>= plot(org.lsm, by = "price2") @ The resulting display is shown in \Fig{org-plot}. This function requires that the \wixpkg{lattice} package be installed. \begin{figure} \begin{center} \includegraphics{using-lsmeans-org-plot.pdf} \end{center} \caption{Confidence intervals for LS~means in the \code{oranges} example.}\label{org-plot} \end{figure} Additional graphical presentations are covered later in this vignette. \section{Contrasts and comparisons} \subsection{Contrasts in general}\ix{contrasts} Often, people want to do pairwise comparisons of LS~means, or compute other contrasts among them. This is the purpose of the \wixcode{contrast} function, which uses a \dqt{ref.grid} or \dqt{lsmobj} object as input. There are several standard contrast families such as \dqt{pairwise}, \dqt{trt.vs.ctrl}, and \dqt{\wixcode{poly}}.\ixsub{contrasts}{polynomial}\ixsub{contrasts}{effects (offsets from mean)} In the following command, we request \dqt{\wixcode{eff}} contrasts, which are differences between each mean and the overall mean: <<>>= contrast(org.lsm, method = "eff") @ Note that this preserves the "by" specification from before, and obtains the effects for each group. In this example, since it is an \wix{additive model}, we obtain exactly the same results in each group. This isn't wrong, it's just redundant.\index{Redundant results} Another popular method is Dunnett-style \wixsub{contrasts}{Dunnett}, where a particular LS~mean is compared with each of the others. This is done using \dqt{\wixcode{trt.vs.ctrl}}. In the following, we obtain (again) the LS~means for days, and compare each with the average of the LS~means on day~5 and~6. <<>>= days.lsm <- lsmeans(oranges.rg1, "day") ( days_contr.lsm <- contrast(days.lsm, "trt.vs.ctrl", ref = c(5,6)) ) @ For convenience, \dqt{\wixcode{trt.vs.ctrl1}} and \dqt{\wixcode{trt.vs.ctrlk}} methods are provided for use in lieu of "ref" for comparing with the first and the last LS~means. The \dqt{\wixcode{dunnettx}} adjustment is a good approximation to the exact Dunnett $P$~value adjustment. If the exact adjustment is desired, use \wixcode{adjust}" = "\dqt{\wixcode{mvt}}; but this can take a lot of computing time when there are several tests. Note that by default, "lsmeans" results are displayed with confidence intervals while "contrast" results are displayed with $t$ tests. One can easily override this; for example, <>= confint(contrast(days.lsm, "trt.vs.ctrlk")) @ (Results not shown.) In the above examples, a default \wixsub{multiplicity adjustment}{default} is determined from the contrast method. This may be overridden by adding an \wixcode{adjust} argument. \subsection{Pairwise comparisons}\ixsub{contrasts}{pairwise comparisons} Often, users want \wix{pairwise comparisons} among the LS~means. These may be obtained by specifying \dqt{\wixcode{pairwise}} or \dqt{\wixcode{revpairwise}} as the "method" argument in the call to \wixcodesub{contrast}{method@\code{method}}. For group labels $A,B,C$, \dqt{pairwise} generates the comparisons $A-B, A-C, B-C$ while \dqt{revpairwise} generates $B-A, C-A, C-B$. As a convenience, a \wixcode{pairs} method is provided that calls "contrast" with "method="\dqt{pairwise}:\ixsub{pairwise comparisons}{using \code{pairs}} <<>>= pairs(org.lsm) @ There is also a \wixcode{cld} (\wix{compact letter display}) method that lists the LS~means along with grouping symbols for pairwise contrasts. It requires the \wixpkg{multcompView} package \citep{mcview} to be installed. <<>>= cld(days.lsm, alpha = .10) @ Two LS~means that share one or more of the same grouping symbols are not significantly different at the stated value of "alpha", after applying the multiplicity adjustment (in this case Tukey's HSD). By default, the LS~means are ordered in this display, but this may be overridden with the argument "sort=FALSE". "cld" returns a \dqt{summary.ref.grid} object, not an "lsmobj". Another way to display pairwise comparisons is via the "comparisons" argument of \wixcode{plot}. \ixcodesub{plot}{\code{comparisons}}\ixsub{pairwise comparisons}{graphical} When this is set to "TRUE", arrows are added to the plot, with lengths set so that the amount by which they overlap (or don't overlap) matches as closely as possible to the amounts by which corresponding confidence intervals for differences cover (or don't cover) the value zero. \ix{comparison arrows} This does not always work, and if there are discrepancies, a message is printed. But it usually works as long as the standard errors of differences are not too discrepant. <>= plot(days.lsm, comparisons = TRUE, alpha = .10) @ \Fig{days-cmp} shows the result. Note that the pairs of means having overlapping arrows are the same as those grouped together in the "cld" display. However, these comparison arrows show more about the degree of significance in the comparisons. The lowest and highest LS~mean have arrows pointing only inward, as the others are not needed. If the confidence intervals and arrows together look too cluttered, one can add the argument \code{intervals = FALSE}, then only the arrows will be displayed.\ixcodesub{plot}{intervals@\code{intervals}} \begin{figure} \begin{center} \includegraphics{using-lsmeans-days-cmp.pdf} \end{center} \caption{Graphical comparisons of the LS~means for \code{days}.}\label{days-cmp} \end{figure} \subsection{Multiplicity adjustments---changing the family}\ixsub{multiplicity adjustment}{combining/subsetting families} You may have noticed that in the preceding examples where $P$-value adjustments were implemented, those adjustments were made \emph{separately} for each sub-table when a "by" variable is active. Some users prefer that all the adjusted tests together as one family---or even combine more than one family of tests into one family for purposes of adjustment. This may be done using the \wixcode{rbind} method (similar to using "rbind" to combine matrices. On the flip side, perhaps we want to exclude some tests. This may be used using the \ixcode{[]} operator: simply specify the row indexes of the tests to include. To illustrate, consider the previously obtained "org.lsm" object. In "pairs(org.lsm)", we obtain the same results twice (as seen above) because the model is additive. For the same reason, if we change the "by" variable to \dqt{day}, we'll obtain three copies of the same comparison of the two "price2"s. If we want to consider the three "day" comparisons and the one "price2" comparison together as one family of four tests, we can do: <<>>= rbind(pairs(org.lsm)[1:3], pairs(org.lsm, by = "day")[1]) @ Note that by default, the \dqt{mvt} adjustment level is used; for complicated families like this, ordinary Tukey and Dunnett adjustments are usually not appropriate. We arrived at this point by a circuitous path. In the additive model, the above conditional results are the same as the marginal ones: <<>>= rbind(pairs(lsmeans(org.lsm, "day")), pairs(lsmeans(org.lsm, "price2"))) @ \section{Multivariate models} The "oranges" data has two response variables. Let's try a \wix{multivariate model} for predicting the sales of the two varieties of oranges, and see what we get if we call "ref.grid": <<>>= oranges.mlm <- lm(cbind(sales1,sales2) ~ price1 + price2 + day + store, data = oranges) ref.grid(oranges.mlm) @ What happens is that the multivariate response is treated like an additional factor, by default named \wixcode{rep.meas}. In turn, it can be used to specify levels for LS~means. Here we rename the multivariate response to \dqt{variety} and obtain "day" means (and a compact letter display for comparisons thereof) for each "variety":\ixcode{mult.name} <<>>= org.mlsm <- lsmeans(oranges.mlm, ~ day | variety, mult.name = "variety") cld(org.mlsm, sort = FALSE) @ \section{Contrasts of contrasts (interaction contrasts)} With the preceding model, we might want to compare the two varieties on each day: <<>>= org.vardiff <- update(pairs(org.mlsm, by = "day"), by = NULL) @ The results (not yet shown) will comprise the six "sales1-sales2" differences, one for each day. The two "by" specifications seems odd, but the one in "pairs" specifies doing a separate comparison for each day, and the one in "update" asks that we convert it to one table with six rows, rather than 6 tables with one row each. Now, let's compare these differences to see if they vary from day to day. <<>>= cld(org.vardiff) @ There is little evidence of variety differences, nor that these differences vary from day to day. A newer feature of the \wixcodesub{contrast}{\code{interaction}} function is the optional "interaction" argument, which may be used to specify \wix{interaction contrasts}\ixsub{contrasts}{interaction}\ixsub{contrasts}{of contrasts} by naming which contrast to use for each variable (in the order of appearance in the grid). In a similar example to the above, suppose we want to compare each polynomial contrast in "day" between the two varieties:\ixsub{contrasts}{polynomial} <<>>= contrast(org.mlsm, interaction = c("poly", "pairwise")) @ \section[Interfacing with multcomp]{Interfacing with \pkg{multcomp}} The \wixpkg{multcomp} package \citep{multc} supports more options for simultaneous inference than are available in \lsm{}. Its \wixcode{glht} (general linear hypothesis testing) function and associated \dqt{glht} class are similar in some ways to "lsmeans" and \dqt{lsmobj} objects, respectively. So \lsm{} provides an \wixcode{as.glht} function to do the conversion. To illustrate, let us convert the "days_contr.lsm" object (produced earlier) to a "glht" object, and use it to obtain adjusted $P$~values under \wix{Westfall's adjustment procedure} (not available in \lsm{}): <>= # Ensure we see the same results each time set.seed(123454321) @ <<>>= library("multcomp") days.glht <- as.glht(days_contr.lsm) summary(days.glht, test = adjusted("Westfall")) @ In addition, \lsm{} provides an \wixcode{lsm} function (or its alias, \wixcode{pmm}) that may be called from within a call to "glht". Thus, another way to obtain the same "glht" object is to use <>= days.glht1 <- glht(oranges.lm1, lsm("day", contr = "trt.vs.ctrl", ref = c(5,6))) @ By the way, the following two statements will produce the same results: <>= summary(days_contr.lsm, adjust = "mvt") summary(days.glht) @ That is, the \dqt{mvt} adjust method in \lsm{} is the same as the default single-step $P$~value adjustment in \pkg{multcomp}.\ixsub{multiplicity adjustment}{single-step (\code{mvt})} One additional detail: If there is a "by" variable in effect, "glht" or "as.glht" returns a "list" of "glht" objects---one for each "by" level. There are courtesy "coef", "confint", "plot", "summary", and "vcov" methods for this \dqt{\wixcode{glht.list}} class to make things a bit more user-friendly. Recall the earlier example result "org.lsm", which contains information for LS~means for three "day"s at each of two values of "price2". Suppose we are interested in pairwise comparisons of these LS~means, by "price2". If we call <>= summary(as.glht(pairs(org.lsm))) @ (results not displayed) we will obtain two "glht" objects with three contrasts each, so that the results shown will incorporate multiplicity adjustments for each family of three contrasts. If, on the other hand, we want to consider those six contrasts as one family, use <>= summary(as.glht(pairs(org.lsm), by = NULL)) @ \ldots{} and note (look carefully at the parentheses) that this is \emph{not} the same as <>= summary(as.glht(pairs(org.lsm, by = NULL))) @ which removes the "by" grouping \emph{before} the pairwise comparisons are generated, thus yielding ${6 \choose 2}=15$ contrasts instead of just six.\ixsub{multiplicity adjustment}{effect of \code{by} on family} \section{A new example: Oat yields}\index{Examples!oat yields}\index{Examples!split-plot experiment} Orange-sales illustrations are probably getting tiresome. To illustrate some new features, let's turn to a new example. The \wixcode{Oats} dataset in the \wixpkg{nlme} package \citep{nlme} has the results of a split-plot experiment discussed in \citet{Yat35}. The experiment was conducted on six blocks (factor "Block"). Each block was divided into three plots, which were randomized to three varieties (factor "Variety") of oats. Each plot was divided into subplots and randomized to four levels of nitrogen (variable "nitro"). The response, "yield", was measured once on each subplot after a suitable growing period. We will fit a model using the "lmer" function in the \wixpkg{lme4} package \citep{lme4}. This will be a mixed model with random intercepts for "Block" and "Block:Variety" (which identifies the plots). A logarithmic transformation is applied to the response variable (mostly for illustration purposes, though it does produce a good fit to the data). Note that "nitro" is stored as a numeric variable, but we want to consider it as a factor in this initial model. <<>>= data("Oats", package = "nlme") library("lme4") Oats.lmer <- lmer(log(yield) ~ Variety*factor(nitro) + (1|Block/Variety), data = Oats) anova(Oats.lmer) @ Apparently, the interaction is not needed. But perhaps we can further simplify the model by using only a linear or quadratic trend in "nitro". We can find out by looking at polynomial contrasts:\ixsub{contrasts}{polynomial} <>= contrast(lsmeans(Oats.lmer, "nitro"), "poly") @ %%% Fake the warning message <>= cat("NOTE: Results may be misleading due to involvement in interactions") @ <>= <> @ \ixsub{warnings}{when interaction is in model} (A message is issued when we average over predictors that interact with those that delineate the LS~means. In this case, it is not a serious problem because the interaction is weak.) Both the linear and quadratic contrasts are pretty significant. All this suggests fitting an additive model where "nitro" is included as a numeric predictor with a quadratic trend. <<>>= Oats.lmer2 <- lmer(log(yield) ~ Variety + poly(nitro,2) + (1|Block/Variety), data = Oats) @ Remember that "nitro" is now used as a quantitative predictor.\ixsub{factors}{with quantitative levels} But for comparing with the previous model, we want to see predictions at the four unique "nitro" values rather than at the average of "nitro". This may be done using "at" as illustrated earlier, or a shortcut is to specify \wixcode{cov.reduce} as "FALSE", which tells "ref.grid" to use all the unique values of numeric predictors. <<>>= Oats.lsm2 <- lsmeans(Oats.lmer2, ~ nitro | Variety, cov.reduce = FALSE) @ The results are displayed as an export table (see Section~\ref{xtsect}) in Table~\ref{xtable:example}, page~\pageref{xtable:example}. These LS~means follow the same quadratic trend for each variety, but with different intercepts.\footnote{% This is the promised example where our generalization of \cite{Sea80}'s definition of LS~means makes sense. Suppose we want to compare the LS~means for \code{Variety} with those in the original model \code{Oats.lmer} where \code{nitro} was a factor, we want to average equally over the four \code{nitro} levels, even though \code{nitro} is a covariate in this second model.}\ixsub{reference grid}{altered for quantitative factor} Fractional \wixsub{degrees of freedom}{fractional} are displayed in these results. These are obtained by default using the Satterthwaite method, using routines in the \wixpkg{lmerTest} package \citep{lmert}. Adding the argument \code{mode = \dqt{kenward-roger}} to the \code{lsmeans} call will cause the degrees of freedom to be computed using instead the Kenward-Roger (K-R) method from the \wixpkg{pbkrtest} package \citep{pbkrt}, which also implements, as a side-effect, a bias adjustment in the estimated covariances (and hence standard errors). The K-R method is probably preferable, but it requires a lot more computation, and hence is no longer the default. A third option is to specify \code{mode = \dqt{asymptotic}}, for which all the degrees of freedom are set to \code{NA}---producing $z$~tests rather than $t$~tests. You may change the default via \code{lsm.options(lmer.df = \emph{\dqt{desired default}})}. These \code{mode} settings are partially matched, so \code{mode = \dqt{k}} is actually good enough. \section{Additional display methods} \subsection{Export tables}\ix{export tables}\label{xtsect} The \pkg{lsmeans} package provides an \wixcode{xtable} method \citep{xtable} that works with "lsmobj", "ref.grid", and "summary.ref.grid" objects. (It actually uses the \wixcode{xtableList} interface; see the \ixpkg{xtable} documentation for details.) This is quite useful if you want a nicely formatted table, especially using \wixcode{Sweave} or \wixcode{knitr}. To illustrate, we display the "Oats.lsm2" object just created. <>= library("xtable") xtbl <- xtable(Oats.lsm2, caption = "Example using \\texttt{xtable}", label = "xtable:example") print(xtbl, table.placement = "t") cat("See Table~\\ref{xtable:example}.\n") @ \subsection{Displaying LS means graphically}\ixsub{graphical displays}{interaction plot} We have already seen the use of the "plot" function to display confidence intervals and/or comparison arrows. The \lsm{} package also includes a function \wixcode{lsmip} that displays predictions in an interaction-plot-like manner.\ix{interaction plot} It uses a formula of the form \begin{Sinput} curve.factors ~ x.factors | by.factors \end{Sinput} This function also requires the \wixpkg{lattice} package. In the above formula, "curve.factors" specifies factor(s) used to delineate one displayed curve from another (i.e., groups in \pkg{lattice}'s parlance). "x.factors" are those whose levels are plotted on the horizontal axis. And "by.factors", if present, break the plots into panels. To illustrate, consider the first model we fitted to the "Oats" data. Let's do a graphical comparison of the two models we have fitted to the "Oats" data. <>= lsmip(Oats.lmer, Variety ~ nitro, ylab = "Observed log(yield)") @ \vspace{-12pt} <>= lsmip(Oats.lsm2, Variety ~ nitro, ylab = "Predicted log(yield)") @ The plots are shown in \Fig{intplots}. Note that the first model fits the cell means perfectly, so its plot is truly an interaction plot of the data. The other displays the parabolic trends we fitted in the revised model. \begin{figure} \includegraphics[width=3in]{using-lsmeans-oatslmer.pdf} \hfill \includegraphics[width=3in]{using-lsmeans-oatslmer2.pdf} \caption{Interaction plots for the cell means and the fitted model, \code{Oats} example.}\label{intplots} \end{figure} \section{Transformations}\ix{transformations} \subsection{Automatic support for transformations}\ixsub{transformations}{automatically detected} When a transformation or link function is used in fitting a model, "ref.grid" (also called by "lsmeans") stores that information in the returned object, as seen in this example: <<>>= str(Oats.lsm2) @ This allows us to conveniently unravel the transformation, via the \wixcode{type} argument in "summary" or related functions such as "lsmip" and "predict".\ixcodesub{summary}{type@\code{type = }\dqt{response}} Here are the predicted yields for (as opposed to predicted log yields) for the polynomial model: <<>>= summary(Oats.lsm2, type = "response") @ It is important to realize that the statistical inferences are all done \emph{before} reversing the transformation. Thus, $t$ ratios are based on the linear predictors and will differ from those computed using the printed estimates and standard errors. Likewise, \wixsub{confidence intervals}{back-transformed} are computed on the linear-predictor scale, then the endpoints are back-transformed. This kind of automatic support is available for most popular response transformations such as "log", "log10", and even transformations like "asin(sqrt())" and "sqrt(y)+sqrt(y+1)". The Details section for \verb|help("make.tran")| provides a complete list. It is also possible to support custom transformations via the "tran" argument in the "update" method---see its help page. \ixsub{transformations}{custom} \subsection{Using \code{make.tran}}\ixsub{transformations}{using \code{make.tran}}\ixsub{transformations}{requiring parameter(s)}\ixsub{transformations}{Box-Cox} The \wixcode{make.tran} function provides support for yet more popular types of transformations, particularly those that require specifying one or more parameters. Examples are general power transformations, \wix{Box-Cox transformations}, and transformations with a shifted origin such as "log(y + a)". Details may of course be found via \verb|help("make.tran")|. The function returns a "list" of functions, compatible with what is returned by "make.link" in the \pkg{stats} package. The latter is intended primarily for use with generalized linear models, and "make.tran" extends such capabilities to other response transformations. There are two basic ways to use "make.tran": retrospectively on an existing model, and prospectively in fitting a new model. Here is an example of retrospective use, where the $\log(y+5)$ transformation was used. This transformation is not auto-detected. <<>>= Oats.log1 <- lmer(log(yield + 5) ~ Variety + factor(nitro) + (1|Block/Variety), data = Oats) ( Oats.rg1 <- update(ref.grid(Oats.log1), tran = make.tran("genlog", 5)) ) @ Here, we created a reference grid for the model, then updated it with its "tran" component set to the result of "make.tran" for a generalized log transformation with parameter 5. \ixcodesub{update}{tran@\code{tran}} This updated reference grid has all the information needed to back-transform the results to the original "yield" scale: <<>>= round(predict(Oats.rg1, type = "response"), 1) @ \ixcodesub{tran}{using \code{linkfun}}\ixcodesub{make.tran}{as enclosing environment} Using "make.tran" prospectively makes use of the fact that the transformation itself is included in the returned list as a function named "linkfun" (somewhat oddly named due to the fact that "make.tran" mimics the functionality of "make.link"). When a model is fitted with "linkfun" as the transformation, its \wix{enclosing environment} is automatically used to obtain the transformation definitions. For illustration, consider a rather far-fetched transformation: <<>>= my.tran <- make.tran("boxcox", c(.567, 10)) my.tran$linkfun(10:15) @ This specifies a Box-Cox transformation with the origin shifted to $10$:% \footnote{To obtain an ordinary Box-Cox transformation, provide just one parameter: \code{make.tran(\dqt{boxcox}, .567)}.} \[ h(y) = \frac{(y-10)^{.567} - 1}{1 - .567} \] If we use "my.tran" as an enclosing environment for fitting the model, the transformation is saved automatically: <<>>= Oats.bc <- with(my.tran, lmer(linkfun(yield) ~ Variety + factor(nitro) + (1|Block/Variety), data = Oats)) ( rg.bc <- ref.grid(Oats.bc) ) round(predict(rg.bc, type = "response"), 1) @ \subsection{Using \code{regrid}}\ixsub{reference grid}{re-gridding to response scale} The \wixcode{regrid} function may be used to, in essence, give a new beginning to an existing reference grid (or "lsmobj"), most redefined on the response scale (i.e., back-transformed). Consider the preceding Box-Cox example, after applying "regrid": <<>>= rg.bc.regrid <- regrid(rg.bc) @ By default, the estimates are back-transformed to the response scale. In a "regrid" result, the "linfct" slot (linear functions) become the identity matrix, and the "bhat" slot (regression coefficients) become the predictions at each grid point: <<>>= round(rg.bc.regrid@bhat, 1) @ which matches the predictions shown previously. The interesting thing is what happens if we subsequently obtain LS~means. Compare these results: <<>>= summary(lsmeans(rg.bc, "Variety"), type = "response") lsmeans(rg.bc.regrid, "Variety") @ \ixcodesub{regrid}{effect on LS~means} Why are the answers somewhat different? Recall that LS~means are obtained via equally-weighted averages of predictions. In the first "lsmeans" call, the predictions, on the Box-Cox scale, are averaged together and then back-transformed to the response scale; whereas in the second "lsmeans" call, the predictions being averaged were already on the response scale. (Hence, the results are the usual arithmetic means of the predictions on the grid.) Since the Box-Cox transformation is nonlinear, averaging then back-transforming is not the same as back-transforming then averaging.\ixsub{mean}{arithmetic} Even the degrees of freedom (d.f.) differ in the two results, because degrees-of-freedom calculations take place on the linear-predictor scale. Once results are back-transformed, "regrid" ``freezes'' the calculated \wixsub{degrees of freedom}{containment method} for each prediction. Subsequently, a containment method is used whereby the returned d.f.\ is the minimum d.f.\ of predictions involved in each LS~mean. Some users prefer averaging the predictions on the response scale as they are then the arithmentic means; and now you see that the way to make that happen is through the "regrid" function. \subsection{Reverse-engineering a log transformation}\ixsub{reference grid}{re-gridding to log scale} When a response has been log-transformed, then there are useful special properties of back-transformed summaries: \begin{itemize} \item LS~means, when back-transformed to the response scale, are actually the \emph{geometric} means of the response-scale predictions.\ixsub{mean}{geometric} \item A difference of two LS~means on the log scale, after back-transforming, becomes an estimate of the \emph{ratio} of the two geometric means. Such comparisons via ratios can be quite useful for positive responses.\ixsub{pairwise comparisons}{by ratios instead of differences} \end{itemize} The \wixcodesub{regrid}{log@\dqt{log} option} function provides a \dqt{log} option that recomputes the reference grid \emph{as if} the response transformation had been the natural logarithm. We can then take advantage of the above special properties of log-transformed responses. The only proviso is that, on the response scale, all of the reference-grid predictions must be positive. To illustrate, we revisit the above Box-Cox model once again, and regrid it on the log scale: <<>>= rg.log <- regrid(rg.bc, "log") lsm.log <- lsmeans(rg.log, "Variety") summary(lsm.log, type = "response") summary(pairs(lsm.log), type = "response") @ The LS~means shown are the geometric means of the predictions, as opposed to the arithmetic means obtained above from "rg.bc.regrid". And the pairwise comparisons come out as ratios of these. \subsection{The \code{transform} argument}\ixcodesub{transform}{in \code{ref.grid} or \code{lsmeans}} For convenience, the user may use a "transform" argument to re-grid as part of a "ref.grid" or "lsmeans" call. For example, \verb|lsmeans(Oats.bc, "Variety", transform = "response")| is equivalent to \verb|lsmeans(rg.bc.regrid, "Variety")| but without needing the two code steps previously used to produce "rg.bc" and "rg.bc.regrid". \section{More on tests} \def\tj{\theta^{(j)}}%%% Notation for this section only The default settings for "test" yield traditional two-tailed $t$ (or $z$) tests of significance against zero. So if $\tj$ is the $j$th parameter (e.g., LS~mean or contrast) being estimated, we are testing the null hypothesis $H_0: \tj=0$ versus the alternative $H_1:\tj\ne 0$. We can, however, specify different types of tests in the \wixcode{test} or \wixcode{summary} functions. \subsection{Nonzero null values}\ixsub{tests}{nonzero null} If we wish to use nonzero null values, i,e., test $H_0:\tj=\tj_0$, use "test" or "summary" with the \wixcode{null} argument set to the desired $\tj_0$ values. For example, in the Oat-yield example, suppose we want to test each of the "Variety" yields against 100 (actually $\log 100$ since the response was transformed): <<>>= Oats.Vlsm = lsmeans(Oats.lmer2, "Variety") test(Oats.Vlsm, null = log(100), type = "response") @ Note that "null" should always be given on the linear-predictor scale (in this case $\log$ yield), even when specifying \verb|type="response"|. We could have specified different null values for each hypothesis by providing a vector of three numbers. \subsection{Equivalence tests}\ixsub{tests}{equivalence}\ix{equivalence tests}\ix{TOST method} The preceding results say that none of the variety means differs significantly from 100, after transforming. But this is not the same as saying that we have evidence that the means are close to 100 (that is, absence of proof is not proof of absence). To make a strong statement that an effect is small, we should use an equivalence test, which more-or-less turns the hypotheses around: \[ H_0: |\tj - \tj_0| \ge \delta \qquad\mbox{versus}\qquad H_1: |\tj - \tj_0| < \delta \] where $\delta$ is a specified threshold of equivalence. A common test procedure is the two one-sided test (TOST) method \citep{Sch87}, whereby we obtain equivalence only if we can establish both that $\tj-\tj_0>-\delta$ and that $\tj-\tj_0<\delta$. In "lsmeans", we do this by pre-identifying the less significant of these two tests: \[ t = \frac{|\hat\tj-\tj_0| - \delta}{SE(\hat\tj)} \] and the $P$~value is the \emph{left}-tail probability of this quantity from the central $t$ distribution. In "test" or "summary", an equivalence test is requested by specifying a nonzero \wixcode{delta} argument, which in turn is used as the threshold $\delta$. In the Oat-yield example, the following results are obtained using a threshold of $\delta=0.20$:\ixcodesub{test}{delta@\code{delta} argument} <<>>= test(Oats.Vlsm, null = log(100), delta = 0.20, type = "r") @ So two of the three Variety means are established as being within $.20$ of $\log100$. The natural log scale has the special property that small increments on the log scale translate to approximate percentage differences of the same size. That is, a threshold of $.20$ corresponds to about a 20\% difference: $\log 80 - \log100 = \log.8 \approx -.223$, and $\log120 - \log100 = \log1.2 \approx .182$. \subsection{One-sided tests, noninferiority, nonsuperiority} \ixsub{tests}{one-sided} The \wixcode{side} argument is also available to specify \wix{one-sided tests}. A right-tailed alternative may be requested using "side" partially matching one of \dqt{+}, \dqt{right}, \verb|">"|, "+1", "1", \dqt{superiority}, or (see later) \dqt{noninferiority}. Similarly, a left-tailed alternative may be specified using "side" equal to \dqt{-}, \dqt{left}, \verb|"<"|, "-1", \dqt{inferiority}, or \dqt{nonsuperiority}. (And for completeness, a two-sided alternative is specified using "0", "2", \verb|"!="|, \dqt{both}, \dqt{two-sided}, \dqt{equivalence}, or \dqt{=}.) In the following example, we test to see if either Golden Rain or Marvellous has better yield than Victory: <<>>= test(contrast(Oats.Vlsm, "trt.vs.ctrlk"), side = ">") @ \ixsub{tests}{noninferity or nonsuperiority} The one-sided version of an equivalence test is called a noninferiority or nonsuperiority test. It is obtained by specifying both "side" and a nonzero "delta". For example, to test whether Victory is as good as the other two within a 25\% threshold, use <<>>= test(contrast(Oats.Vlsm, "trt.vs.ctrlk"), side = "nonsup", delta = .25) @ We find strong evidence that, with the stated threshold of .25, Golden Rain is nonsuperior to Victory (so that Victory is noninferior to Golden Rain); but not strong evidence that Victory is noninferior to Marvellous. \section{Trends}\ix{trends, estimating and comparing}\ix{slopes, estimating and comparing} \index{Examples!chick weights}\index{Examples!comparing trends} The \lsm{} package provides a function \wixcode{lstrends} for estimating and comparing the slopes of fitted lines (or curves). To illustrate, consider the built-in R dataset \wixcode{ChickWeight} which has data on the growths of newly hatched chicks under four different diets. The following code produces the display in \Fig{chick-plot}. <>= require("lattice") xyplot(weight ~ Time | Diet, groups = ~ Chick, data = ChickWeight, type = "o", layout=c(4, 1)) @ \begin{figure} \centerline{\includegraphics[width=6in]{using-lsmeans-chick-plot}} \caption{Growth curves of chicks, dataset \texttt{ChickWeight}.}\label{chick-plot} \end{figure} Let us fit a model to these data using random slopes for each chick and allowing for a different average slope for each diet (a square-root transformation straightens-out the curves somewhat): <<>>= Chick.lmer <- lmer(sqrt(weight) ~ Diet * Time + (0 + Time | Chick), data = ChickWeight) @ We can then call \wixcode{lstrends} (or, its anti-SAS alias, \wixcode{pmtrends}) to estimate and compare the average slopes for each diet. <<>>= Chick.lst <- lstrends (Chick.lmer, ~ Diet, var = "Time") @ Now, let's summarize the estimated trends and pairwise comparisons of these slopes using a compact letter display. <<>>= cld (Chick.lst) @ According to the Tukey~HSD comparisons (with default significance level of $.05$), there are two groupings of slopes: Diet~1's mean slope is significantly less than $3$ or $4$'s, Diet~2's slope is not distinguished from any other. Because of the response transformation, the slopes we just computed are for trends on the square-root-weight scale. If you want the trends on the actual weight scale after back-transforming, that is possible via the "transform" argument:\ixcodesub{lstrends}{with response transformation}\ixcodesub{transform}{in \code{lstrends}} <<>>= lstrends(Chick.lmer, ~ Diet | Time, var = "Time", transform = "response", at = list(Time = c(5, 15))) @ We specified two different "Time" values to emphasize that after back-transforming, the slopes are different at each "Time", whereas (by the model specification) the slopes don't depend on "Time" when we leave it on the square-root scale. Note: "lstrends" computes a difference quotient based on two slightly different reference grids. Thus, if it it must be called with a model object, not a "ref.grid" object. \ixsub{reference grid}{difference quotient of two} \section{User preferences}\ix{user preferences} \lsm{} sets certain defaults for displaying results---for example, using $.95$ for the confidence coefficient, and showing intervals for "lsmeans" output and test statistics for "contrast" results. As discussed before, one may use arguments in "summary" to change what is displayed, or "update" to change the defaults for a given object. But suppose you want different defaults to begin with. These can be set using the \wixcode{lsm.options} statement. For example: <<>>= lsm.options(ref.grid = list(level = .90), lsmeans = list(), contrast = list(infer = c(TRUE, TRUE))) @ This requests that any object created by "ref.grid" be set to have confidence levels default to $90$\%, and that "contrast" results are displayed with both intervals and tests. No new options are set for "lsmeans" results, and the "lsmeans" part could have been omitted. These options are stored with objects created by "ref.grid", "lsmeans", and "contrast". For example, even though no new defaults are set for "lsmeans", future calls to "lsmeans" \emph{on a model object} will be displayed with 90\% confidence intervals, because "lsmeans" calls "ref.grid". However, calling "lsmeans" on an existing \dqt{ref.grid} object will inherit that object's setting. Certain other options are available; for example, the \dqt{\wixcode{estble.tol}} option sets the tolerance for determining estimability of linear contrasts. To see its current value: <<>>= get.lsm.option("estble.tol") @ Defaults for this and some other parameters are saved in \wixcode{defaults.lsm}. \ixcode{get.lsm.option} %%%%%%%%%%%%%%%%% INDEXING COMPLETED TO HERE 3-5-16 %%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Two-sided formulas}\ixsub{factors}{specifying}\ixsub{formula specs}{two-sided} In its original design, the only way to obtain contrasts and comparisons in \lsm{} was to specify a two-sided formula, e.g., "pairwise ~ treatment", in the "lsmeans" call. The result is then a list of "lsmobj" objects (class \dqt{lsm.list}). \index{lsm.list@\dqt{lsm.list} class} In its newer versions, \lsm{} offers a richer family of objects that can be re-used, and dealing with a list of objects can be awkward or confusing, so its continued use is not encouraged. Nonetheless, it is still available for backward compatibility. Here is an example where, with one command, we obtain both the LS~means and pairwise comparisons for "Variety" in the model "Oats.lmer2": {\small <<>>= lsmeans(Oats.lmer2, pairwise ~ Variety) @ } This example also illustrates the effect of the preceding "lsm.options" settings. Let us now return to the default display for contrast results. <<>>= lsm.options(ref.grid = NULL, contrast = NULL) @ \section{Messy data}\ix{messy data}\index{Examples!nutrition study}\index{Examples!messy data} To illustrate some more \code{lsmeans} capabilities, consider the dataset named \wixcode{nutrition} that is provided with the \lsm{} package. These data come from \citet{Mil92}, and contain the results of an observational study on nutrition education. Low-income mothers are classified by race, age category, and whether or not they received food stamps (the \code{group} factor); and the response variable is a gain score (post minus pre scores) after completing a nutrition training program. Consider the model that includes all main effects and two-way interactions. A Type-II (hierarchical) analysis-of-variance table is also shown. <<>>= nutr.lm <- lm(gain ~ (age + group + race)^2, data = nutrition) library("car") Anova(nutr.lm) @ One main effect ("group") is quite significant, and there is possibly an interaction with "race". Let us look at the \code{group} by \code{race} LS~means: <>= lsmip(nutr.lm, race ~ age | group) lsmeans(nutr.lm, ~ group*race) @ \begin{figure} \centerline{\includegraphics[scale=.75]{using-lsmeans-nutr-intplot}} \caption{Predictions for the nutrition data}\label{nutr-intplot} \end{figure} \Fig{nutr-intplot} shows the predictions from this model. One thing the output illustrates is that \code{lsmeans} incorporates an \wix{estimability} check, and returns a missing value when a prediction cannot be made uniquely. In this example, we have very few Hispanic mothers in the dataset, resulting in \wix{empty cells}. This creates a \wix{rank deficiency} in the fitted model, and some predictors are thrown out. We can avoid non-estimable cases by using \code{at} to restrict the reference levels to a smaller set. A useful summary of the results might be obtained by narrowing the scope of the reference levels to two races and the two middle age groups, where most of the data lie. However, always keep in mind that whenever we change the reference grid, we also change the definition of the LS~means. Moreover, it may be more appropriate to average the two ages using weights proportional to their frequencies in the data set. The simplest way to do this is to add a \wixcode{weights} argument.\footnote{ It may also be done by specifying a custom function in the \wixcode{fac.reduce} argument, but for simple weighting, \code{weights} is simpler.} With those ideas in mind, here are the LS~means and comparisons within rows and columns: <<>>= nutr.lsm <- lsmeans(nutr.lm, ~ group * race, weights = "proportional", at = list(age = c("2","3"), race = c("Black","White"))) @ So here are the results <<>>= nutr.lsm summary(pairs(nutr.lsm, by = "race"), by = NULL) summary(pairs(nutr.lsm, by = "group"), by = NULL) @ The general conclusion from these analyses is that for age groups 2 and~3, the expected gains from the training are higher among families receiving food stamps. Note that this analysis is somewhat different than the results we would obtain by subsetting the data before analysis, as we are borrowing information from the other observations in estimating and testing these LS~means. \subsection{More on weighting}\label{weights} The \wixcodesub{weights}{equal, proportional, outer, cells} argument can be a vector of numerical weights (it has to be of the right length), or one of four text values: \dqt{equal} (weight the predictions equally when averaging them, the default), \dqt{proportional} (weight them proportionally to the observed frequencies of the factor combinations being averaged over), \dqt{outer} (weight according to the outer products of the one-factor marginal counts), or \dqt{cells} (weight each mean differently, according to the frequencies of the predictions being averaged). \Fig{wtcomp} shows the LS~means for "race" using the four different weighting schemes. (Note: If the model itself has weights, then the total weights are used instead of counts.) \begin{figure} \hspace{-.06\linewidth} \begin{minipage}{1.12\linewidth} \hrule \columnseprule=.2pt \begin{multicols}{2}\footnotesize <<>>= lsmeans(nutr.lm, "race", weights = "equal") lsmeans(nutr.lm, "race", weights = "prop") lsmeans(nutr.lm, "race", weights = "outer") lsmeans(nutr.lm, "race", weights = "cells") @ \end{multicols} \hrule \end{minipage} \caption{Comparison of four different weighting methods}\label{wtcomp} \end{figure} Note there are four different sets of answers. The \dqt{equal} weighting is self-explanatory. But what's the distinction between \dqt{proportional} and \dqt{outer}? To clarify, consider: <<>>= temp = lsmeans(nutr.lm, c("group","race"), weights = "prop") lsmeans(temp, "race", weights = "prop") @ The previous results using \dqt{outer} weights are the same as those using \dqt{proportional} weights on one factor at a time. Thus, if only one factor is being averaged over, \dqt{outer} and \dqt{proportional} are the same. Another way to look at it is that outer weights are like the expected counts in a chi-square test; each factor is weighted independently of the others. The results for \dqt{cells} weights stand out because everything is estimable---that's because the empty cells in the data were given weight zero. These results are the same as the unadjusted means: <<>>= with(nutrition, tapply(gain, race, mean)) @ \subsection{Alternative covariate adjustments}\ix{covariate adjustments} \index{Example!framing experiment} The \wixcode{framing} data in the \pkg{mediation} package has the results of an experiment conducted by \cite{Bra08} where subjects were given the opportunity to send a message to Congress regarding immigration. However, before being offered this, some subjects ("treat=1") were first shown a news story that portrays Latinos in a negative way. Besides the binary response (whether or not they elected to send a message), we also measured "emo", the subjects' emotional state after the treatment was applied. There are various demographic variables as well. Before fitting a logistic regression model, I will change the labels for "educ" to shorter strings. <<>>= library("mediation") levels(framing$educ) = c("NA","Ref","< HS", "HS", "> HS","Coll +") framing.glm = glm(cong_mesg ~ age + income + educ + emo + gender * factor(treat), family = binomial, data = framing) @ The left-hand plot in \Fig{framing} displays the conventional \wix{adjusted means}, where predictions are made with the covariates "age", "income", and "emo" set to their mean values: <>= lsmip(framing.glm, treat ~ educ | gender, type = "response") @ This plot is rather implausible because the displayed treatment effects are the opposite for females as for males, and the effect of education isn't monotone as one might expect. \begin{figure} \begin{center} \begin{tabular}{c@{\qquad}c} (a) & (b) \\ \includegraphics[width=3in]{using-lsmeans-framinga.pdf} & \includegraphics[width=3in]{using-lsmeans-framingb.pdf} \end{tabular} \end{center} \caption{Estimated responses for the \code{framing} data. (a)~Holding \code{emo} constant at its mean; (b)~Using predictions of \code{emo} for each \code{treat}.}\label{framing} \end{figure} \ix{covariate affected by treatments}\ix{mediating covariate} However, "emo" is a post-treatment measurement. This means that the treatment could have affected it (it is a \emph{mediating} covariate). If it is indeed affected by "treat", then \Fig{framing}(a) would be misleading because "emo" is held constant. Instead, consider making the predictions where "emo" is set to its predicted value at each combination of "treat" and the demographic variables. This is easily done by setting \wixcodesub{cov.reduce}{as a formula} to a formula for how to predict "emo": <>= lsmip(framing.glm, treat ~ educ | gender, type = "response", cov.reduce = emo ~ treat*gender + age + educ + income) @ This plot is shown in \Fig{framing}(b). It is quite different, suggesting that "emo" does indeed play a strong mediating role. (The \pkg{mediation} package has functions for estimating the strength of these effects.) The predictions suggest that, taking emotional response into account, male subjects exposed to the negative news story are more likely to send the message than are females or those not seeing the negative news story. Also, the effect of "educ" is (almost) monotone. You can see what values of "emo" are used in these predictions by looking at the "grid" slot in the reference grid: <<>>= ref.grid(framing.glm, cov.reduce = emo ~ treat*gender + age + educ + income)@grid @ whereas the overall mean of \Sexpr{round(mean(framing$emo), 3)} is used as the value of "emo" in \Fig{framing}(a). \ifx %%% Old covariate example is commented-out %%%%%%%%%%%%%%%%%% \cite{urq82} reports data on slaughter weights of animals that entered a feedlot as yearling calves. The animals came from 11 different herds, and each animal was randomized to one of three diets. In addition, the weight of each yearling at entry was recorded. The "feedlot" dataset provided in \lsm{} contains these results. From the feedlot operator's perspective, both diets and herds are fixed effects. Let us fit a factorial model with slaughter weight "swt" as the response and entry weight "ewt" as a covariate. %<<>>= feedlot.lm <- lm(swt ~ ewt + herd * diet, data = feedlot) Anova(feedlot.lm) @ The interaction tesrm doesn't make much of a contribution here, so we will work with an additive model instead (which also ameliorates some non-estimability issues due to missing cells). %<<>>= feedlot.add <- update(feedlot.lm, . ~ . - herd:diet) @ Here are the "LS~means" for the herds, and a compact letter display for comparisons thereof: %<<>>= cld(lsmeans(feedlot.add, "herd")) @ No herds are found to be different---not a surprise given that the $P$~value for "herd" is about the same as for the original model. However, these predictions are made at the same entry weight for every herd. This is \emph{not} the right thing to do here, because the herds differ in genetic makeup, the way they were fed and managed, and so forth---which affect the yearlings' entry weights. This is an example where a treatment affects a covariate. Each herd should have its own reference value for entry weight. This is done in "lsmeans" by providing a formula in the "cov.reduce" argument. The formula "ewt ~ herd" indicates that the reference grid should be constructed using the predicted value of "ewt", based on a linear model with "herd" as the predictor. Here are the results: %<<>>= cld(lsmeans(feedlot.add, "herd", cov.reduce = ewt ~ herd)) @ What a world of difference! We now see many significant differences in the comparisons. By the way, another approach would be to simply omit "ewt" from the model, to prevent making inappropriate adjustments in the traditional analysis. With such a model (not shown), the predictions are similar to those above; however, their standard errors are substantially higher, because---as seen in the ANOVA table---the covariate explains a lot of the variation. \fi %%%%%%%%%%%%% end of commented-out section %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Another use of formulas in \wixcodesub{cov.reduce}{to reflect dependence} is to create representative values of some covariates when others are specified in \wixcode{at}. For example, suppose there are three covariates $x_1,x_2,x_3$ in a model, and we want to see predictions at a few different values of $x_1$. We might use <>= rg <- ref.grid(my.model, at = list(x1 = c(5,10,15)), cov.reduce = list(x2 ~ x1, x3 ~ x1 + x2)) @ (When more than one formula is given, they are processed in the order given.) The values used for $x_2$ and $x_3$ will depend on $x_1$ and should in some sense be more realistic values of those covariates as $x_1$ varies than would be the overall means of $x_2$ and $x_3$. Of course, it would be important to display the values used---available as "rg@grid"---when reporting the analysis. \section{Other types of models}\ix{models supported} \subsection[Models supported by lsmeans]{Models supported by \lsm{}} The \lsm{} package comes with built-in support for quite a number of packages and model classes, including \dqt{lm}, \dqt{mlm}, \dqt{aov}, \dqt{aovlist}, and \dqt{glm} in the \wixpkg{stats} package, mixed models such as \dqt{lme}, \dqt{lmerMod}, and \dqt{glmerMod}, several survival models, GEE-type models, models having responses that are ordinal, multinomial, counts, and interval-(0,1), and Bayesian models. For a complete list, use \code{help(\dqt{models})}. \ifx % COMPLETE-ish LIST IS NOW COMMENTED-OUT ----------------- \begin{quote} \begin{description} \pitem{stats}: \dqt{lm}, \dqt{mlm}, \dqt{aov}, \dqt{aovlist}, \dqt{glm} \pitem{nlme}: \dqt{lme}, \dqt{gls}, \dqt{nlme} \pitem{lme4}: \dqt{lmerMod}, \dqt{glmerMod} \pitem{survival}: \dqt{survreg}, \dqt{coxph} %%%\pitem{coxme}: \dqt{coxme} \pitem{MASS}: \dqt{polr} \pitem{gee, geepack}: \dqt{gee}, \dqt{geeglm}, \dqt{geese} \pitem{ordinal}: \dqt{clm}, \dqt{clmm} \pitem{rms}: \dqt{rms} and descendents such as \dqt{ols}, \dqt{lrm}, \dqt{orm}, etc. \end{description} \end{quote} \fi % ----------------------------------------- \lsm{} support for all these models works similarly to the examples we have presented. Note that generalized linear or mixed models, and several others such as survival models, typically employ link functions such as "log" or "logit". In most cases, the LS~means displayed are on the scale of the linear predictor, and any averaging over the reference grid is performed on the linear-predictor scale; but there are exceptions. Some objects have optional arguments that can be specified in the "ref.grid" or "lsmeans" call: see "?models" for details. \subsection{Ordinal-data example} The "clm" and "clmm" functions in \wixpkg{ordinal}, as well as the "polr" function in \wixpkg{MASS}, fit polytomous regression models to \wix{Likert-scale data}. They do this by modeling the ordinal response as a categorization of a continuous latent variable $S$, then estimating thresholds for this categorization and fitting a generalized linear model to the cumulative probabilities for each threshold. By default, "lsmeans" produces predictions of the \wix{latent variable}. \index{Example!ordinal response}\index{Examples!housing data} The example shown here is based on the \wixcode{housing} data in the \pkg{MASS} package, where the response variable is satisfaction ("Sat") on a three-point scale of low, medium, high; and predictors include "Type" (type of rental unit, four levels), "Infl" (influence on management of the unit, three levels), and "Cont" (contact with other residents, two levels). We will assume that the latent variable is normally distributed (by specifying a probit link). <>= library("ordinal") data(housing, package = "MASS") housing.clm <- clm(Sat ~ (Infl + Type + Cont)^2, data = housing, weights = Freq, link = "probit") lsmip(housing.clm, Cont ~ Infl | Type, layout = c(4,1)) @ \begin{figure} \begin{center} \includegraphics[width=6in]{using-lsmeans-housing-plot.pdf} \end{center} \caption{Interaction plot for the latent variable in the \code{housing} example.}\label{housing-plot} \end{figure} The plot is shown in \Fig{housing-plot}. Generally, the higher the influence, the higher the satisfaction. Overall $F$ tests of the "Infl" effect suggest that it is strong for all four housing types: <<>>= test(pairs(lsmeans(housing.clm, ~ Infl | Type)), joint = TRUE) @ The tests are asymptotic (signaled by "df2 = NA"), so they are actually chi-square tests for the statistics $X^2 = df_1\cdot F$ with $df_1$ degrees of freedom. Higher contact also seems to be associated with higher satisfaction, but terrace apartments may be an exception. Let's see: <<>>= test(pairs(lsmeans(housing.clm, ~ Cont | Type)), joint = TRUE) @ So the effect is inconclusive for both atria and terraces. The \wixcode{mode} argument may be used to choose what to examine. Modes \dqt{linear.predictor} and \dqt{cum.prob} create an additional pseudo-factor named "cut" for the thresholds at which the predictions are made. <<>>= ref.grid(housing.clm, mode = "cum.prob") @ So here are our estimated marginal probabilities for "Infl" of being less than highly satisfied: <<>>= lsmeans(housing.clm, ~ Infl, at = list(cut = "Medium|High"), mode = "cum.prob") @ Compare these results with those for the back-transformed linear predictor: <<>>= summary(lsmeans(housing.clm, ~ Infl, at = list(cut = "Medium|High"), mode = "linear.predictor"), type = "response") @ The results are similar, but somewhat different because of the back-transformation\ix{transformations} coming before (first case) or after (second case) averaging or computing confidence limits. \subsection{Chick weights, revisited}\index{Examples!chick weights}\index{Examples!nonlinear curves} Previously, we used the \wixcode{ChickWeight} data to illustrate the use of "lstrends". That example made the simplifying assumption that the growth trends are linear, which is clearly questionable. To do a better job of fitting the data, consider instead the idea of fitting a \wix{logistic curve} to each chick's data. The \pkg{stats} package provides the "SSlogis" function for this purpose: it is an S-shaped curve (scaled from the cdf of a logistic distribution) having three parameters "asym" (the asymptotic value at which it levels off), "xmid" (the $x$ coordinate of its inflection point), and "scal" (roughly the difference between the median and the .73rd quantile). Also, the \wixpkg{nlme} package's \wixcode{nlme} function can fit a set of \wix{nonlinear curves} such that the parameters of those curves may be modeled using a mixed-effects linear model. Accordingly, let us fit a model where each chick has a logistic curve for which the "asym" parameter varies randomly for each chick, and for which both "asym" and "xmid" depend on the chick's diet. We chose starting values by examining the curves and making a rough judgment of the typical asymptotic value, midpoint, and scale for each diet. We need to keep firmly in mind how factors are coded; so we explicitly show that we intend to use \dqt{contr.treatment} coding, by which the first mean is estimated directly, and the remaining estimates are offsets from that. We need a set of four starting values for "asym" and "xmid", and one for "scal". <<>>= require("nlme") options(contrasts = c("contr.treatment", "contr.poly")) Chick.nlme = nlme(weight ~ SSlogis(Time, asym, xmid, scal), data = ChickWeight, fixed = list(asym + xmid ~ Diet, scal ~ 1), random = asym ~ 1 | Chick, start = c(200, 100, 200, 100, 10, 0, 0, 0, 7)) Chick.nlme @ Now we can use "lsmeans" to compare the parameters based on "Diet": <<>>= cld(lsmeans(Chick.nlme, ~ Diet, param = "asym")) cld(lsmeans(Chick.nlme, ~ Diet, param = "xmid")) @ The result is that diet~3 has both a higher mean "asym" an a higher mean "xmid" than the other diets. This is compatible with the results of the earlier "lstrends" analysis, but grounded in terms of the parameters of the logistic curve. \subsection{Extending to more models}\ix{extending the \pkg{lsmeans} package} The functions "ref.grid" and "lsmeans" work by first reconstructing the dataset (so that the reference grid can be identified) and extracting needed information about the model, such as the regression coefficients, covariance matrix, and the linear functions associated with each point in the reference grid. For a fitted model of class, say, \dqt{modelobj}, these tasks are accomplished by defining S3 methods \wixcode{recover.data}".modelobj" and \wixcode{lsm.basis}".modelobj". The help page \dqt{extending-lsmeans} and the vignette by the same name provide details and examples. Developers of packages that fit models are encouraged to include support for \lsm{} by incorporating (and exporting) "recover.data" and "lsm.basis" methods for their model classes. \subsection{Bayesian models}\index{Examples!Bayesian Poisson regression} Certain \wix{Bayesian models} are now supported by \lsm{}. For illustration, consider a two-factor Poisson regression example given in the \wixpkg{MCMCpack} package: <<>>= library("MCMCpack") counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12) outcome <- gl(3, 1, 9) treatment <- gl(3, 3) posterior <- MCMCpoisson(counts ~ outcome + treatment, mcmc = 1000) @ The result is an "mcmc" object\index{mcmc@\code{mcmc} object} (defined in the \wixpkg{coda} package), but it has an added \dqt{call} attribute that enables "lsmeans" to do its work. Here are results for treatments, averaged over outcomes: <<>>= ( post.lsm <- lsmeans(posterior, "treatment") ) @ This is a frequentist summary, based on the mean and covariance of the regression parameters in the "posterior" sample. But \lsm{} provides an \wixcode{as.mcmc} method to obtain a sample from the posterior distribution of the LS~means\ix{posterior LS~means} (that is, the original posterior sample of regression coefficients, transformed by the appropriate linear functions.) <<>>= library("coda") summary(as.mcmc(post.lsm)) @ Since "as.mcmc" produces an "mcmc" object, any of the other available methods may be used with it. \section{Discussion} The design goal of \lsm{} is primarily to provide the functionality of the "LSMEANS"\index{SAS!LSMEANS} statement in various \SAS{} procedures. Thus its emphasis is on tabular results which, of course, may also be used as data for further analysis or graphics. By design, it can be extended with relative ease to additional model classes. A unique capability of \lsm{} is its explicit reliance on the concept of a reference grid, which I feel is a useful approach for understanding what is being computed. Some \lsm{} capabilities exceed those of \SAS, including the "lstrends" capability, more flexibility in organizing the output, and more built-in contrast families. In addition, \SAS{} does not allow LS~means for factor combinations when the model does not include the interaction of those factors; or creating a grid of covariate values using "at". There are a few other \R{} packages that provide capabilities that overlap with those of \lsm{}. The \wixpkg{effects} package \citep{effects,fox09} can compute LS~means. However, for an unbalanced dataset, it does not use equal \wix{weights}, but rather it appears to use ``outer'' weights, as described in Section~\ref{weights}. Also, it does not check \wix{estimability}, so some results could be questionable. The emphasis of \pkg{effects} is on graphical rather than tabular displays. It has special strengths for curve-fitting models such as splines. In contrast, \lsm{}'s strengths are more in the area of factorial models where one wants traditional summaries in the form of estimates, contrasts, and interaction plots. The \wixpkg{doBy} package \citep{doBy} provides an "LSmeans" function that has some of the capabilities of "lsmeans", but it produces a data frame rather than a reusable object. In earlier versions of the package, this function was named "popMeans". The package also has an "LSmatrix" function to obtain the linear functions needed to obtain LS~means. Also, the \wixpkg{lmerTest} package also offers an "lsmeans" function, as well as "difflsmeans" for differences of LS~means. 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Compute contrasts or linear functions of least-squares means, and comparisons of slopes. Plots and compact letter displays. License: GPL-2 | GPL-3 NeedsCompilation: no Packaged: 2016-11-19 02:12:07 UTC; rlenth Author: Russell Lenth [aut, cre, cph] Maintainer: Russell Lenth Repository: CRAN Date/Publication: 2016-11-19 10:15:04 lsmeans/man/0000755000175100001440000000000013013732335012505 5ustar hornikuserslsmeans/man/models.Rd0000644000175100001440000004611413013732335014265 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{models} \alias{models} \title{Models supported in \pkg{lsmeans}} \description{ Here we document what model objects may be used with \pkg{lsmeans}, and some special features of some of them. We start with those in the \pkg{stats} package; the other packages follow in alphabetical order. Certain objects are affected by optional arguments to functions that construct \code{ref.grid} or \code{lsmobj} objects, including \code{\link{ref.grid}}, \code{\link{lsmeans}}, \code{\link{lstrends}}, and \code{\link{lsmip}}. When \dQuote{arguments} are mentioned in the subsequent object-by-object documentation, we are talking about arguments in these constructors. Additional models can be supported by writing appropriate \code{recover.data} and \code{lsm.basis} methods. See \code{\link{extending-lsmeans}} and \code{vignette("extending")} for details. } \section{\pkg{stats} package}{ \describe{ \item{lm, aov, glm}{No extended features. Note that the \code{lm} support often extends to a number of model objects that inherit from it, such as \code{rlm} in the \pkg{MASS} package and \code{rsm} in the \pkg{rsm} package.} \item{mlm, maov, manova}{When there is a multivariate response, the different responses are treated as if they were levels of a factor -- named \code{rep.meas} by default. The \code{mult.name} argument may be used to change this name. The \code{mult.levs} argument may specify a named list of one or more sets of levels. If this has more than one element, then the multivariate levels are expressed as combinations of the named factor levels via the function \code{\link{expand.grid}}.} \item{aovlist}{Support for these objects is limited. To avoid strong biases in the predictions, the \code{contrasts} attribute of all factors should be of a type that sums to zero -- for example, \code{"contr.sum"}, \code{"contr.poly"}, or \code{"contr.helmert"} but \emph{not} \code{"contr.treatment"}. Only intra-block estimates of covariances are used. That is, if a factor appears in more than one error stratum, only the covariance structure from its lowest stratum is used in estimating standard errors. Degrees of freedom are obtained using the Satterthwaite method. In general, \code{aovList} support is best with balanced designs, and due caution in the use of contrasts. If a \code{vcov.} argument is supplied, it must yield a single covariance matrix for the unique fixed effects, and the degrees of freedom are set to \code{NA}.} }} %stats %%% !!! ALPHABETICAL FROM HERE !!! \section{\pkg{afex} package}{ \describe{ \item{mixed}{Support for \code{mixed} objects has been removed. Version 0.14 and later of \pkg{afex} provides new object classes with their own \pkg{lsmeans} support.}}} % afex %%%Support for the \code{full.model} element of these objects is the same as that for \code{merMod} in the \pkg{lme4} package -- see below. However, for \pkg{afex} versions 0.10-113 and earlier, the \code{data} argument is required in calls to \code{lsmeans} or \code{ref.grid}, as the information about the original dataset is not preserved in the object.} \section{\pkg{betareg} package}{ \describe{ \item{betareg}{The additional \code{mode} argument has possible values of \code{"response"}, \code{"link"}, \code{"precision"}, \code{"phi.link"}, \code{"variance"}, and \code{"quantile"}, which have the same meaning as the \code{type} argument in \code{predict.betareg} -- with the addition that \code{"phi.link"} is like \code{"link"}, but for the precision portion of the model. When \code{mode = "quantile"} is specified, the additional argument \code{quantile} (a numeric scalar or vector) specifies which quantile(s) to compute; the default is 0.5 (the median). Also in \code{"quantile"} mode, an additional variable \code{quantile} is added to the reference grid, and its levels are the values supplied. } }}% betareg \section{\pkg{CARBayes} package}{ \describe{ \item{carbayes}{The user \emph{must} supply (via the \code{data} argument) the dataset used in fitting the model. As with other MCMC-based objects, the summaries and such are frequentist, but the \code{as.mcmc} method provides a posterior sample of the desired quantities.} }}% CARBayes \section{\pkg{coxme} package}{ \describe{ \item{coxme}{No additional options. Support for these models is experimental; may throw errors or incorrect results.} }}% coxme \section{\pkg{gam} package}{ \describe{ \item{gam}{Currently, \code{gam} objects are not supported. Past versions of \pkg{lsmeans} appeared to support \code{gam} models owing to inheritance from \code{lm}, but the results were incorrect because spline features were ignored. We now explicitly trap \code{gam} objects to avoid tyhese misleading analyses.} }}% coxme \section{\pkg{gee} and \pkg{geepack} packages}{ These models all have more than one covariance estimate available, and it may be selected by supplying a string as the \code{vcov.method} argument. It is partially matched with the available choices; thus, for example, \samp{vcov = "n"} translates to \samp{vcov.method = "naive"} \describe{ \item{gee}{Available covariance estimates are specified in \code{vcov.method} as \code{"robust"} (the default) and \code{"naive"}.} \item{geeglm, geese}{Available covariance estimates are specified in \code{vcov.method} as \code{"vbeta"} (the default), \code{"vbeta.naiv"}, \code{"vbeta.j1s"}, or \code{"vbeta.fij"}. The aliases \code{"robust"} (for \code{"vbeta"}) and \code{"naive"} (for \code{"vbeta.naiv"} are also accepted.} } If a matrix or function is supplied as \code{vcov.method}, it is interpreted as a \code{vcov.} specification as described for \code{...} in \code{\link{ref.grid}}. }% geepack \section{\pkg{glmmADMB} package}{ \describe{ \item{glmmadmb}{No extended features.} }}% glmmadmb \section{\pkg{lme4} package}{ \describe{ \item{lmerMod}{There is an optional \code{mode} argument that defaults to \code{get.lsm.option("lmer.df")} (which in turn defaults to \code{"satterthwaite"}). The possible values are "satterthwaite", "kenward-roger", and "asymptotic" (these are partially matched and case-insensitive). With \code{"satterthwaite"}, d.f. are obtained using code from the \pkg{lmerTest} package, if installed. With \code{"kenward-roger"}, d.f. are obtained using code from the \pkg{pbkrtest} package, if installed. With \code{"asymptotic"}, or if the needed package is not installed, d.f. are set to \code{NA}. A by-product of the Kenward-Roger method is that the covariance matrix is adjusted using \code{\link[pbkrtest]{vcovAdj}}. This can require considerable computation; so to avoid that overhead, the user should opt for the Satterthwaite or asymptotic method; or, for backward compatibility, may disable the use of \pkg{pbkrtest} via \samp{lsm.options(disable.pbkrtest=TRUE)} (this does not disable the \pkg{pbkrtest} package entirely, just its use in \pkg{lsmeans}). The computation time required depends roughly on the number of observations, \emph{N}, in the design matrix (because a major part of the computation involves inverting an \emph{N x N} matrix). Thus, \pkg{pbkrtest} is automatically disabled if \emph{N} exceeds the value of \code{get.lsm.option("pbkrtest.limit")}. If desired, the user may use \code{lsm.options} to adjust this limit from the default of 3000. The \code{df} argument may be used to specify some other degrees of freedom. Note that if \code{df} and \code{method = "satterthwaite"} are both specified, the covariance matrix is adjusted but the K-R degrees of freedom are not used. } \item{glmerMod}{No degrees of freedom are available for these objects, so tests and confidence intervals are asymptotic.} }}% lme4 \section{\pkg{lme4.0} package}{ \describe{ \item{mer}{Only asymptotic results are available (no d.f.).} }}% lme4.0 \section{\pkg{MASS} package}{ \describe{ \item{glmmPQL}{Supported by virtue of inheritance from \code{lme} in the \pkg{nlme} package.} \item{glm.nb}{Supported by virtue of inheritance from \code{glm}.} \item{polr}{There are two optional arguments: \code{mode} and \code{rescale} (which defaults to \samp{c(0,1)}). For details, see the documentation below regarding the support for the \pkg{ordinal} package, which produces comparable objects (but since \code{polr} does not support scale models, \code{mode="scale"} is not supported). Tests and confidence intervals are asymptotic.} \item{rlm}{Supported by virtue of inheritance from \code{lm}.} }}% MASS %\section{\pkg{mgcv} package}{ %\describe{ %\item{gam}{Supported by virtue of inheritance from \code{glm}.} %\item{gamm}{Not supported at this time.} %}}% mgcv \section{\pkg{MCMCglmm} package}{ \describe{ \item{MCMCglmm}{Currently, I have found no way to reconstruct the data based on information in the object; thus, you \emph{must} provide the dataset via the \code{data} argument. In addition, the \code{contrasts} specifications are not recoverable from the object, so the system default must match what was actually used in fitting the model. The usual \code{summary}, \code{test}, etc. methods provide frequentist analyses of the results based on the posterior means and covariances. However, an \code{as.mcmc} method is provided that creates an \code{mcmc} object that can be summarized or plotted using the \pkg{coda} package. It provides a posterior sample of lsmeans for the given reference grid, based on the posterior sample of the fixed effects from the \code{MCMCglmm} object.} }}% MCMCglmm \section{\pkg{MCMCpack} package (and perhaps others)}{ \describe{ \item{mcmc}{Certain linear-model or mixed-model objects are of class \code{mcmc}, and contain a sample from the posterior distribution of fixed-effect coefficients. In some cases (e.g., results of \code{MCMCregress} and \code{MCMCpoisson}), the object may include a \code{"call"} attribute that \code{lsmeans} can use to reconstruct the data and obtain a basis for the least-squares means. If not, a \code{formula} and \code{data} argument are provided that may help produce the right results. In addition, the \code{contrasts} specifications are not recoverable from the object, so the system default must match what was actually used in fitting the model. As for other MCMC-based objects, the summaries and such are frequentist, but the \code{as.mcmc} method provides a posterior sample of the desired quantities.} }}% MCMCpack \section{\pkg{nlme} package}{ \describe{ \item{gls}{No additional features. Degrees of freedom are computed using \code{N - p} in \code{object$dims}. This is consistent with \code{nlme:::summary.gls} but seems questionable.} \item{lme}{Degrees of freedom are obtained using a containment method, i.e., the minimum of those elements of \code{object$fixDF$X} receiving nonzero weight (but with a correction to the \code{lme} object's intercept df). (This is similar to \pkg{SAS}'s containment method, but I believe \pkg{SAS} does it incorrectly when the estimands are not contrasts.) The optional argument \code{sigmaAdjust} (defaults to \code{TRUE}) will adjust standard errors like in \code{\link[nlme]{summary.lme}} when the model is fitted using the \code{"ML"} method. \bold{Note:} \code{sigmaAdjust} is comparable to \code{adjustSigma} in \code{\link[nlme]{summary.lme}} but it is renamed to avoid conflicting with \code{adjust}.} \item{nlme}{Support is provided for inferences on parameters named in the \code{fixed} part of the model. The user \emph{must} specify \code{param} in the call and give the name of a parameter that appears in the right-hand side of a \code{fixed} formula. Degrees of freedom are obtained using the containment-like method described above for \code{lme}.} }}% nlme \section{\pkg{nnet} package}{ \describe{ \item{multinom}{ The reference grid includes a pseudo-factor with the same name and levels as the multinomial response. There is an optional \code{mode} argument which should match \code{"prob"} or \code{"latent"}. With \code{mode = "prob"}, the reference-grid predictions consist of the estimated multinomial probabilities. The \code{"latent"} mode returns the linear predictor, recentered so that it averages to zero over the levels of the response variable (similar to sum-to-zero contrasts). Thus each latent variable can be regarded as the log probability at that level minus the average log probability over all levels. Please note that, because the probabilities sum to 1 (and the latent values sum to 0) over the multivariate-response levels, all sensible results from \code{lsmeans} must involve that response as one of the factors. For example, if \code{resp} is a response with \eqn{k} levels, \code{lsmeans(model, ~ resp | trt)} will yield the estimated multinomial distribution for each \code{trt}; but \code{lsmeans(model, ~ trt)} will just yield the average probability of \eqn{1/k} for each \code{trt}. }}}% nnet, multinom \section{\pkg{ordinal} package}{ \describe{ \item{clm,clmm}{The reference grid will include all variables that appear in the main model as well as those in the \code{scale} or \code{nominal} models. There are two optional arguments: \code{mode} (a character string) and \code{rescale} (which defaults to \samp{c(0,1)}). \code{mode} should match one of \code{"latent"} (the default), \code{"linear.predictor"}, \code{"cum.prob"}, \code{"exc.prob"}, \code{"prob"}, \code{"mean.class"}, or \code{"scale"}. With \samp{mode = "latent"}, the reference-grid predictions are made on the scale of the latent variable implied by the model. The scale and location of this latent variable are arbitrary, and may be altered via \code{rescale}. The predictions are multiplied by \samp{rescale[2]}, then \samp{rescale[1]} is added. Keep in mind that the scaling is related to the link function used in the model; for example, changing from a probit link to a logistic link will inflate the latent values by around \eqn{\pi/\sqrt{3}}{pi/sqrt(3)}, all other things being equal. \code{rescale} has no effect for other values of \code{mode}. With \samp{mode = "linear.predictor"} \code{mode = "cum.prob"}, and \code{mode = "exc.prob"}, the boundaries between categories (i.e., thresholds) in the ordinal response are included in the reference grid as a pseudo-factor named \code{cut}. The reference-grid predictions are then of the cumulative probabilities at each threshold (for \code{mode = "cum.prob"}), exceedance probabilities (one minus cumulative probabilities, for \code{mode = "exc.prob"}), or the link function thereof (for \code{mode = "linear.predictor"}). With \code{mode = "prob"}, a pseudo-factor with the same name as the model's response variable is created, and the grid predictions are of the probabilities of each class of the ordinal response. With \code{"mean.class"}, the returned results are means of the ordinal response, interpreted as a numeric value from 1 to the number of classes, using the \code{"prob"} results as the estimated probability distribution for each case. With \code{mode = "scale"}, and the fitted object incorporates a scale model, least-squares means are obtained for the factors in the scale model instead of the response model. The grid is constructed using only the factors in the scale model. Any grid point that is non-estimable by either the location or the scale model (if present) is set to \code{NA}, and any LS-means involving such a grid point will also be non-estimable. A consequence of this is that if there is a rank-deficient \code{scale} model, and then \emph{all} latent responses become non-estimable because the predictions are made using the average log-scale estimate. Tests and confidence intervals are asymptotic.} }}% ordinal \section{\pkg{pscl} package}{ \describe{ \item{hurdle, zeroinfl}{ Two optional arguments -- \code{mode} and \code{lin.pred} -- are provided. The \code{mode} argument has possible values \code{"response"} (the default), \code{"count"}, \code{"zero"}, or \code{"prob0"}. \code{lin.pred} is logical and defaults to \code{FALSE}. With \code{lin.pred = FALSE}, the results are comparable to those returned by \code{predict(..., type = "response")}, \code{predict(..., type = "count")}, \code{predict(..., type = "zero")}, or \code{predict(..., type = "prob")[, 1]}. See the documentation for \code{\link[pscl]{predict.hurdle}} and \code{\link[pscl]{predict.zeroinfl}}. The option \code{lin.pred = TRUE} only applies to \code{mode = "count"} and \code{mode = "zero"}. The results returned are on the linear-predictor scale, with the same transformation as the link function in that part of the model. The predictions for a reference grid with \code{mode = "count"}, \code{lin.pred = TRUE}, and \code{type = "response"} will be the same as those obtained with \code{lin.pred = FALSE} and \code{mode = "count"}; however, any LS means derived from these grids will be defferent, because the averaging is done on the log-count scale and the actual count scale, respectively -- thereby producing geometric means versus arithmetic means of the predictions. If the \code{vcov.} argument is used (see details in \code{\link{ref.grid}}), it must yield a matrix of the same size as would be obtained using \code{\link[pscl]{vcov.hurdle}} or \code{\link[pscl]{vcov.zeroinfl}} with its \code{model} argument set to \code{("full", "count", "zero")} in respective correspondence with \code{mode} of \code{("mean", "count", "zero")}. If \code{vcov.} is a function, it must support the \code{model} argument. } }}% pscl \section{\pkg{rms} package}{ \describe{ \item{Potential masking issue}{ Both \pkg{rms} and \pkg{lsmeans} offer \code{contrast} methods, and whichever package is loaded later masks the other. Thus, you may need to call \code{lsmeans::contrast} or \code{rms::contrast} explicitly to access the one you want. } \item{Objects inheriting from rms}{ Standard support is provided. However, with models having more than one intercept (e.g. from \code{orm}), a \code{mode} argument is provided that works similarly to that for the \pkg{ordinal} package. The available modes are \code{"middle"} (the default), \code{"latent"}, \code{"linear.predictor"}, \code{"cum.prob"}, \code{"exc.prob"}, \code{"prob"}, and \code{"mean.class"}. All are as described for the \pkg{ordinal} package, except as noted below. With \code{mode = "middle"} (this is the default), the middle intercept is used, comparable to the default for \pkg{rms}'s \code{Predict} function. This is quite similar in concept to \code{mode = "latent"}, where all intercepts are averaged together. Results for \code{mode = "linear.predictor"} are reversed from those in the \pkg{ordinal} package, because \code{orm} models predict the link function of the \emph{upper}-tail (exceedance) probabilities. With \code{mode = "prob"}, a pseudo-factor is created having the same name as the model response variable, but its levels are always integers \samp{1, 2, ...} regardless of the levels of the original response. } }}% rms \section{\pkg{survival} package}{ \describe{ \item{survreg, coxph}{No extended features.} }}% survival %\references{} \author{ Russell V. Lenth } %\note{} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{ref.grid}}, \code{\link{lsm.basis}} } \keyword{ models } \keyword{ regression } \keyword{ htest } lsmeans/man/oranges.Rd0000644000175100001440000000272113013732335014434 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{oranges} \alias{oranges} \docType{data} \title{ Orange sales } \description{ This example dataset on sales of oranges has two factors, two covariates, and two responses. There is one observation per factor combination. } \usage{data(oranges)} \format{ A data frame with 36 observations on the following 6 variables. \describe{ \item{\code{store}}{a factor with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6}. The store that was observed.} \item{\code{day}}{a factor with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6}. The day the observation was taken (same for each store).} \item{\code{price1}}{a numeric vector. Price of variety 1.} \item{\code{price2}}{a numeric vector. Price of variety 2.} \item{\code{sales1}}{a numeric vector. Sales (per customer) of variety 1.} \item{\code{sales2}}{a numeric vector. Sales (per customer) of variety 2.} } } %\details{} \source{ Download from \url{http://ftp.sas.com/samples/A56655}. } \references{ Littell, R., Stroup W., Freund, R. (2002) \emph{SAS For Linear Models} (4th edition). SAS Institute. ISBN 1-59047-023-0. } \examples{ require(lsmeans) # Example on p.244 of Littell et al. oranges.lm <- lm(sales1 ~ price1*day, data = oranges) lsmeans(oranges.lm, "day") # Example on p.246 lsmeans(oranges.lm, "day", at = list(price1 = 0)) } \keyword{datasets} lsmeans/man/ref.grid.Rd0000644000175100001440000002166413013732335014505 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{ref.grid} \alias{ref.grid} \alias{.Last.ref.grid} % handy for extra documentation... \alias{ref.grid.object} \alias{summary.ref.grid.object} \title{ Create a reference grid from a fitted model } \description{ Using a fitted model object, determine a reference grid for which least-squares means are defined. The resulting \code{ref.grid} object encapsulates all the information needed to calculate LS means and make inferences on them. } \usage{ ref.grid(object, at, cov.reduce = mean, mult.name, mult.levs, options = get.lsm.option("ref.grid"), data, df, type, transform = c("none", "response", "log"), ...) .Last.ref.grid } \arguments{ \item{object}{An object produced by a supported model-fitting function, such as \code{lm}. Many models are supported. See \code{\link{models}}.} \item{at}{Optional named list of levels for the corresponding variables} \item{cov.reduce}{A function, logical value, or formula; or a named list of these. Each covariate \emph{not specified in} \code{at} is reduced according to these specifications. If a single function, it is applied to each covariate. If logical and \code{TRUE}, \code{mean} is used. If logical and \code{FALSE}, it is equivalent to specifying \samp{function(x) sort(unique(x))}, and these values are considered part of the reference grid; thus, it is a handy alternative to specifying these same values in \code{at}. If a formula (which must be two-sided), then a model is fitted to that formula using \code{\link{lm}}; then in the reference grid, its response variable is set to the results of \code{\link{predict}} for that model, with the reference grid as \code{newdata}. (This is done \emph{after} the reference grid is determined.) A formula is appropriate here when you think experimental conditions affect the covariate as well as the response. If \code{cov.reduce} is a named list, then the above criteria are used to determine what to do with covariates named in the list. (However, formula elements do not need to be named, as those names are determined from the formulas' left-hand sides.) Any unresolved covariates are reduced using \code{"mean"}. Any \code{cov.reduce} specification for a covariate also named in \code{at} is ignored. } % end of \item{cov.reduce} \item{mult.name}{Character, the name to give to the \dQuote{factor} whose levels delineate the elements of a multivariate response. If this is provided, it overrides the default name, e.g., \code{"rep.meas"} for an \code{\link[=lm]{mlm}} object or \code{"cut"} for a \code{\link[MASS]{polr}} object.} \item{mult.levs}{A named list of levels for the dimensions of a multivariate response. If there is more than one element, the combinations of levels are used, in \code{\link{expand.grid}} order. The (total) number of levels must match the number of dimensions. If \code{mult.name} is specified, this argument is ignored.} \item{options}{If non-\code{NULL}, a named \code{list} of arguments to pass to \code{\link{update}}, just after the object is constructed.} \item{data}{A \code{data.frame} to use to obtain information about the predictors (e.g. factor levels). If missing, then \code{\link{recover.data}} is used to attempt to reconstruct the data.} \item{df}{This is a courtesy shortcut, equivalent to specifying \code{options(df = df)}. See \code{\link{update}}.} \item{type}{If provided, this is saved as the \code{"predict.type"} setting. See \code{\link{update}}} \item{transform}{If other than \code{"none"}, the reference grid is reconstructed via \code{\link{regrid}} with the given \code{transform} argument. See Details.} \item{\dots}{Optional arguments passed to \code{\link{lsm.basis}}, such as \code{vcov.} (see Details below) or options for certain models (see \link{models}).} } %--- end of \arguments \details{The reference grid consists of combinations of independent variables over which predictions are made. Least-squares means are defined as these predictions, or marginal averages thereof. The grid is determined by first reconstructing the data used in fitting the model (see \code{\link{recover.data}}), or by using the \code{data.frame} provided in \code{context}. The default reference grid is determined by the observed levels of any factors, the ordered unique values of character-valued predictors, and the results of \code{cov.reduce} for numeric predictors. These may be overridden using \code{at}. Ability to support a particular class of \code{object} depends on the existence of \code{recover.data} and \code{lsm.basis} methods -- see \link{extending-lsmeans} for details. The call \code{methods("recover.data")} will help identify these. In certain models, (e.g., results of \code{\link[lme4]{glmer.nb}}), it is not possible to identify the original dataset. In such cases, we can work around this by setting \code{data} equal to the dataset used in fitting the model, or a suitable subset. Only the complete cases in \code{data} are used, so it may be necessary to exclude some unused variables. Using \code{data} can also help save computing, especially when the dataset is large. In any case, \code{data} must represent all factor levels used in fitting the model. It \emph{cannot} be used as an alternative to \code{at}. (Note: If there is a pattern of \code{NAs} that caused one or more factor levels to be excluded when fitting the model, then \code{data} should also exclude those levels.) By default, the variance-covariance matrix for the fixed effects is obtained from \code{object}, usually via its \code{\link{vcov}} method. However, the user may override this via a \code{vcov.} argument, specifying a matrix or a function. If a matrix, it must be square and of the same dimension and parameter order of the fixed efefcts. If a function, must return a suitable matrix when it is called with \code{object} as its only argument. There is a subtle difference between specifying \samp{type = "response"} and \samp{transform = "response"}. While the summary statistics for the grid itself are the same, subsequent use in \code{\link{lsmeans}} will yield different results if there is a response transformation. With \samp{type = "response"}, LS means are computed by averaging together predictions on the \emph{linear-predictor} scale and then back-transforming to the response scale; while with \samp{transform = "response"}, the predictions are already on the response scale so that the LS means will be the arithmetic means of those response-scale predictions. To add further to the possibilities, \emph{geometric} means of the response-scale predictions are obtainable via \samp{transform = "log", type = "response"}. The most recent result of \code{ref.grid}, whether called directly or indirectly via \code{\link{lsmeans}}, \code{\link{lstrends}}, or some other function that calls one of these, is saved in the user's environment as \code{.Last.ref.grid}. This facilitates checking what reference grid was used, or reusing the same reference grid for further calculations. This automatic saving is enabled by default, but may be disabled via \samp{lsm.options(save.ref.grid = FALSE)}, and re-enabled by specifying \code{TRUE}. } % --- end of details \value{An S4 object of class \code{"ref.grid"} (see \code{\link{ref.grid-class}}). These objects encapsulate everything needed to do calculations and inferences for least-squares means, and contain nothing that depends on the model-fitting procedure. As a side effect, the result is also saved as \code{.Last.ref.grid} (in the global environment, unless this variable is found in another position). } %%%\references{} \author{ Russell V. Lenth } %\note{} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ See also \code{\link[lsmeans]{summary}} and other methods for the returned objects. Reference grids are fundamental to \code{\link{lsmeans}}. Click here for more on the \code{\link[=ref.grid-class]{ref.grid}} class. Supported models are detailed in \code{\link{models}}. } \examples{ require(lsmeans) fiber.lm <- lm(strength ~ machine*diameter, data = fiber) ref.grid(fiber.lm) summary(ref.grid(fiber.lm)) ref.grid(fiber.lm, at = list(diameter = c(15, 25))) \dontrun{ # We could substitute the sandwich estimator vcovHAC(fiber.lm) # as follows: require(sandwich) summary(ref.grid(fiber.lm, vcov. = vcovHAC)) } # If we thought that the machines affect the diameters # (admittedly not plausible in this example), then we should use: ref.grid(fiber.lm, cov.reduce = diameter~machine) # Multivariate example MOats.lm = lm(yield ~ Block + Variety, data = MOats) ref.grid(MOats.lm, mult.name = "nitro") # silly illustration of how to use 'mult.levs' ref.grid(MOats.lm, mult.levs = list(T=LETTERS[1:2], U=letters[1:2])) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ regression } \keyword{ models }% __ONLY ONE__ keyword per line lsmeans/man/pairwise.lsmc.Rd0000644000175100001440000001337513013732335015565 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{pairwise.lsmc} \alias{pairwise.lsmc} \alias{revpairwise.lsmc} \alias{poly.lsmc} \alias{trt.vs.ctrl.lsmc} \alias{trt.vs.ctrl1.lsmc} \alias{trt.vs.ctrlk.lsmc} \alias{eff.lsmc} \alias{del.eff.lsmc} \alias{tukey.lsmc} \alias{dunnett.lsmc} \alias{consec.lsmc} \alias{mean_chg.lsmc} \title{ Contrast families } \description{ These functions return standard sets of contrast coefficients. The name of any of these functions (with the \code{.lsmc} omitted) may be used as the \code{method} argument in \code{\link{contrast}}, or as the \code{contr} argument or left-hand side of a \code{spec} formula in \code{\link{lsmeans}}. } \usage{ pairwise.lsmc(levs, ...) revpairwise.lsmc(levs, ...) tukey.lsmc(levs, reverse = FALSE) poly.lsmc(levs, max.degree = min(6, k - 1)) trt.vs.ctrl.lsmc(levs, ref = 1) trt.vs.ctrl1.lsmc(levs, ...) trt.vs.ctrlk.lsmc(levs, ...) dunnett.lsmc(levs, ref = 1) consec.lsmc(levs, reverse = FALSE, ...) mean_chg.lsmc(levs, reverse = FALSE, ...) eff.lsmc(levs, ...) del.eff.lsmc(levs, ...) } \arguments{ \item{levs}{Vector of factor levels} \item{\dots}{Additional arguments, ignored but needed to make these functions interchangeable} \item{max.degree}{The maximum degree of the polynomial contrasts in \code{poly.lsmc}} \item{reverse}{Logical value to determine the direction of comparisons, e.g., pairwise (if \code{TRUE}) or reverse-pairwise (if \code{FALSE}) comparisons.} \item{ref}{Reference level (or control group) in \code{trt.vs.ctrl.lsmc}} } \details{ Each contrast family has a default multiple-testing adjustment as noted below. These adjustments are often only approximate; for a more exacting adjustment, use the interfaces provided to \code{\link[multcomp]{glht}} in the \pkg{multcomp} package. \code{pairwise.lsmc}, \code{revpairwise.lsmc}, and \code{tukey.lsmc} generate contrasts for all pairwise comparisons among least-squares means at the levels in \code{levs}. The distinction is in which direction they are subtracted. For factor levels A, B, C, D, \code{pairwise.lsmc} generates the comparisons A-B, A-C, A-D, B-C, B-D, and C-D, whereas \code{revpairwise.lsmc} generates B-A, C-A, C-B, D-A, D-B, and D-C. \code{tukey.lsmc} invokes \code{pairwise.lsmc} or \code{revpairwise.lsmc} depending on \code{reverse}. The default multiplicity adjustment method is \code{"tukey"}, which is approximate when the standard errors differ. \code{poly.lsmc} generates orthogonal polynomial contrasts, assuming equally-spaced factor levels. These are derived from the \code{\link{poly}} function, but an ad hoc algorithm is used to scale them to integer coefficients that are (usually) the same as in published tables of orthogonal polynomial contrasts. The default multiplicity adjustment method is \code{"none"}. \code{trt.vs.ctrl.lsmc} and its relatives generate contrasts for comparing one level (or the average over specified levels) with each of the other levels. The argument \code{ref} should be the \emph{index}(es) (not the labels) of the reference level(s). \code{trt.vs.ctrl1.lsmc} is the same as \code{trt.vs.ctrl} with a reference value of \code{1}, and \code{trt.vs.ctrlk.lsmc} is the same as \code{trt.vs.ctrl} with a reference value of \code{length(levs)}. \code{dunnett.lsmc} is the same as \code{trt.vs.ctrl}. The default multiplicity adjustment method is \code{"dunnettx"}, a close approximation to the Dunnett adjustment. \code{consec.lsmc} and \code{mean_chg.lsmc} are useful for contrasting treatments that occur in sequence. For a factor with levels A, B, C, D, E, \code{consec.lsmc} generates the comparisons B-A, C-B, and D-C, while \code{mean_chg.lsmc} generates the contrasts (B+C+D)/3 - A, (C+D)/2 - (A+B)/2, and D - (A+B+C)/3. With \code{reverse = TRUE}, these differences go in the opposite direction. \code{eff.lsmc} and \code{del.eff.lsmc} generate contrasts that compare each level with the average over all levels (in \code{eff.lsmc}) or over all other levels (in \code{del.eff.lsmc}). These differ only in how they are scaled. For a set of \eqn{k} lsmeans, \code{del.eff.lsmc} gives weight \eqn{1} to one lsmean and weight \eqn{-1/(k-1)} to the others, while \code{eff.lsmc} gives weights \eqn{(k-1)/k} and \eqn{-1/k} respectively, as in subtracting the overall lsmean from each lsmean. The default multiplicity adjustment method is \code{"fdr"}. This is a Bonferroni-based method and is slightly conservative; see \code{\link{p.adjust}} } \value{ A \code{data.frame}, each column containing contrast coefficients for \code{levs}. The \code{"desc"} attribute is used to label the results in \code{lsmeans}, and the \code{"adjust"} attribute gives the default adjustment method for multiplicity. } \author{ Russell V. Lenth } \note{ You may create your own contrast functions, using these as guides. A function named \code{mycontr.lsmc} may be invoked in \code{lsmeans} via, e.g., \preformatted{lsmeans(\var{object}, mycontr ~ \var{factor})} The \code{"desc"}, \code{"adjust"}, and \code{"offset"} attributes are optional; if present, these are passed to \code{contrast}. If absent, the root name of the function is used as \code{"desc"}, and no adjustment is requested for p values. See the examples. } \seealso{ \code{\link{lsmeans}}, \code{\link[multcomp]{glht}} } \examples{ ### View orthogonal polynomials for 4 levels poly.lsmc(1:4) ### Setting up a custom contrast function helmert.lsmc <- function(levs, ...) { M <- as.data.frame(contr.helmert(levs)) names(M) <- paste(levs[-1],"vs earlier") attr(M, "desc") <- "Helmert contrasts" M } warp.lm <- lm(breaks ~ wool*tension, data = warpbreaks) lsmeans(warp.lm, helmert ~ tension | wool) } \keyword{ models } \keyword{ regression } \keyword{ htest } lsmeans/man/lsmeans.Rd0000644000175100001440000004123313013732335014441 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{lsmeans} \alias{lsmeans} \alias{lsmeans.formula} \alias{lsmeans.list} \alias{lsmeans.character} \alias{lsmeans.character.ref.grid} \alias{lstrends} \alias{lsmobj} \alias{pmmeans} \alias{pmtrends} \alias{pmmobj} \title{Least-squares means (or predicted marginal means)} \description{ Compute least-squares means (predicted marginal means) for specified factors or factor combinations in a linear model, and optionally comparisons or contrasts among them. } \usage{ \method{lsmeans}{character}(object, specs, ...) ## (used when 'specs' is 'character') \method{lsmeans}{character.ref.grid}(object, specs, by = NULL, fac.reduce = function(coefs) apply(coefs, 2, mean), contr, options = getOption("lsmeans")$lsmeans, weights, trend, ...) ## (used when 'object' is a 'ref.grid' and 'specs' is 'character') \method{lsmeans}{list}(object, specs, ...) ## (used when 'specs' is a 'list') \method{lsmeans}{formula}(object, specs, contr.list, trend, ...) ## (used when 'specs' is a 'formula') lstrends(model, specs, var, delta.var = 0.01 * rng, data, transform = c("none", "response"), ...) lsmobj(bhat, V, levels, linfct, df = NA, post.beta = matrix(NA), ...) pmmeans(...) pmtrends(...) pmmobj(...) } \arguments{ \item{object}{ An object of class \code{ref.grid}; or a fitted model object that is supported, such as the result of a call to \code{lm} or \code{lmer}. Many fitted-model objects are supported; see \code{link{models}} for details.} \item{specs}{ A \code{character} vector specifying the names of the predictors over which LS-means are desired. \code{specs} may also be a \code{formula} or a \code{list} (optionally named) of valid \code{spec}s. Use of formulas is described in the Details section below.} \item{by}{ A character vector specifying the names of predictors to condition on.} \item{fac.reduce}{ A function that combines the rows of a matrix into a single vector. This implements the ``marginal averaging'' aspect of least-squares means. The default is the mean of the rows. Typically if it is overridden, it would be some kind of weighted mean of the rows. If \code{fac.reduce} is nonlinear, bizarre results are likely, and LS means will not be interpretable. If the \code{weights} argument is non-missing, \code{fac.reduce} is ignored.} \item{contr}{ A \code{list} of contrast coefficients to apply to the least-squares means -- or the root name of an \code{.lsmc} function that returns such coefficients. In addition, \code{contr = "cld"} is an alternative way to invoke the \code{\link[lsmeans]{cld}} function. See \code{\link{contrast}} for more details on contrasts. NOTE: \code{contr} is ignored when \code{specs} is a formula. } \item{contr.list}{ A named \code{list} of \code{list}s of contrast coefficients, as for \code{contr}. This is used only in the formula method; see Details below. } \item{options}{If non-\code{NULL}, a named \code{list} of arguments to pass to \code{\link{update}}, just after the object is constructed.} \item{weights}{ Numeric vector, numeric matrix, or character string specifying weights to use in averaging predictions. If a vector, its length must equal the number of predictions to be averaged to obtain each least-squares mean. If a matrix, each row of the matrix is used in turn, wrapping back to the first row as needed. When in doubt about what is being averaged (or how many), first call with \code{weights = "show.levels"}.) If a string, it should partially match one of the following: \describe{ \item{\code{"equal"}}{Use an equally weighted average.} \item{\code{"proportional"}}{Weight in proportion to the frequencies (in the original data) of the factor combinations that are averaged over.} \item{\code{"outer"}}{Weight in proportion to each individual factor's marginal frequencies. Thus, the weights for a combination of factors are the outer product of the one-factor margins} \item{\code{"cells"}}{Weight according to the frequencies of the cells being averaged.} \item{\code{show.levels}}{This is a convenience feature for understanding what is being averaged over. Instead of a table of LS means, this causes the function to return a table showing the levels that are averaged over, in the order they appear.} } Outer weights are like the 'expected' counts in a chi-square test of independence, and will yield the same results as those obtained by proportional averaging with one factor at a time. All except \code{"cells"} uses the same set of weights for each mean. In a model where the predicted values are the cell means, cell weights will yield the raw averages of the data for the factors involved. Note: If weights were used in fitting the model, then weight totals are used in place of frequencies in these schemes. If \code{weights} is used, \code{fac.reduce} is ignored. } \item{trend}{ Including this argument is an alternative way of calling \code{lstrends} with \code{trend} as its \code{var} argument and \code{object} as its \code{model}. } \item{model}{ A supported model object (\emph{not} a \code{ref.grid}). } \item{var}{ Character giving the name of a variable with respect to which a difference quotient of the linear predictors is computed. In order for this to be useful, \code{var} should be a numeric predictor that interacts with at least one factor in \code{specs}. Then instead of computing least-squares means, we compute and compare the slopes of the \code{var} trend over levels of the specified other predictor(s). As in least-squares means, marginal averages are computed when some variables in the reference grid are excluded for the specification. The user may specify some monotone function of one variable, e.g., \code{var = "log(dose)"}. If so, the chain rule is applied. Note that, in this example, if \code{model} contains \code{log(dose)} as a predictor, we will be comparing the slopes estimated by that model, whereas specifying \code{var = "dose"} would perform a transformation of those slopes. } \item{delta.var}{ The value of \emph{h} to use in forming the difference quotient \emph{(f(x+h) - f(x))/h}. Changing it (especially changing its sign) may be necessary to avoid numerical problems such as logs of negative numbers. The default value is 1/100 of the range of \code{var} over the dataset. } \item{data}{As in \code{\link{ref.grid}}, you may use this argument to supply the dataset used in fitting the model, for situations where it is not possible to reconstruct the data. Otherwise, leave it missing.} \item{transform}{In \code{lstrends}, if \code{object} has a response transformation, then specifying \code{transform = "response"} will cause \code{lstrends} to calculate the trends after back-transforming to the response scale. This is done using the chain rule, and standard errors are estimated via the delta method. With \code{transform = "none"} (the default), the trends are calculated on the scale of the linear predictor, without back-transforming it. This argument works similarly to the \code{transform} arguent in \code{\link{ref.grid}} (but without a \code{"log"} option), in that the returned object is re-gridded to the new scale (see also \code{\link{regrid}}).} \item{bhat}{Numeric. Vector of regression coefficients.} \item{V}{Square matrix. Covariance matrix of \code{bhat}} \item{levels}{Named list or vector. Levels of factor(s) that define the estimates defined by \code{linfct}. If not a list, we assume one factor named \code{"level"}} \item{linfct}{Matrix. Linear functions of \code{bhat} for each combination of \code{levels}} \item{df}{Numeric or function with arguments \code{(x,dfargs)}. If a number, that is used for the degrees of freedom. If a function, it should return the degrees of freedom for \code{sum(x*bhat)}; if additional parameters are needed, include them in \code{\dots} as \code{dfargs} (not abbreviated).} \item{post.beta}{Matrix whose columns comprise a sample from the posterior distribution of the regression coefficients (so that typically, the column averages will be \code{bhat}). A 1 x 1 matrix of \code{NA} indicates that such a sample is unavailable.} \item{\dots}{Additional arguments passed to other methods or to \code{\link{ref.grid}}. For example, \code{vcov.} may be used to override the default covariance estimate, and some models allow additional options. Some models require \code{data} to be given explicitly. See the help pages for \code{\link{ref.grid}} and \link{models}. In addition, if the model formula contains references to variables that are not predictors, you must provide a \code{params} argument with a list of their names; see the example below for \code{Oatsq.lm}.} } \details{ Least-squares means (also called predicted marginal means) are predictions from a linear model over a \emph{reference grid}, or marginal averages thereof. They have been popularized by \pkg{SAS} (SAS Institute, 2012). The \code{\link{ref.grid}} function identifies/creates the reference grid upon which \code{lsmeans} is based. For those who prefer the term \dQuote{predicted marginal means}, courtesy wrappers \code{pmmeans}, \code{pmtrends}, and \code{pmmobj} are provided that behave identically to those that start with \code{ls}, except that estimates are relabeled accordingly (e.g., \code{lsmean} becomes \code{pmmean}). If \code{specs} is a \code{formula}, it should be of the form \code{~ specs}, \code{~ specs | by}, \code{contr ~ specs}, or \code{contr ~ specs | by}. The formula is parsed and the variables therein are used as the arguments \code{specs}, \code{by}, and \code{contr} as indicated. The left-hand side is optional, but if specified it should be the name of a contrast family (e.g., \code{pairwise}) or of a sub-list of \code{contr.list}. Operators like \code{*} or \code{:} are necessary to delineate names in the formulas, but otherwise are ignored. In the special case where the mean (or weighted mean) of all the predictions is desired, specify \code{specs} as \code{~ 1} or \code{"1"}. A number of standard contrast families are provided. They can be identified as functions having names ending in \code{.lsmc} -- use \preformatted{ls("package:lsmeans", pat=".lsmc")} to list them. See the documentation for \code{\link{pairwise.lsmc}} and its siblings for details. You may write your own \code{.lsmc} function for custom contrasts. The function \code{lsmobj} may be used to construct an object just like one returned by \code{lsmeans} from user-specified coefficients, covariance matrix, levels (or row labels), linear functions for each row, and degrees of freedom. After the object is constructed, it is \code{\link[lsmeans]{update}}ed with any additional arguments in \code{\dots}. } \value{ When \code{specs} is a \code{character} vector or one-sided formula, an object of class \code{\link[=lsmobj-class]{lsmobj}}. A number of methods are provided for further analysis, including \code{\link[lsmeans]{summary}}, \code{\link[lsmeans]{confint}}, \code{\link[lsmeans]{test}}, \code{\link[lsmeans]{contrast}}, \code{\link[lsmeans]{pairs}}, and \code{\link[lsmeans]{cld}}. When \code{specs} is a \code{list} or a \code{formula} having a left-hand side, the eturn value is an \code{lsm.list} object, which is simply a \code{list} of \code{lsmobj} objects. Methods for \code{lsm.list} objects are the same as those for \code{lsmobj}, but they apply to only one member of the list, determined by its \code{which} argument. \bold{Side effect:} When \code{object} is a model, a reference grid is constructed and it is saved as \code{.Last.ref.grid} in the user's enironment (unless this is disabled via \samp{lsm.option(save.ref.grid = FALSE)}). This makes it possible to check what reference grid was used, or to use it as the \code{object} in future \code{lsmeans} calls (and bypass reconstructing it). Similarly, \code{lstrends} also saves its reference grid (but for predicting difference quotients) as \code{.Last.ref.grid}. } \note{If the model formula contains variables that are not predictors (e.g., degree of a polynomial, knots for a spline, etc.), you must add a \code{params} argument to the call} \note{While using \code{specs} as a two-sided formula or a list is a convenient way to get a lot of results with minimal effort, it can also create confusion when additional arguments are provided, because not all arguments may be applied to all the results produced (see examples). Thus, the safer route is to do things incrementally.} \note{\code{lsmeans} and its relatives can produce fatal errors or incorrect results with models containing splines (e.g., \code{\link{ns}}) and other smoothers because the required information to reconstruct their basis is not always available. A model with \code{\link{poly}} involving two or more predictors will almost always produce misleading results without any warning; but \code{poly(..., raw = TRUE)} will work correctly.} \note{For a \code{ref.grid} or \code{lsmobj} object created in \pkg{lsmeans} version 2.10 or earlier, the information needed by the \code{weights} argument is not present; so a message is displayed and averaging is done using \code{fac.reduce}.} \references{ SAS Institute Inc. (2012) Online documentation; Shared concepts; LSMEANS statement, \url{http://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#statug_introcom_a0000003362.htm}, accessed August 15, 2012. } \author{ Russell V. Lenth } \seealso{ \code{\link{ref.grid}}, \code{\link{.Last.ref.grid}}, \code{\link{models}}, \code{\link{pairwise.lsmc}}, \code{\link[multcomp]{glht}}, \code{\link{lsm.options}} } \examples{ require(lsmeans) ### Covariance example (from Montgomery Design (8th ed.), p.656) # Uses supplied dataset 'fiber' fiber.lm <- lm(strength ~ diameter + machine, data = fiber) # adjusted means and comparisons, treating machine C as control ( fiber.lsm <- lsmeans (fiber.lm, "machine") ) contrast(fiber.lsm, "trt.vs.ctrlk") # Or get both at once using # lsmeans (fiber.lm, "machine", contr = "trt.vs.ctrlk") ### Factorial experiment warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks) ( warp.lsm <- lsmeans (warp.lm, ~ wool | tension, options = list(estName = "pred.breaks")) ) pairs(warp.lsm) # remembers 'by' structure contrast(warp.lsm, method = "poly", by = "wool") ### Unbalanced split-plot example ### #-- The imbalance is imposed deliberately to illustrate that #-- the variance estimates become biased require(nlme) Oats.lme <- lme(yield ~ factor(nitro) + Variety, random = ~1 | Block/Variety, subset = -c(1,2,3,5,8,13,21,34,55), data = Oats) (Oats.anal <- lsmeans(Oats.lme, list(poly ~ nitro, pairwise ~ Variety))) ### Issues with lists of specs test(Oats.anal) # Uses 1st element by default confint(Oats.anal, which = 4) # or confint(Oats.anal[[4]]) # Using 'pmmeans' wrapper ... pmmeans(warp.lm, ~ wool, options = list(infer = c(TRUE, TRUE), null = 22, side = ">")) ### Weights # See what's being averaged over in the above lsmeans(Oats.lme, ~ nitro, cov.reduce = FALSE, weights = "show.levels") # Give three times the weight to Marvellous lsmeans(Oats.lme, ~ nitro, cov.reduce = FALSE, weights = c(1,3,1)) # Overall mean lsmeans(Oats.lme, ~ 1, weights = "equal") lsmeans(Oats.lme, "1", weights = "cells") ### Model with a quadratic trend for 'nitro' # Also illustrates use of `params` argument to list non-predictors deg = 2 Oatsq.lm <- lm(yield ~ Block + poly(nitro, deg) + Variety, data = Oats) # Predictions at each unique 'nitro' value in the dataset lsmeans(Oatsq.lm, ~ nitro, cov.reduce = FALSE, params = "deg") ### Trends fiber.lm <- lm(strength ~ diameter*machine, data=fiber) # Obtain slopes for each machine ... ( fiber.lst <- lstrends(fiber.lm, "machine", var="diameter") ) # ... and pairwise comparisons thereof pairs(fiber.lst) # Suppose we want trends relative to sqrt(diameter)... lstrends(fiber.lm, ~ machine | diameter, var = "sqrt(diameter)", at = list(diameter = c(20,30))) # Given summary statistics for 4 cities computed elsewhere, # obtain multiple comparisons of their means using the # Satterthwaite method ybar <- c(47.6, 53.2, 88.9, 69.8) s <- c(12.1, 19.5, 22.8, 13.2) n <- c(44, 11, 37, 24) se2 = s^2 / n Satt.df <- function(x, dfargs) sum(x * dfargs$v)^2 / sum((x * dfargs$v)^2 / (dfargs$n - 1)) city.pmm <- pmmobj(bhat = ybar, V = diag(se2), levels = list(city = LETTERS[1:4]), linfct = diag(c(1,1,1,1)), df = Satt.df, dfargs = list(v = se2, n = n), estName = "mean") city.pmm contrast(city.pmm, "revpairwise") # See also many other examples in documentation for # 'contrast', 'cld', 'glht', 'lsmip', 'ref.grid', 'MOats', # 'nutrition', etc., and in the vignettes } \keyword{ models } \keyword{ regression } \keyword{ htest } lsmeans/man/feedlot.Rd0000644000175100001440000000402013013732335014412 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{feedlot} \alias{feedlot} \docType{data} \title{ Feedlot data } \description{ This is an unbalanced analysis-of-covariance example, where one covariate is affected by a factor. Feeder calves from various herds enter a feedlot, where they are fed one of three diets. The weight of the animal at entry is the covariate, and the weight at slaughter is the response. } \usage{data(feedlot)} \format{ A data frame with 67 observations on the following 4 variables. \describe{ \item{\code{herd}}{a factor with levels \code{9} \code{16} \code{3} \code{32} \code{24} \code{31} \code{19} \code{36} \code{34} \code{35} \code{33}, designating the herd that a feeder calf came from.} \item{\code{diet}}{a factor with levels \code{Low} \code{Medium} \code{High}: the energy level of the diet given the animal.} \item{\code{swt}}{a numeric vector: the weight of the animal at slaughter.} \item{\code{ewt}}{a numeric vector: the weight of the animal at entry to the feedlot.} } } \details{ The data arise from a Western Regional Research Project conducted at New Mexico State University. Calves born in 1975 in commercial herds entered a feedlot as yearlings. Both diets and herds are of interest as factors. The covariate, \code{ewt}, is thought to be dependent on \code{herd} due to different genetic backgrounds, breeding history, etc. The levels of \code{herd} ordered to similarity of genetic background. Note: There are some empty cells in the cross-classification of \code{herd} and \code{diet}. } \source{ Urquhart NS (1982) Adjustment in covariates when one factor affects the covariate. \emph{Biometrics} 38, 651-660. } \examples{ require(lsmeans) feedlot.lm <- lm(swt ~ ewt + herd*diet, data = feedlot) # Obtain LS~means with a separate reference value of ewt for each # herd. This reproduces the last part of Table 2 in the reference lsmeans(feedlot.lm, ~ diet | herd, cov.reduce = ewt ~ herd) } \keyword{datasets} lsmeans/man/update.Rd0000644000175100001440000002447613013732335014273 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{update} \alias{update} \alias{update.ref.grid} \alias{lsm.options} \alias{get.lsm.option} \alias{defaults.lsm} \alias{pmm.options} \alias{get.pmm.option} \title{ Set or retrieve options for objects and summaries in \pkg{lsmeans} } \description{ Objects of class \code{ref.grid} or \code{lsmobj} contain several settings in their \code{"misc"} slot that affect primarily the defaults used by \code{\link{summary}}. This \code{update} method allows them to be changed more safely than by modifying this slot directly. In addition, the user may set defaults for all objects using \samp{options(lsmeans = ...)}, or more conveniently using the \code{lsm.options} and \code{get.lsm.option} functions documented here (or its courtesy wrappers, \code{pmm.options} and \code{get.pmm.option} for those who dislike the \sQuote{least-squares means} terminology). } \usage{ \S3method{update}{ref.grid}(object, ..., silent = FALSE) lsm.options(...) get.lsm.option(x, default = defaults.lsm[[x]]) pmm.options(...) get.pmm.option(...) } \arguments{ \item{object}{An object of class \code{ref.grid} (or its extension, \code{lsmobj}) } \item{\dots}{ Arguments specifying elements' names and their new values. } \item{silent}{If \code{FALSE}, a message is displayed for any unmatched names.} \item{x}{Character string holding an option name for \code{lsm.options}.} \item{default}{Return value if \code{x} is not found.} } \details{ \subsection{Using \code{update}}{ In \code{update}, the names in \code{\dots} are partially matched against those that are valid, and if a match is found, it adds or replaces the current setting. The valid names are \describe{ \item{\code{tran}}{(\code{list} or \code{character}) specifies the transformation which, when inverted, determines the results displayed by \code{\link{summary}}, \code{\link{predict}}, or \code{\link{lsmip}} when \code{type="response"}. The value may be the name of a standard transformation from \code{\link{make.link}} or additional ones supported by name, such as \code{log2}; or, for a custom transformation, a \code{list} containing at least the functions \code{linkinv} (the inverse of the transformation) and \code{mu.eta} (the derivative thereof). The \code{\link{make.tran}} function returns such lists for a number of popular transformations. See the help page of \code{\link{make.tran}} for details as well as information on the additional named transformations that are supported. } \item{\code{tran.mult}}{Multiple for \code{tran}. For example, for the response transformation \samp{2*sqrt(y)} (or \samp{sqrt(y) + sqrt(y + 1)}, for that matter), we should have \code{tran = "sqrt"} and \code{tran.mult = 2}. If absent, a multiple of 1 is assumed.} \item{\code{estName}}{(\code{character}) is the column label used for displaying predictions or LS means.} \item{\code{inv.lbl}}{(\code{character)}) is the column label to use for predictions or LS means when \code{type="response"}.} \item{\code{by.vars}}{(\code{character)} vector or \code{NULL}) the variables used for grouping in the summary, and also for defining subfamilies in a call to \code{\link{contrast}}.} \item{\code{pri.vars}}{(\code{character} vector) are the names of the grid variables that are not in \code{by.vars}. Thus, the combinations of their levels are used as columns in each table produced by \code{\link{summary}}.} \item{\code{alpha}}{(numeric) is the default significance level for tests, in \code{\link{summary}} as well as \code{\link{cld}} and \code{\link{plot}} when \samp{intervals = TRUE}} \item{\code{adjust}}{(\code{character)}) is the default for the \code{adjust} argument in \code{\link{summary}}.} \item{\code{estType}}{(\code{character}) is the type of the estimate. It should match one of \samp{c("prediction","contrast","pairs")}. This is used along with \code{"adjust"} to determine appropriate adjustments to P values and confidence intervals.} \item{\code{famSize}}{(integer) is the \code{nmeans} parameter for \code{\link{ptukey}} when \code{adjust="tukey"}. } \item{\code{infer}}{(\code{logical} vector of length 2) is the default value of \code{infer} in \code{\link{summary}}.} \item{\code{level}}{(numeric) is the default confidence level, \code{level}, in \code{\link{summary}}} \item{\code{df}}{(numeric) overrides the default degrees of freedom with a specified single value.} \item{\code{null}}{(numeric) null hypothesis for \code{summary} or \code{test} (taken to be zero if missing).} \item{\code{side}}{(numeric or character) \code{side} specification for for \code{summary} or \code{test} (taken to be zero if missing).} \item{\code{delta}}{(numeric) \code{delta} specification for \code{summary} or \code{test} (taken to be zero if missing).} \item{\code{predict.type}}{(character) sets the default method of displaying predictions in \code{\link{summary}}, \code{\link{predict}}, and \code{\link{lsmip}}. Valid values are \code{"link"} (with synonyms \code{"lp"} and \code{"linear"}), or \code{"response"}.} \item{\code{avgd.over}}{(\code{character)} vector) are the names of the variables whose levels are averaged over in obtaining marginal averages of predictions, i.e., LS means. Changing this might produce a misleading printout, but setting it to \code{character(0)} will suppress the \dQuote{averaged over} message in the summary.} \item{\code{initMesg}}{(\code{character}) is a string that is added to the beginning of any annotations that appear below the \code{\link{summary}} display.} \item{\code{methDesc}}{(\code{character}) is a string that may be used for creating names for a list of \code{lsmobj} objects. } \item{(any slot name)}{If the name matches an element of \code{slotNames(object)}, that slot is replaced by the supplied value, if it is of the required class (otherwise an error occurs). Note that all the other possibilities above refer to elements of \code{misc}; hence, you probably don't want to replace \code{misc} itself. The user must be very careful in replacing slots because they are interrelated; for example, the \code{levels} and \code{grid} slots must involve the same variable names, and the lengths and dimensions of \code{grid}, \code{linfct}, \code{bhat}, and \code{V} must conform. } } % end \describe } % end subsection \subsection{Using \code{lsm.options}}{ In \code{lsm.options}, we may set or change the default values for the above attributes in the \code{lsmeans} option list(see \code{\link{options}}). Currently, the following elements of this list are used if specified: \describe{ \item{\code{ref.grid}}{A named \code{list} of defaults for objects created by \code{\link{ref.grid}}. This could affect other objects as well. For example, if \code{lsmeans} is called with a fitted model object, it calls \code{ref.grid} and this option will affect the resulting \code{lsmobj} object.} \item{\code{lsmeans}}{A named \code{list} of defaults for objects created by \code{\link{lsmeans}} (or \code{\link{lstrends}}).} \item{\code{contrast}}{A named \code{list} of defaults for objects created by \code{\link{contrast}} (or \code{\link{pairs}}).} \item{\code{summary}}{A named \code{list} of defaults used by the methods \code{\link{summary}}, \code{\link{predict}}, and \code{\link{lsmip}}. The only option that can affect the latter two is \code{"predict.method"}.} \item{\code{estble.tol}}{Tolerance for determining estimability in rank-deficient cases. If absent, the value in \code{defaults.lsm$estble.tol)} is used.} \item{(others)}{Other options may be accessed by support code for particular model classes (see \code{\link{models}}). For example, the \code{lmer.df}, \code{disable.pbkrtest}, and \code{pbkrtest.limit} options affect how degrees of freedom are computed for \code{lmerMod} objects (\pkg{lme4} package).} } % end \describe } % end subsection } % end details \value{ \code{update} returns a copy of \code{object} with its \code{"misc"} slot modified (and perhaps other slots). \code{lsm.options} returns the current options (same as the result of \samp{getOption("lsmeans")}) -- invisibly, unless called with no arguments. } \author{ Russell V. Lenth } \note{If a call to \code{\link{lsmeans}}, \code{\link{contrast}}, or \code{\link{ref.grid}} contains a non-\code{NULL} \code{options} list, those options are passed in a call to \code{update} on the constructed object before it is returned. This allows you, for example, to override the defaults used by \code{\link{summary}}. In addition, user defaults may be set using an \code{link{options}} setting for \code{"lsmeans"}. It should be a list with one or more named elements \code{lsmeans}, \code{contrast}, or \code{ref.grid}, used for setting the defaults for objects constructed by functions of these same names. Note that options can get \dQuote{inherited}. See the examples. Unlike the \code{update} method for model classes (\code{lm}, \code{glm}, etc.), this does not re-fit or re-estimate anything; but it does affect how \code{object} is treated by other methods for its class.} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{summary}}, \code{\link{make.tran}} } \examples{ # An altered log transformation warp.lm1 <- lm(log(breaks + 1) ~ wool*tension, data = warpbreaks) rg1 <- update(ref.grid(warp.lm1), tran = list(linkinv = function(eta) exp(eta) - 1, mu.eta = function(eta) exp(eta)), inv.lbl = "pred.breaks") summary(rg1, type = "response") \dontrun{ lsm.options(ref.grid = list(level = .90), contrast = list(infer = c(TRUE,FALSE)), estble.tol = 1e-6) # Sets default confidence level to .90 for objects created by ref.grid # AS WELL AS lsmeans called with a model object (since it creates a # reference grid). In addition, when we call 'contrast', 'pairs', etc., # confidence intervals rather than tests are displayed by default. } \dontrun{ lsm.options(disable.pbkrtest = TRUE) # This forces use of asymptotic methods for lmerMod objects. # Set to FALSE or NULL to re-enable using pbkrtest. } # See tolerance being used for determining estimability get.lsm.option("estble.tol") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ models } \keyword{ htest }% __ONLY ONE__ keyword per line lsmeans/man/extending.Rd0000644000175100001440000001655313013732335014773 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{recover.data} \alias{recover.data} \alias{recover.data.call} \alias{lsm.basis} %%\alias{.all.vars} % documented in the vignette %%\alias{nonest.basis} % moved to estimability package %%\alias{is.estble} % additional ref topic \alias{extending-lsmeans} \title{Support functions for creating a reference grid} \description{ This documents the methods used to create a \code{\link[=ref.grid-class]{ref.grid}} object from a fitted model. } \usage{ recover.data(object, ...) \S3method{recover.data}{call}(object, trms, na.action, data = NULL, params = NULL, ...) lsm.basis(object, trms, xlev, grid, ...) %%% %%%.all.vars(expr, retain = c("\\\\$", "\\\\[\\\\[", "\\\\]\\\\]"), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{ An object returned from a model-fitting function.} \item{trms}{The \code{\link{terms}} component of \code{object}} \item{xlev}{Named list of levels of factors in the model frame. This should \emph{not} include levels of factors created in the model itself, e.g., by including a \code{factor} call in the model formula.} \item{grid}{A \code{data.frame} containing predictor values at which predictions are needed.} \item{na.action}{Integer vector of indices of observations to ignore; or \code{NULL} if none} \item{data}{Data frame. Usually, this is \code{NULL}. However, if non-null, this is used in place of the reconstructed dataset. It must have all of the predictors used in the model, and any factor levels must match those used in fitting the model.} \item{params}{Character vector giving the names of any variables in the model formula that are \emph{not} predictors. An example would be a variable \code{knots} specifying the knots to use in a spline model. } % \item{expr}{A formula} % \item{retain}{Character vector of operators to retain (escaped as for \code{\link{gsub}})} \item{\dots}{Additional arguments passed to other methods.} } \details{ To create a reference grid, the \code{ref.grid} function needs to reconstruct the data used in fitting the model, and then obtain a matrix of linear functions of the regression coefficients for a given grid of predictor values. These tasks are performed by calls to \code{recover.data} and \code{lsm.basis} respectively. To extend \pkg{lsmeans}'s support to additional model types, one need only write S3 methods for these two functions. The existing methods serve as helpful guidance for writing new ones. Most of the work for \code{recover.data} can be done by its method for class \code{"call"}, providing the \code{terms} component and \code{na.action} data as additional arguments. Writing an \code{lsm.basis} method is more involved, but the existing methods (e.g., \code{lsmeans:::lsm.basis.lm}) can serve as models. See the ``Value'' section below for details on what it needs to return. Also, certain \code{recover.data} and \code{lsm.basis} methods are exported from \pkg{lsmeans}, so if your object is based on another model-fitting object, it may be that all that is needed is to call one of these exported methods and perhaps make modifications to the results. Contact the developer if you need others of these exported. If the model has a multivariate response, \code{bhat} needs to be \dQuote{flattened} into a single vector, and \code{X} and \code{V} must be constructed consistently. In models where a non-full-rank result is possible (often you can tell by seeing if there is a \code{singular.ok} argument in the model-fitting function), \code{summary} and \code{predict} check the estimability of each prediction, using the \code{\link[estimability]{nonest.basis}} function in the \pkg{estimability} package. The models already supported are detailed in \code{\link{models}}. Some packages may provide additional \pkg{lsmeans} support for its object classes. } \value{ \code{recover.data} should return a \code{data.frame} containing all the variables in the original data that appear as predictors in the model. Several attributes need to be included as well; see the code for \code{lsmeans:::recover.data.lm}. \code{lsm.basis} should return a \code{list} with the following elements: \item{X}{The matrix of linear fcns over \code{grid}, having the same number of rows as \code{grid} and the number of columns equal to the length of \code{bhat}.} \item{bhat}{The vector of regression coefficients for fixed effects. This should \emph{include} any \code{NA}s that result from rank deficiencies.} \item{nbasis}{A matrix whose columns form a basis for non-estimable functions of beta, or a 1x1 matrix of \code{NA} if there is no rank deficiency.} \item{V}{The estimated covariance matrix of \code{bhat}.} \item{dffun}{A function of \code{(k, dfargs)} that returns the degrees of freedom associated with \code{sum(k * bhat)}.} \item{dfargs}{A \code{list} containing additional arguments needed for \code{dffun}.} %%%\code{.all.vars} is an enhancement of \code{\link{all.vars}}, whereby the operators specified in \code{retain} are left intact. Thus, \code{All.vars(foo$y ~ bar[[2]])} returns \code{"foo$y", "bar[[2]]"}, whereas \code{all.vars} returns \code{"foo", "y", "bar"} } %\references{} \section{Optional hooks}{ Some models may need something other than standard linear estimates and standard errors. If so, custom functions may be pointed to via the items \code{misc$estHook}, \code{misc$vcovHook} and \code{misc$postGridHook}. If just the name of the hook function is provided as a character string, then it is retrieved using \code{\link{get}}. The \code{estHook} function should have arguments \samp{(object, do.se, tol, ...)} where \code{object} is the \code{ref.grid} or \code{lsmobj} object, \code{do.se} is a logical flag for whether to return the standard error, and \code{tol} is the tolerance for assessing estimability. It should return a matrix with 3 columns: the estimates, standard errors (\code{NA} when \code{do.se==FALSE}), and degrees of freedom (\code{NA} for asymptotic). The number of rows should be the same as \samp{object@linfct}. The \code{vcovHook} function should have arguments \samp{(object, tol, ...)} as described. It should return the covariance matrix for the estimates. Finally, \code{postGridHook}, if present, is called at the very end of \code{ref.grid}; it takes one argument, the constructed \code{object}, and should return a suitably modified\code{ref.grid} object. } \section{Additional functions}{ A few additional functions used in the \pkg{lsmeans} codebase are exported as they may be useful to package developers. See details near the end of the vignette \code{"extending"}. } \author{ Russell V. Lenth } %\note{} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{models}}, \code{\link{ref.grid}}, \code{\link{ref.grid-class}} } \examples{ \dontrun{ require(lsmeans) # Fit a 2-factor model with two empty cells warpsing.lm <- lm(breaks ~ wool*tension, data = warpbreaks, subset = -(16:40)) lsmeans:::recover.data.lm(warpsing.lm, data = NULL) grid = with(warpbreaks, expand.grid(wool = levels(wool), tension = levels(tension))) lsmeans:::lsm.basis.lm(warpsing.lm, delete.response(terms(warpsing.lm)), warpsing.lm$xlevels, grid) } % end dontrun } \keyword{ models } \keyword{ regression } lsmeans/man/ref.grid.class.Rd0000644000175100001440000001431213013732335015601 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{ref.grid-class} \Rdversion{1.1} \docType{class} \alias{ref.grid-class} \alias{lsmobj-class} \alias{lsmeans,ref.grid,character-method} \alias{show,ref.grid-method} \alias{show,lsmobj-method} \alias{summary,ref.grid-method} \title{Classes \code{"ref.grid"} and \code{"lsmobj"}} \description{ A reference grid encapsulates everything needed to compute least-squares means, independently of the underlying model object. The \code{"lsmobj"} class is a minor extension of \code{"ref.grid"} where the linear predictors for the reference grid are transformed in some linear way such as marginal averages or contrasts. } \section{Objects from the Classes}{ Objects of class \code{"ref.grid"} are most commonly created by calling the \code{\link{ref.grid}} function. Objects of class \code{"lsmobj"} are created by calling \code{\link{lsmeans}} or a related function such as \code{\link{contrast}}.} \section{Slots}{ \describe{ \item{\code{model.info}:}{Object of class \code{"list"} containing the elements \code{call} (the call that produced the model), \code{terms} (its \code{terms} object), and \code{xlev} (factor-level information)} \item{\code{roles}:}{Object of class \code{"list"} containing at least the elements \code{predictors}, \code{responses}, and \code{multresp}. These are character vectors of names of these variables.} \item{\code{grid}:}{Object of class \code{"data.frame"} containing the combinations of the variables that define the reference grid. In addition, there is an auxiliary column named \code{".wgt."} holding the observed frequencies or weights for each factor combination (excluding covariates). If the model has one or more \code{\link{offset}()} calls, there is an another auxiliary column named \code{".offset."}. Auxiliary columns are not considered part of the reference grid. (However, any variables included in \code{offset} calls \emph{are} in the reference grid.)} \item{\code{levels}:}{Object of class \code{"list"} with each entry containing the distinct levels of variables in the reference grid. Note that \code{grid} is obtained by applying the function \code{\link{expand.grid}} to this list} \item{\code{matlevs}:}{Object of class \code{"list"} Like \code{levels} but has the levels of any matrices in the original dataset. Matrix columns must always be reduced to a single value for purposes of the reference grid } \item{\code{linfct}:}{Object of class \code{"matrix"} giving the linear functions of the regression coefficients for predicting each element of the reference grid. The rows of this matrix go in one-to-one correspondence with the rows of \code{grid}, and the columns with elements of \code{bhat}} \item{\code{bhat}:}{Object of class \code{"numeric"} with the regression coefficients. If there is a multivariate response, this must be flattened to a single vector, and \code{linfct} and \code{V} redefined appropriately. Important: \code{bhat} must \emph{include} any \code{NA} values produced by collinearity in the predictors. These are taken care of later in the estimability check.} \item{\code{nbasis}:}{Object of class \code{"matrix"} with the basis for the non-estimable functions of the regression coefficients. Every LS mean will correspond to a linear combination of rows of \code{linfct}, and that result must be orthogonal to all the columns of \code{nbasis} in order to be estimable. This will be \code{NULL} if everything is estimable} \item{\code{V}:}{Object of class \code{"matrix"}, the symmetric variance-covariance matrix of \code{bhat} } \item{\code{dffun, dfargs:}}{Objects of class \code{"function"} and \code{"list"} respectively. \code{dffun(k,dfargs)} should return the degrees of freedom for the linear function \code{sum(k*bhat)}, or \code{NA} if unavailable} \item{\code{misc}:}{A \code{list} containing additional information used by methods. These include at least the following: \code{estName} (the label for the estimates of linear functions), and the default values of \code{infer}, \code{level}, and \code{adjust} to be used in the \code{\link{summary}} method. Elements in this slot may be modified if desired using the \code{\link{update}} method.} \item{\code{post.beta}:}{A \code{matrix} containing a sample from the posterior distribution of the regression coefficients; or a 1 x 1 matrix of \code{NA} if this is not available. When it is non-trivial, the \code{as.mcmc} method returns \code{post.beta} times \code{t(linfct)}, which is a sample from the posterior distribution of the LS means.} }} % end of describe and slots section \section{Extends}{ \code{Class "lsmobj"} extends \code{Class "ref.grid"}, directly. There is hardly a difference between these classes except for how the slots \code{linfct} and \code{grid} are obtained, and their \code{show} methods.} \section{Methods}{ All methods for these objects are S3 methods except for \code{show}. \describe{ \item{\code{show}:}{Prints the results of \code{str} for \code{ref.grid} objects, and \code{summary} for \code{lsmobj} objects.} \item{\code{str}:}{Displays a brief listing of the variables and levels defining the grid. } \item{\code{summary}:}{Displays a summary of estimates, standard errors, degrees of freedom, and optionally, tests and/or confidence intervals. } \item{\code{lsmeans}:}{Computes least-squares means and creates an \code{"lsmobj"} object.} \item{\code{confint}:}{Confidence intervals for lsmeans.} \item{\code{test}:}{Hypothesis tests. } \item{\code{cld}:}{Compact-letter display for tests of pairwise comparisons} \item{\code{contrast}:}{Contrasts among lsmeans. } \item{\code{pairs}:}{A special case of \code{contrasts} for pairwise comparisons. } \item{\code{update}:}{Change defaults used primarily by \code{summary}, such as transformation, p-value adjustment, and confidence level.} } % end of \describe } % end of Methods section %\references{} \author{ Russell V. Lenth } %\note{} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{ref.grid}}, \code{\link{lsmeans}} } \examples{ showClass("ref.grid") showClass("lsmobj") } \keyword{classes} lsmeans/man/make.tran.Rd0000644000175100001440000001341113013732335014654 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{make.tran} \alias{make.tran} \title{ Response transformations } \description{ Create the needed information to perform transformations of the response variable, including inverting the transformation and estimating variances of back-transformed predictions via the delta method. \code{make.tran} is similar to \code{\link{make.link}}, but it covers additional transformations. The result can be used as an environment in which the model is fitted, or as the \code{tran} argument in \code{\link{update.ref.grid}} (when the given transformation was already applied in an existing model). } \usage{ make.tran(type = c("genlog", "power", "boxcox", "sympower", "asin.sqrt"), param = 1) # See Details for additional auto-detected transformations } %- maybe also 'usage' for other objects documented here. \arguments{ \item{type}{ The name of the transformation. See Details. } \item{param}{ Numeric parameter for the transformation. Optionally, it may be a vector of two numeric values; the second element specifies an alternative base or origin for certain transformations. See Details. } } \details{ The functions \code{\link{lsmeans}}, \code{\link{ref.grid}}, and related ones automatically detect response transformations that are recognized by examining the model formula. These are \code{log}, \code{log2}, \code{log10}, \code{sqrt}, \code{logit}, \code{probit}, \code{cauchit}, \code{cloglog}; as well as (for a response variable \code{y}) \code{asin(sqrt(y))}, \code{asinh(sqrt(y))}, and \code{sqrt(y) + sqrt(y+1)}. In addition, any constant multiple of these (e.g., \code{2*sqrt(y)}) is auto-detected and appropriately scaled (see also the \code{tran.mult} argument in \code{\link{update.ref.grid}}). A few additional character strings may be supplied as the \code{tran} argument in \code{\link{update.ref.grid}}: \code{"identity"}, \code{"1/mu^2"}, \code{"inverse"}, \code{"reciprocal"}, \code{"asin.sqrt"}, and \code{"asinh.sqrt"}. More general transformations may be provided as a list of functions and supplied as the \code{tran} argument as documented in \code{\link{update.ref.grid}}. The \code{make.tran} function returns a suitable list of functions for several popular transformations. Besides being usable with \code{update}, the user may use this list as an enclosing environment in fitting the model itself, in which case the transformation is auto-detected when the special name \code{linkfun} (the transformation itself) is used as the response transformation in the call. See the examples below. Most of the transformations available in "make.tran" require a parameter, specified in \code{param}; we use \eqn{p} to denote this parameter, and \eqn{y} to denote the response variable, in subsequent expressions. The \code{type} argument specifies the following transformations: \describe{ \item{\code{"genlog"}}{Generalized logarithmic transformation: \eqn{log(y + p)}, where \eqn{y > -p}} \item{\code{"power"}}{Power transformation: \eqn{y^p}, where \eqn{y > 0}. When \eqn{p = 0}, \code{"log"} is used instead} \item{\code{"boxcox"}}{The Box-Cox transformation (unscaled by the geometric mean): \eqn{(y^p - 1) / p}, where \eqn{y > 0}. When \eqn{p = 0}, \eqn{log(y)} is used.} \item{\code{"sympower"}}{A symmetrized power transformation on the whole real line: \eqn{abs(y)^p * sign(y)}. There are no restrictions on \eqn{y}, but we require \eqn{p > 0} in order for the transformation to be monotone and continuous.} \item{\code{"asin.sqrt"}}{Arcsin-square-root transformation: \eqn{sin^(-1)(y/p)^{1/2)}. Typically, the parameter \eqn{p} is equal to 1 for a fraction, or 100 for a percentage.} } The user may include a second element in \code{param} to specify an alternative origin (other than zero) for the \code{"power"}, \code{"boxcox"}, or \code{"sympower"} transformations. For example, \samp{type = "power", param = c(1.5, 4)} specifies the transformation \eqn{(y - 4)^1.5}. In the \code{"genpower"} transformation, a second \code{param} element may be used to specify a base other than the default natural logarithm. For example, \samp{type = "genlog", param = c(.5, 10)} specifies the \eqn{log10(y + .5)} transformation. For purposes of back-transformation, the \samp{sqrt(y) + sqrt(y+1)} transformation is treated exactly the same way as \samp{2*sqrt(y)}, because both are regarded as estimates of \eqn{2\sqrt\mu}. }} % end of \details \value{ A \code{list} having at least the same elements as that returned by \code{\link{make.link}}. The \code{linkfun} component is the transformation itself. } %%%\references{} \author{ Russell V. Lenth } \note{ We modify certain \code{\link{make.link}} results in transformations where there is a restriction on valid prediction values, so that reasonable inverse predictions are obtained for no matter what. For example, if a \code{sqrt} transformation was used but a predicted value is negative, the inverse transformation is zero rather than the square of the prediction. A side effect of this is that it is possible for one or both confidence limits, or even a standard error, to be zero. } \seealso{ \code{\link{make.link}}, \code{\link[lsmeans]{update}} } \examples{ require("lsmeans") # Fit a model using an oddball transformation: bctran <- make.tran("boxcox", 0.368) warp.bc <- with(bctran, lm(linkfun(breaks) ~ wool * tension, data = warpbreaks)) # Obtain back-transformed LS means: lsmeans(warp.bc, ~ tension | wool, type = "response") \dontrun{ # An existing model 'mod' was fitted with a log(y + 1) transformation... mod.rg <- update(ref.grid(mod), tran = make.tran("genlog", 1)) lsmeans(mod.rg, "treatment") } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ models } lsmeans/man/lsmeans-package.Rd0000644000175100001440000001526613013732335016041 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{lsmeans-package} \alias{lsmeans-package} \docType{package} \encoding{utf-8} \title{ Least-squares means } \description{ This package provides methods for obtaining so-called least-squares means for factor combinations in a variety of fitted linear models. It can also compute contrasts or linear combinations of these least-squares means, (several standard contrast families are provided), and in addition can estimate and contrast slopes of trend lines. Popular adjustments for multiple-comparisons are provided, as well as graphical ways of displaying the results. } \section{Overview}{ \describe{ \item{Concept}{ Least-squares means (see Searle \emph{et al.} 1980, who prefer the term \dQuote{predicted marginal means} (PMM)) are popular for summarizing linear models that include factors. For balanced experimental designs, they are just the marginal means. For unbalanced data, they in essence estimate what you \emph{would} have observed that the data arisen from a balanced experiment. } \item{Reference grids}{ The implementation in \pkg{lsmeans} relies on our own concept of a \emph{reference grid}, which is an array of factor and predictor levels. Predictions are made on this grid, and least-squares means are defined as averages of these predictions over zero or more dimensions of the grid. The function \code{\link{ref.grid}} explicitly creates a reference grid (\code{ref.grid} object) that can subsequently be used to obtain least-squares means. The \code{\link{update}} method is used to change its properties. Our reference-grid framework expands slightly upon Searle \emph{et al.}'s definitions of PMMs, in that it is possible to include multiple levels of covariates in the grid. } \item{Models supported}{ Many linear models are supported by the package, including \code{lm}, \code{glm}, \code{aovList}, and \code{mlm} in the \pkg{stats} package, as well as fitted-model objects from several contributed packages including \pkg{nlme}, \pkg{lme4}, \pkg{survival}, and \pkg{geepack}. The help page for \code{\link{models}} provides more details, including, in some cases, additional \code{ref.grid} arguments that might affect the subsequent analysis. Also, some models require other packages be installed in order to obtain all the available features. } \item{Least-squares means}{ The \code{\link{lsmeans}} function computes least-squares means given a \code{ref.grid} object or a fitted model, and a specification indicating what factors to include. The \code{\link{lstrends}} function creates the same sort of results for estimating and comparing slopes of fitted lines. Both return an \code{lsmobj} object very much like a reference grid, but with possibly fewer factors involved. } \item{Summaries and analysis}{ The \code{\link{summary}} method may be used to display a \code{ref.grid} or an \code{lsmobj}. Special-purpose summaries are available via \code{\link{confint}} and \code{\link{test}}, the latter of which can also do a joint test of several estimates. The user may specify by variables, multiplicity-adjustment methods, confidence levels, etc., and if a transformation or link function is involved, may reverse-transform the results to the response scale. } \item{Contrasts and comparisons}{ The \code{\link{contrast}} method is used to obtain contrasts among the estimates; several standard contrast families are available such as deviations from the mean, polynomial contrasts, and comparisons with one or more controls. Another \code{lsmobj} object is returned, which can be summarized or further analyzed. For convenience, a \code{pairs} method is provided for the case of pairwise comparisons. Related to this is the \code{\link{cld}} method, which provides a compact letter display for grouping pairs of means that are not significantly different. \code{cld} requires the \pkg{multcompView} package. } \item{Graphs}{The \code{\link[=plot.lsmobj]{plot}} method will display side-by-side confidence intervals for the estimates, and/or \sQuote{comparison arrows} whereby the significance of pairwise differences can be judged by how much they overlap. The \code{\link{lsmip}} function displays estimates like an interaction plot, multi-paneled if there are by variables. These graphics capabilities require the \pkg{lattice} package be installed. } \item{\pkg{multcomp} interface}{ The \code{\link{as.glht}} function and \code{\link{glht}} method for \code{lsmobj}s provide an interface to the \code{\link[multcomp]{glht}} function in the \pkg{multcomp} package, thus providing for more exacting simultaneous estimation or testing. The package also provides an \code{\link{lsm}} method that works as an alternative to \code{\link[multcomp]{mcp}} in a call to \code{glht}. } }}% overview \section{Additional information}{ Examples and discussion are available via \code{vignette("using-lsmeans", package="lsmeans")}. Some features of the \pkg{lsmeans} require (or are enhanced by) additional packages that are loaded when needed. Since they are not \dQuote{required} packages, they are not automatically installed with \pkg{lsmeans}. We highly recommend that users also install the following packages: \pkg{multcomp} (if \code{\link{cld}}, \code{\link{glht}}, or \code{\link{as.glht}} are to be used), \pkg{multcompView} (for \code{\link{cld}}), \pkg{lattice} (for \code{\link{plot}} and \code{\link{lsmip}}), and \pkg{lmerTest} or \pkg{pbkrtest} (for models fitted by the \pkg{lme4} package). Starting with \pkg{lsmeans} version 2, a new object framework based on \emph{reference grids} is used that increases flexibility and provides for extending its capabilities to additional model objects. Use \code{vignette("lsmeans-changes")} for information on the user impact of these changes. It is possible to write your own interfaces for models not yet supported by \pkg{lsmeans}. See the help page \code{\link{extending-lsmeans}} and \code{vignette("extending")} for details on how to do this. } % add'l information \details{ \tabular{ll}{ Package: \tab lsmeans\cr Type: \tab Package\cr License: \tab GPL-2\cr Other information: \tab See DESCRIPTION\cr } } \references{ Russell V. Lenth (2016) Least-Squares Means: The R Package lsmeans. \emph{Journal of Statistical Software}, 69(1), 1-33. doi:10.18637/jss.v069.i01 Searle S.R. Speed F.M. Milliken G.A. (1980) Population marginal means in the linear model: An alternative to least squares means. \emph{The American Statistician} \bold{34}(4), 216-221. } \author{ Russell V. Lenth (author), Maxime Hervé (contributor) Maintainer: Russ Lenth } \keyword{ package } \keyword{ models } \keyword{ regression } \keyword{ htest } lsmeans/man/fiber.Rd0000644000175100001440000000241713013732335014067 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{fiber} \alias{fiber} \docType{data} \title{ Fiber data } \description{ Fiber data from Montgomery Design (8th ed.), p.656 (Table 15.10). Useful as a simple analysis-of-covariance example. } \usage{fiber} \format{ A data frame with 15 observations on the following 3 variables. \describe{ \item{\code{machine}}{a factor with levels \code{A} \code{B} \code{C}. The primary factor of interest.} \item{\code{strength}}{a numeric vector. The response variable.} \item{\code{diameter}}{a numeric vector. A covariate.} } } \details{ The goal of the experiment is to compare the mean breaking strength of fibers produced by the three machines. When testing this, the technician also measured the diameter of each fiber, and this measurement may be used as a concomitant variable to improve precision of the estimates. } \source{ Montgomery, D. C. (2013) \emph{Design and Analysis of Experiments} (8th ed.). John Wiley and Sons, ISBN 978-1-118-14692-7. } \examples{ require(lsmeans) fiber.lm <- lm(strength ~ diameter + machine, data=fiber) ref.grid(fiber.lm) # Covariate-adjusted means and comparisons lsmeans(fiber.lm, pairwise ~ machine) } \keyword{datasets} lsmeans/man/glht.Rd0000644000175100001440000000624613013732335013742 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{glht} \alias{glht} \alias{lsm} \alias{glht.ref.grid} \alias{as.glht} \alias{as.glht.ref.grid} \alias{as.glht.lsm.list} \alias{coef.glht.list} \alias{confint.glht.list} \alias{plot.glht.list} \alias{summary.glht.list} \alias{vcov.glht.list} \alias{pmm} %- Also NEED an '\alias' for EACH other topic documented here. \title{ \pkg{lsmeans} support for \code{glht} } \description{ These functions and methods provide an interface between \pkg{lsmeans} and the \code{\link[multcomp]{glht}} function for simultaneous inference in the \pkg{multcomp} package. } \usage{ \method{as.glht}{ref.grid}(object, ...) \method{as.glht}{lsm.list}(object, ..., which = 1) \method{coef}{glht.list}(object, ...) \method{confint}{glht.list}(object, ...) \method{plot}{glht.list}(x, ...) \method{summary}{glht.list}(object, ...) \method{vcov}{glht.list}(object, ...) lsm(...) pmm(...) } \arguments{ \item{object, x}{ An object of the required class. } \item{which}{Numeric index of which element of the \code{lsm.list} to use.} \item{\dots}{ Additional arguuments to other methods. } } \details{ \code{lsm} (and \code{pmm}, which is identical) are meant to be called only \emph{from} \code{"glht"} as its second (\code{linfct}) argument. It works similarly to \code{\link[multcomp]{mcp}} except with \code{specs} (and optionally \code{by} and \code{contr} arguments) provided as in a call to \code{\link{lsmeans}} or \code{pmmeans}. When there is a non-\code{NULL} \code{by} variable (either explicitly or implicitly), each ``by'' group is passed separately to \code{glht} and returned as a \code{list} of \code{"glht"} objects. For convenience, this is classed as \code{"glht.list"}, and appropriate methods \code{coef}, \code{confint}, \code{plot}, \code{summary}, and \code{vcov} are provided. } \value{ \code{as.glht} returns an object of class \code{\link[multcomp]{glht}}, or of class \code{glht.list} if \code{by} is non-\code{NULL}. The latter is simply a list of \code{glht} objects, and the provided methods \code{coef}, \code{confint}, \code{plot}, \code{summary}, and \code{vcov} simply \code{\link{lapply}} the corresponding methods for class \code{"glht"}. } %\references{} \author{ Russell V. Lenth } \note{ There is also a \code{glht} method for class \code{ref.grid}, but it is far preferable to use \code{as.glht} instead, as its \code{model} argument is redundant. } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{lsmeans}}, \code{\link[multcomp]{glht}} } \examples{ require(lsmeans) require(multcomp) warp.lm <- lm(breaks ~ wool*tension, data = warpbreaks) # Using 'lsm' summary(glht(warp.lm, lsm(pairwise ~ tension | wool))) # Same, but using an existing 'lsmeans' result warp.lsmobj <- lsmeans(warp.lm, ~ tension | wool) summary(as.glht(pairs(warp.lsmobj))) # Same contrasts, but treat as one family summary(as.glht(pairs(warp.lsmobj), by = NULL)) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ regression } \keyword{ models } \keyword{ htest } lsmeans/man/cld.Rd0000644000175100001440000000750013013732335013540 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{cld} \alias{cld} \alias{cld.ref.grid} \alias{cld.lsm.list} \title{Compact letter display of pairwise comparisons} \description{ Extract and display information on all pairwise comparisons of least-squares means.} \usage{ \method{cld}{ref.grid}(object, details = FALSE, sort = TRUE, by, alpha = 0.05, Letters = c("1234567890", LETTERS, letters), reversed = FALSE, ...) \method{cld}{lsm.list}(object, ..., which = 1) } \arguments{ \item{object}{An object of class \code{ref.grid}} \item{details}{Logical value determining whether detailed information on tests of pairwise comparisons is displayed} \item{sort}{Logical value determining whether the LS means are sorted before the comparisons are produced. When \code{sort} is \code{TRUE}, the results are displayed in increasing order if \code{reversed} is \code{FALSE} (the default), or in decreasing order if \code{reversed} is \code{TRUE}.} \item{by}{Character value giving the name or names of variables by which separate families of comparisons are tested. If \code{NULL}, all means are compared. If missing, and a \code{by} variable was used in creating \code{object}, it is used as the \code{by} variable in \code{cld}.} \item{alpha}{Numeric value giving the significance level for the comparisons} \item{Letters}{Character vector of letters to use in the display. Any strings of length greater than 1 are expanded into individual characters} \item{reversed}{Logical value (passed to \code{\link[multcompView]{multcompLetters}} in the \pkg{multcompView} package.) If \code{TRUE}, the order of use of the letters is reversed. In addition, if both \code{sort} and \code{reversed} are \code{TRUE}, the sort order of results is reversed.} \item{\dots}{Arguments passed to \code{\link{contrast}} (for example, an \code{adjust} method)} \item{which}{When \code{object} is a list, this determines which element is analyzed.} } \details{ This function uses the Piepho (2004) algorithm (as implemented in the \pkg{multcompView} package) to generate a compact letter display of all pairwise comparisons of least-squares means. The function obtains (possibly adjusted) \emph{P} values for all pairwise comparisons of means, using the \code{\link{contrast}} function with \code{method = "pairwise"}. When a \code{P} value exceeds \code{alpha}, then the two means have at least one letter in common. } \value{ When \code{details == FALSE}, an object of class \code{summary.ref.grid} (which inherits from \code{data.frame}) showing the summary of LS means with an added column named \code{.groups} with the cld information. When \code{details == TRUE}, a list the object just described, as well as the summary of the \code{contrast} results showing each comparison, its estimate, standard error, \eqn{t} ratio, and adjusted \eqn{P} value. } \references{ Hans-Peter Piepho (2004) An algorithm for a letter-based representation of all pairwise comparisons, \emph{Journal of Computational and Graphical Statistics}, 13(2), 456-466. } \author{Russell V. Lenth} \note{This function requires the \pkg{multcompView} package to be installed. Otherwise an error message is produced.} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link[multcomp]{cld}} in the \pkg{multcomp} package } \examples{ warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks) warp.lsm <- lsmeans(warp.lm, ~ tension | wool) cld(warp.lsm) # implicitly uses by = "wool" cld(warp.lsm, by = "tension") # overrides implicit 'by' # Mimic grouping bars and compare all 6 means cld(warp.lsm, by = NULL, Letters = "||||||||", alpha = .01) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ htest } lsmeans/man/contrast.Rd0000644000175100001440000002071113013732335014632 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{contrast} \alias{contrast} \alias{contrast.ref.grid} \alias{contrast.lsm.list} \alias{test} \alias{test.ref.grid} \alias{confint} \alias{confint.ref.grid} \alias{pairs} \alias{pairs.ref.grid} \title{ Methods for obtaining analyses \code{ref.grid} and \code{lsmobj} objects } \description{ These methods provide for analyses of \code{ref.grid} objects, or follow-up analyses of \code{lsmobj} objects: Contrasts, pairwise comparisons, tests, and confidence intervals. } \usage{ \method{contrast}{ref.grid}(object, method = "eff", interaction = FALSE, by, offset = NULL, name = "contrast", options = getOption("lsmeans")$contrast, adjust, ...) \method{contrast}{lsm.list}(object, ..., which = 1) \method{test}{ref.grid}(object, null = 0, joint = FALSE, verbose = FALSE, rows, by, ...) \method{confint}{ref.grid}(object, parm, level = 0.95, ...) \method{pairs}{ref.grid}(x, reverse = FALSE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object, x}{ An object of class \code{"ref.grid"} or its extension, \code{"lsmobj"}. } \item{method}{ Character value giving the root name of a contast method (e.g. \code{"pairwise"}). Alternatively, a named list of contrast coefficients that must each conform to the number of least-squares means in each \code{by} group. This is just like the \code{contr} argument in \code{\link{lsmeans}}. To identify the available methods, see \preformatted{ls("package:lsmeans", pat=".lsmc")} You may define your own \code{.lsmc} function and use its root name as \code{method}. If \code{interaction} is of character type, this argument is ignored. } \item{interaction}{ Character vector or logical value. In multi-factor situations with \code{interaction = FALSE}, the factor combinations are treated as levels of a single \dQuote{uber-factor}, and the contrast specified in \code{method} is applied to it. Otherwise, interaction contrasts are computed: Contrasts are generated for each factor separately, one at a time; and these contrasts are applied to the object (the first time around) or to the previous result (subsequently). (Any factors specified in \code{by} are skipped.) The final result comprises contrasts of contrasts, or, equivalently, products of contrasts for the factors involved. Processing is done in the order of appearance in \code{object@levels}. With \code{interaction = TRUE}, \code{method} (if specified as character) is used for each contrast. If \code{interaction} is a character vector, the elements specify the respective contrast method(s); they are recycled as needed. } \item{by}{ Character names of variable(s) to be used for ``by'' groups. The contrasts or joint tests will be evaluated separately for each combination of these variables. If \code{object} was created with by groups, those are used unless overridden. Use \code{by = NULL} to use no by groups at all. } \item{offset}{Numeric vector of the same length as each \code{by} group. These values are added to their respective linear estimates. This argument is ignored when\code{interaction} is not \code{FALSE}. } \item{name}{Name to use to label the contrasts in table headings or subsequent contrasts of the returned object. This argument is ignored when\code{interaction} is not \code{FALSE}. } \item{options}{If non-\code{NULL}, a named \code{list} of arguments to pass to \code{\link{update}}, just after the object is constructed.} \item{adjust}{ Method to use for adjusting \emph{P} values. This is passed to \code{\link[lsmeans]{summary}}. This argument is available in \code{contrast} for historical reasons; but it is better style to specify the adjustment method, along with other testing options such as \code{side}, as part of \code{options}. } \item{joint}{Logical value. If \code{FALSE}, the arguments are passed to \code{\link{summary}} with \code{infer=c(FALSE,TRUE)}. If \code{TRUE}, a joint test of the hypothesis L beta = null is performed, where L is \code{object@linfct} and beta is the vector of fixed effects estimated by \code{object@betahat}. This will be either an \emph{F} test or a chi-square (Wald) test depending on whether degrees of freedom are available.} \item{rows}{Integer values. The rows of L to be tested in the joint test. If missing, all rows of L are used. If not missing, \code{by} variables are ignored.} \item{null}{Numeric value specifying the null value(s) being tested against. It may be either a single value, in which case it is used as the null value for all linear functions under test; or a numeric vector of length equal to the number of linear functions.} \item{parm}{This is ignored, but it is a required argument of the generic \code{confint} method.)} \item{verbose}{Logical value. If \code{TRUE} and \code{joint==TRUE}, a table of the effects being tested is printed.} \item{level}{ Numeric value of the desired confidence level.} \item{which}{When \code{object} is a list of \code{lsmobj} objects, this specifies which member of the list is analyzed.} \item{reverse}{Logical value determining whether \code{"pairwise"} or \code{"revpairwise"} pairwise comparisons are generated.} \item{\dots}{ Additional arguments passed to \code{\link[lsmeans]{summary}} or to a contrast function.} } \details{ Though \code{contrast} is ordinarily used to create true contrasts (whose coefficients sum to zero), it may be used to estimate any linear function of the LS means; and \code{offset} expands this capability further by allowing additive constants. \code{pairs} is equivalent to \code{contrast} with \code{method = "pairwise"}. \code{confint} and \code{test} (when \code{JOINT==FALSE}) are equivalent to calling \code{\link[lsmeans]{summary}} with \code{infer=c(TRUE,FALSE)} and \code{infer=c(FALSE,TRUE)}, respectively. When using \code{test} to do a joint test of L beta = null, an error is thrown if any row of L is non-estimable. It is permissible for the rows of L to be linearly dependent as long as \code{null == 0}; a reduced set of contrasts is tested. Linear dependence and nonzero \code{null} cause an error. } \value{ \code{contrast} and \code{pairs} return an object of class \code{"lsmobj"}, which is an extension of \code{"ref.grid"}. Consequently, they may be used as arguments to other \code{"lsmobj"} or \code{"ref.grid"} methods. The user may, for example, compute contrasts of contrasts, or re-summarize a set of confidence intervals with a different \code{by} grouping or confidence level. The ``grid'' for the returned value is simply the set of variables that identify the results. For example, \code{contrast}'s return value is a reference grid for one factor named \code{contrast}. \code{confint} and \code{test} (when \code{Joint==FALSE}) return an object of class \code{summary.ref.grid}. When \code{JOINT==TRUE}, \code{test} returns a \code{"summary.ref.grid"} object (extends \code{"data.frame"}) with the test statistic, degrees of freedom, and \emph{P} value for each \code{by} group. } %\references{} \author{ Russell V. Lenth } %\note{} %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ Additional \code{"lsmobj"} methods having their own help pages are \code{\link[lsmeans]{cld}} and \code{\link[lsmeans]{glht}}. Also, the \code{\link[lsmeans]{summary}} and other methods for \code{"ref.grid"} objects also work for \code{"lsmobj"} objects. } \examples{ require(lsmeans) warp.lm <- lm(breaks ~ wool*tension, data = warpbreaks) warp.lsm <- lsmeans(warp.lm, ~ tension | wool) # Polynomial contrasts of tension, by wool (warp.pl <- contrast(warp.lsm, "poly", name = "order")) # Same results with a different adjustment summary(warp.pl, adjust = "fdr") # Jointly test the tension effects for each wool test(warp.pl, joint = TRUE, by = "wool") # Compare the two contrasts for each order contrast(warp.pl, "revpairwise", by = "order") # User-provided contrasts, ignoring the previous by grouping contrast(warp.lsm, list(c1=c(1,0,0,-1,0,0), c2=c(1,1,1,-1,-1,-1)/3), by = NULL) # Compare consecutive tension*wool comb's as treatment with 6 levels contrast(warp.lsm, "consec", by = NULL) # Interaction contrasts (comparisons of linear and quadratic contrasts) contrast(warp.lsm, interaction = c("poly", "consec"), by = NULL) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ models } \keyword{ regression } \keyword{ htest } lsmeans/man/MOats.Rd0000644000175100001440000000321313013732335014016 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{MOats} \alias{MOats} \docType{data} \title{ Oats data in multivariate form } \description{ This is the \code{Oats} dataset provided in the \pkg{nlme} package, but it is rearranged as one multivariate observation per plot. } \usage{data(MOats)} \format{ A data frame with 18 observations on the following 3 variables. \describe{ \item{\code{Variety}}{a factor with levels \code{Golden Rain}, \code{Marvellous}, \code{Victory}} \item{\code{Block}}{an ordered factor with levels \code{VI} < \code{V} < \code{III} < \code{IV} < \code{II} < \code{I}} \item{\code{yield}}{a matrix with 4 columns, giving the yields with nitrogen concentrations of 0, .2, .4, and .6.} } } \details{ These data arise from a split-plot experiment reported by Yates (1935) and used as an example in Pinheiro and Bates (2000) and other texts. Six blocks were divided into three whole plots, randomly assigned to the three varieties of oats. The whole plots were each divided into 4 split plots and randomized to the four concentrations of nitrogen. } \source{ The dataset \code{\link[nlme]{Oats}} in the \pkg{nlme} package. } \references{ Pinheiro, J. C. and Bates D. M. (2000) \emph{Mixed-Effects Models in S and S-PLUS}, Springer, New York. (Appendix A.15) Yates, F. (1935) Complex experiments, \emph{Journal of the Royal Statistical Society} Suppl. 2, 181-247 } \examples{ require(lsmeans) MOats.lm <- lm (yield ~ Block + Variety, data = MOats) MOats.rg <- ref.grid (MOats.lm, mult.name = "nitro") lsmeans(MOats.rg, ~ nitro | Variety) } \keyword{datasets} lsmeans/man/summary.Rd0000644000175100001440000004712213013732335014477 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{summary} \alias{summary} \alias{summary.ref.grid} \alias{str.ref.grid} \alias{predict.ref.grid} \alias{print.ref.grid} \alias{print.summary.ref.grid} \alias{plot.lsmobj} \alias{plot.summary.ref.grid} \alias{vcov.ref.grid} \alias{regrid} \alias{rbind} \alias{rbind.ref.grid} \alias{as.mcmc} \alias{as.mcmc.ref.grid} \alias{[.ref.grid} \alias{xtable.ref.grid} \alias{xtable.summary.ref.grid} \alias{print.xtable.lsm} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Methods for \code{ref.grid} objects } \description{ Use these methods to summarize, print, plot, or examine objects of class \code{"ref.grid"}. They also apply to the class \code{"lsmobj"}, which is an extension of \code{"ref.grid"}. } \usage{ \method{summary}{ref.grid}(object, infer, level, adjust, by, type, df, null, delta, side, ...) \method{predict}{ref.grid}(object, type, ...) \method{str}{ref.grid}(object, ...) \method{rbind}{ref.grid}(..., deparse.level = 1, adjust = "mvt") \method{[}{ref.grid}(x, i, adjust = "mvt", ...) \method{print}{ref.grid}(x, ...) \method{print}{summary.ref.grid}(x, ..., digits = NULL, quote = FALSE, right = TRUE) \method{xtable}{ref.grid}(x, caption = NULL, label = NULL, align = NULL, digits = 4, display = NULL, auto = FALSE, ...) \method{xtable}{summary.ref.grid}(x, caption = NULL, label = NULL, align = NULL, digits = 4, display = NULL, auto = FALSE, ...) \method{print}{xtable.lsm}(x, type = getOption("xtable.type", "latex"), include.rownames = FALSE, sanitize.message.function = footnotesize, ...) \method{plot}{lsmobj}(x, y, type, intervals = TRUE, comparisons = FALSE, alpha = 0.05, adjust = "tukey", int.adjust, ...) \method{plot}{summary.ref.grid}(x, y, horizontal = TRUE, xlab, ylab, layout, ...) \method{vcov}{ref.grid}(object, ...) regrid (object, transform = c("response", "log", "none"), inv.log.lbl = "response", predict.type) \method{as.mcmc}{ref.grid}(x, names = TRUE, ...) } \arguments{ \item{object}{ An object of class \code{"ref.grid"}. } \item{infer}{ A vector of two logical values. The first determines whether confidence intervals are displayed, and the second determines whether \emph{t} tests and \emph{P} values are displayed. If only one value is provided, it is used for both. } \item{level}{ Confidence level for confidence intervals, if \code{infer[1]} is \code{TRUE}. } \item{adjust}{ Character value naming the method used to adjust \eqn{p} values or confidence limits; or to adjust comparison arrows in \code{plot}. See Details. } \item{by}{ Character name(s) of variables to use for grouping. This affects the family of tests considered in adjusted \emph{P} values. The printed display of the summary is grouped by the \code{by} variables. } \item{type}{ Type of prediction desired (except in \code{print.xtable}). This only has an effect if there is a known transformation or link function. \code{"response"} specifies that the inverse transformation be applied. Other valid values are \code{"link"}, \code{"lp"}, and \code{"linear"}; these are equivalent, and request that results be shown for the linear predictor. The default is \code{"link"}, unless the \code{"predict.type"} option is in force; see \code{\link{lsm.options}}. Note that \code{type} is also an argument for the \code{print.xtable} method; it is passed to \code{\link[xtable]{print.xtableList}} in the \pkg{xtable} package. } \item{df}{ If non-missing a constant number of degrees of freedom to use in constructing confidence intervals and \emph{P} values (\code{NA} specifies asymptotic results). } \item{null}{Null hypothesis value(s) against which estimates are tested. May be a single value used for all, or a numeric vector of length equal to the number of tests in each family (i.e., \code{by} group in the displayed table).} \item{delta}{Numeric value. If zero, ordinary tests of significance are performed. If positive, this specifies a threshold for testing equivalence (using the TOST or two-one-sided-test method), non-inferiority, or non-superiority, depending on \code{side}. See Details for how the test statistics are defined.} \item{side}{Numeric or character value specifying whether the test is left-tailed (\code{-1}, \code{"-"}, code{"<"}, \code{"left"}, or \code{"nonsuperiority"}); right-tailed (\code{1}, \code{"+"}, \code{">"}, \code{"right"}, or \code{"noninferiority"}); or two-sided (\code{0}, \code{2}, \code{"!="}, \code{"two-sided"}, \code{"both"}, \code{"equivalence"}, or \code{"="}).} \item{deparse.level}{This argument is needed by the generic \code{rbind} method, but ignored by its \code{ref.grid} method.} \item{x}{ The object to be subsetted, printed, plotted, or converted. } \item{y}{This argument is ignored.} \item{i}{Integer index(es) of which linear functions to extract.} \item{horizontal}{Determines orientation of plotted confidence intervals.} \item{intervals}{If \code{TRUE}, confidence intervals are plotted for each estimate} \item{comparisons}{If \code{TRUE}, \dQuote{comparison arrows} are added to the plot, in such a way that the degree to which arrows overlap reflects as much as possible the significance of the comparison of the two estimates.} \item{alpha}{The \code{alpha} argument to use in constructing comparison arrows.} \item{int.adjust}{the multiplicity adjustment method for the plotted confidence intervals; if missing, it defaults to the object's internal \code{adjust} setting (see \code{\link{update}}). (Note: the \code{adjust} argument in \code{plot} sets the adjust method for the comparison arrows, not the confidence intervals.)} \item{transform}{Character value. If \code{"response"}, the inverse transformation is applied to the estimates in the grid; if \code{"log"}, the results are formulated as if the response had been \code{log}-transformed; if \code{"none"}, predictions thereof are on the same scale as in \code{object}, and any internal transformation information is preserved. For compatibility with past versions, \code{transform} may also be logical; \code{TRUE} is taken as \code{"response"}, and \code{FALSE} as \code{"none"}.} \item{inv.log.lbl}{Character value. This applies only when \code{transform = "log"}, and is used to label the predictions if subsequently summarized with \code{type = "response"}.} \item{predict.type}{Character value. If provided, the returned object is first \code{\link{update}}d with the given type, e.g., \code{"response"}.} \item{names}{Logical scalar or vector specifying whether variable names are appended to levels in the column labels for the \code{as.mcmc} result -- e.g., column names of \code{treat A} and \code{treat B} versus just \code{A} and \code{B}. When there is more than one variable involved, the elements of \code{names} are used cyclically.} \item{\dots, digits, quote, right, caption, label, align, display, auto, include.rownames, sanitize.message.function, xlab, ylab, layout}{For summaries, these are additional arguments passed to other methods including \code{\link{print.data.frame}}, \code{\link{xtableList}}, \code{\link{print.xtableList}}, \code{\link{update}}, or \code{\link{dotplot}} as appropriate. If not specified, appropriate defaults are used. For example, the default \code{layout} is one column of horizontal panels or one row of vertical panels.} } \details{ \bold{Defaults:} The \code{misc} slot in \code{object} contains default values for \code{by}, \code{infer}, \code{level}, \code{adjust}, \code{type}, \code{null}, \code{side}, and \code{delta}. These defaults vary depending on the code that created the object. The \code{\link{update}} method may be used to change these defaults. In addition, any options set using \samp{lsm.options(summary=...)} will trump those stored in the object's \code{misc} slot. \bold{Transformations and links:} With \code{type="response"}, the transformation assumed can be found in \samp{object@misc$tran}, and its label, for the summary is in \samp{object@misc$inv.lbl}. At this time, \code{tran} must be one of the named transformations valid for \code{\link{make.link}}. Any \eqn{t} or \eqn{z} tests are still performed on the scale of the linear predictor, not the inverse-transformed one. Similarly, confidence intervals are computed on the linear-predictor scale, then inverse-transformed. \bold{Confidence-limit and P-value adjustments:} The \code{adjust} argument specifies a multiplicity adjustment for tests or confidence intervals. This adjustment always is applied \emph{separately} to each table or sub-table that you see in the printed output (see the details on \code{rbind} below for how to combine tables). The valid values of \code{adjust} are as follows: \describe{ \item{\code{"tukey"}}{Uses the Studentized range distribution with the number of means in the family. (Available for two-sided cases only.)} \item{\code{"scheffe"}}{Computes \eqn{p} values from the \eqn{F} distribution, according to the Scheffe critical value of \eqn{\sqrt{kF(k,d)}}{sqrt[k*F(k,d)]}, where \eqn{d} is the error degrees of freedom and \eqn{k} is (family size minus 1) for contrasts, and (number of estimates) otherwise. (Available for two-sided cases only.)} \item{\code{"sidak"}}{Makes adjustments as if the estimates were independent (a conservative adjustment in many cases).} \item{\code{"bonferroni"}}{Multiplies \eqn{p} values, or divides significance levels by the number of estimates. This is a conservative adjustment.} \item{\code{"dunnettx"}}{Uses an approximation to the Dunnett distribution for a family of estimates having pairwise correlations of \eqn{0.5} (as is true when comparing treatments with a control with equal sample sizes). The accuracy of the approximation improves with the number of simultaneous estimates, and is much faster than \code{"mvt"}. (Available for two-sided cases only.)} \item{\code{"mvt"}}{Uses the multivariate \eqn{t} distribution to assess the probability or critical value for the maximum of \eqn{k} estimates. This method produces the same \eqn{p} values and intervals as the default \code{summary} or \code{confint} methods to the results of \code{\link{as.glht}}. In the context of pairwise comparisons or comparisons with a control, this produces \dQuote{exact} Tukey or Dunnett adjustments, respectively. However, the algorithm (from the \pkg{mvtnorm} package) uses a Monte Carlo method, so results are not exactly repeatable unless the random-number seed is used (see \code{\link[base]{set.seed}}). As the family size increases, the required computation time will become noticeable or even intolerable, making the \code{"tukey"}, \code{"dunnettx"}, or others more attractive.} \item{\code{"none"}}{Makes no adjustments to the \eqn{p} values.} } % end \describe {} For P-value adjustments only, the Bonferroni-inequality-based adjustment methods in \code{\link{p.adjust}} are also available (currently, these include \code{"holm"}, \code{"hochberg"}, \code{"hommel"}, \code{"bonferroni"}, \code{"BH"}, \code{"BY"}, \code{"fdr"}, and \code{"none"}). If a \code{p.adjust.methods} method other than \code{"bonferroni"} or \code{"none"} is specified for confidence limits, the straight Bonferoni adjustment is used instead. Also, if an adjustment method is not appropriate (e.g., using \code{"tukey"} with one-sided tests, or with results that are not pairwise comparisons), a more appropriate method (usually \code{"sidak"}) is substituted. In some cases, confidence and \eqn{p}-value adjustments are only approximate -- especially when the degrees of freedom or standard errors vary greatly within the family of tests. The \code{"mvt"} method is always the correct one-step adjustment, but it can be very slow. One may use \code{\link{as.glht}} with methods in the \pkg{multcomp} package to obtain non-conservative multi-step adjustments to tests. \bold{\code{xtable}-related methods:} The \code{\link{xtable}} methods actually use \code{\link{xtableList}}, because of the ability to display messages such as those for P-value adjustments. These methods return an object of class \code{"xtable.lsm"} -- an extension of \code{"xtableList"}. Unlike other \code{xtable} methods, the number of digits defaults to 4; and degrees of freedom and \emph{t} ratios are always formatted independently of \code{digits}. The \code{print} method uses \code{\link{print.xtableList}}, and any \code{\dots} arguments are passed there. \bold{\code{rbind} and \code{[} methods:} \code{rbind} can be used to combine two or more reference grids into one. The \code{"["} method for \code{ref.grid}s may be used to obtain a subset. The primary reason for doing this would be to redefine the family of tests to which a P-value adjustment method applies. In \code{rbind}, the variables defined in the objects' grids are merged into one grid, and the returned object has no \dQuote{by} variables and the multiplicity adjustment method set to \code{"mvt"} (as this is likely the only appropriate one). \code{rbind} throws an error if there are any mismatches among the dimensions, fixed-effect coefficients, or covariance matrices. \bold{Non-estimable cases:} When the model is rank-deficient, each row \code{x} of \code{object}'s \code{linfct} slot is each checked for estimability. If \code{sum(x*bhat)} is found to be non-estimable, then an \code{NA} is displayed for the estimate (as well as any associated statistics). This check is performed using the orthonormal basis \code{N} in the \code{nbasis} slot for the null space of the rows of the model matrix. Estimability fails when \eqn{||Nx||^2 / ||x||^2} exceeds \code{tol}, which by default is \code{1e-8}. You may change it via \code{\link{lsm.options}} by setting \code{estble.tol} to the desired value. \bold{More on tests:} When \code{delta = 0}, test statistics are of the usual form \samp{(estimate - null)/SE}, or notationally, \eqn{t = (Q - \theta_0)/SE} where \eqn{Q} is our estimate of \eqn{\theta}; then left, right, or two-sided \eqn{p} values are produced. When \code{delta} is positive, the test statistic depends on \code{side} as follows. Left-sided (nonsuperiority, \eqn{H_0: \theta \ge \theta_0 + \delta} versus \eqn{H_1: \theta < \theta_0 + \delta}): \eqn{t = (Q - \theta_0 - \delta)/SE}. The \eqn{p} value is the lower-tail probability. Right-sided (noninferiority): \eqn{H_0: \theta \le \theta_0 - \delta} versus \eqn{H_1: \theta > \theta_0 - \delta}): \eqn{t = (Q - \theta_0 + \delta)/SE}. The \eqn{p} value is the upper-tail probability. Two-sided (equivalence): \eqn{H_0: |\theta - \theta_0| \ge \delta} versus \eqn{H_1: |\theta - \theta_0| < \delta}): \eqn{t = (|Q - \theta_0| - \delta)/SE}. The \eqn{p} value is the \emph{lower}-tail probability. \bold{Plots:} The \code{plot} method for \code{"lsmobj"} or \code{"summary.ref.grid"} objects (but not \code{"ref.grid"} objects themselves) produces a plot displaying confidence intervals for the estimates. If any \code{by} variables are in force, the plot is divided into separate panels. These functions use the \code{\link[lattice]{dotplot}} function, and thus require that the \pkg{lattice} package be installed. For \code{"summary.ref.grid"} objects, the \code{\dots} arguments in \code{plot} are passed \emph{only} to \code{dotplot}, whereas for \code{"lsmobj"} objects, the object is updated using \code{\dots} before summarizing and plotting. In plots with \code{comparisons = TRUE}, the resulting arrows are only approximate, and in some cases may fail to accurately reflect the pairwise comparisons of the estimates -- especially when estimates having large and small standard errors are intermingled in just the wrong way. \bold{Re-gridding:} The \code{regrid} function reparameterizes an existing \code{ref.grid} so that its \code{linfct} slot is the identity matrix and its \code{bhat} slot consists of the estimates at the grid points. If \code{transform} is \code{TRUE}, the inverse transform is applied to the estimates. Outwardly, the \code{summary} after applying \code{regrid} is identical to what it was before (using \samp{type="response"} if \code{transform} is \code{TRUE}). But subsequent contrasts will be conducted on the transformed scale -- which is the reason this function exists. See the example below. In cases where the degrees of freedom depended on the linear function being estimated, the d.f. from the reference grid are saved, and a kind of \dQuote{containment} method is substituted in the returned object whereby the calculated d.f. for a new linear function will be the minimum d.f. among those having nonzero coefficients. This is kind of an \emph{ad hoc} method, and it can over-estimate the degrees of freedom in some cases. \bold{MCMC samplers:} When the object's \code{post.beta} slot is non-trivial, \code{as.mcmc} will return an \code{\link[coda]{mcmc}} object that can be summarized or plotted using methods in the \pkg{coda} package. Specifically, \code{post.beta} is transformed by post-multiplying by \code{t(linfct)}, creating a sample from the posterior distribution of LS means. } \value{ The \code{summary} method for \code{"ref.grid"} objects returns an object of class \code{"summary.ref.grid"}, which extends \code{"data.frame"}. \code{xtable} returns an object of class \code{"xtable.lsm"}, as explained in details. \code{plot} returns an object of class \code{"trellis"}. \code{vcov} returns the covariance matrix of the product of the object's \code{linfct} and \code{bhat} slots. \code{as.mcmc} returns a \pkg{coda} \code{mcmc} object. } %%\references{} \author{ Russell V. Lenth } %\note{} \seealso{ Methods for the closely related \code{"lsmobj"} class can be found in \code{\link[lsmeans]{contrast}}, \code{\link[lsmeans]{cld}}, and \code{\link[lsmeans]{glht}}. For more on Bonferroni-based P-value adjustments, see \code{\link{p.adjust}}. Also, \code{\link{test}} and \code{\link{confint}} are essentially front-ends for \code{summary}, so additional examples may be found there. } \examples{ require(lsmeans) warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks) warp.rg <- ref.grid(warp.lm) str(warp.rg) summary(warp.rg) summary(warp.rg, by = "wool", infer = c(TRUE, FALSE), level = .90, adjust = "sidak") # Do all pairwise comparisons within rows or within columns, # all considered as one faily of tests: w.t <- pairs(lsmeans(warp.rg, ~ wool | tension)) t.w <- pairs(lsmeans(warp.rg, ~ tension | wool)) rbind(w.t, t.w) # Transformed response sqwarp.rg <- ref.grid(update(warp.lm, sqrt(breaks) ~ .)) summary(sqwarp.rg) # Back-transformed results - compare with summary of 'warp.rg' summary(sqwarp.rg, type = "response") # But differences of sqrts can't be back-transformed summary(pairs(sqwarp.rg, by = "wool"), type = "response") # We can do it via regrid sqwarp.rg2 <- regrid(sqwarp.rg) summary(sqwarp.rg2) # same as for sqwarp.rg with type = "response" pairs(sqwarp.rg2, by = "wool") # Logistic regression # Reshape the Titanic data Titan <- do.call("expand.grid", dimnames(Titanic)[-4]) Titan$Died <- matrix(Titanic, ncol=2) Titan.glm <- glm(Died ~ (Class + Sex + Age)^2, family = binomial, data = Titan) Titan.lsm <- lsmeans(Titan.glm, ~ Class|Sex, at = list(Age="Adult")) summary(Titan.lsm, type="response") summary(pairs(Titan.lsm), type="response") # Nonsuperiority test: Is any class no more likely to die than # the 1st class passengers? summary(contrast(Titan.lsm, "trt.vs.ctrl1"), delta = 1, adjust = "none", side = "<") # Plot 90% CIs on the response scale plot(Titan.lsm, type = "response", level = .90, xlab = "Predicted probability of drowning") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ models } \keyword{ htest } lsmeans/man/nutrition.Rd0000644000175100001440000000353713013732335015037 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{nutrition} \alias{nutrition} \docType{data} \title{ Nutrition data } \description{ This observational dataset involves three factors, but where several factor combinations are missing. It is used as a case study in Milliken and Johnson, Chapter 17, p.202. (You may also find it in the second edition, p.278.) } \usage{nutrition} \format{ A data frame with 107 observations on the following 4 variables. \describe{ \item{\code{age}}{a factor with levels \code{1}, \code{2}, \code{3}, \code{4}. Mother's age group.} \item{\code{group}}{a factor with levels \code{FoodStamps}, \code{NoAid}. Whether or not the family receives food stamp assistance.} \item{\code{race}}{a factor with levels \code{Black}, \code{Hispanic}, \code{White}. Mother's race.} \item{\code{gain}}{a numeric vector (the response variable). Gain score (posttest minus pretest) on knowledge of nutrition.} } } \details{ A survey was conducted by home economists ``to study how much lower-socioeconomic-level mothers knew about nutrition and to judge the effect of a training program designed to increase therir knowledge of nutrition.'' This is a messy dataset with several empty cells. } \source{ Milliken, G. A. and Johnson, D. E. (1984) \emph{Analysis of Messy Data -- Volume I: Designed Experiments}. Van Nostrand, ISBN 0-534-02713-7. } \examples{ require(lsmeans) nutr.aov <- aov(gain ~ (group + age + race)^2, data = nutrition) # Summarize predictions for age group 3 nutr.lsm <- lsmeans(nutr.aov, ~ race * group, at = list(age="3")) lsmip(nutr.lsm, race ~ group) # Hispanics seem exceptional; but, this doesn't test out due to very sparse data cld(nutr.lsm, by = "group") cld(nutr.lsm, by = "race") } \keyword{datasets} lsmeans/man/auto.noise.Rd0000644000175100001440000000417613013732335015070 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{auto.noise} \alias{auto.noise} \docType{data} \title{Auto Pollution Filter Noise} \description{Three-factor experiment comparing pollution-filter noise for two filters, three sizes of cars, and two sides of the car.} \usage{auto.noise} \format{ A data frame with 36 observations on the following 4 variables. \describe{ \item{\code{noise}}{Noise level in decibels - a numeric vector.} \item{\code{size}}{The size of the vehicle - an ordered factor with levels \code{S}, \code{M}, \code{L}.} \item{\code{type}}{Type of anti-pollution filter - a factor with levels \code{Std} and \code{Octel}} \item{\code{side}}{The side of the car where measurement was taken -- a factor with levels \code{L} and \code{R}.} } } \details{ The data are from a statement by Texaco, Inc., to the Air and Water Pollution Subcommittee of the Senate Public Works Committee on June 26, 1973. Mr. John McKinley, President of Texaco, cited an automobile filter developed by Associated Octel Company as effective in reducing pollution. However, questions had been raised about the effects of filters on vehicle performance, fuel consumption, exhaust gas back pressure, and silencing. On the last question, he referred to the data included here as evidence that the silencing properties of the Octel filter were at least equal to those of standard silencers.} \source{The dataset was imported from the Data and Story Library - \url{http://lib.stat.cmu.edu/DASL/Datafiles/airpullutionfiltersdat.html} (sic). However, the factor levels were assigned meaningful names, and the observations were sorted in random order as if this were the run order of the experiment. } \references{ A.Y. Lewin and M.F. Shakun (1976) \emph{Policy Sciences: Methodology and Cases}. Pergammon Press. p.313. } \examples{ require(lsmeans) noise.lm <- lm(noise ~ size * type * side, data = auto.noise) # Interaction plot of predictions lsmip(noise.lm, type ~ size | side) # Confidence intervals plot(lsmeans(noise.lm, ~ size | side*type)) } \keyword{datasets} lsmeans/man/lsmip.Rd0000644000175100001440000001036013013732335014120 0ustar hornikusers% Copyright (c) 2012-2016 Russell V. Lenth % \name{lsmip} \alias{lsmip} \alias{lsmip.default} \alias{pmmip} \title{ Least-squares (predicted marginal) means interaction plot } \description{ This function creates an interaction plot of the least-squares means based on a fitted model and a simple formula specification. } \usage{ \method{lsmip}{default}(object, formula, type, pch = c(1,2,6,7,9,10,15:20), lty = 1, col = NULL, plotit = TRUE, ...) pmmip(...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{ An object of class \code{lsmobj}, or a fitted model of a class supported by \code{\link{lsmeans}}. } \item{formula}{ Formula of the form \code{trace.factors ~ x.factors | by.factors}. The least-squares means are plotted against \code{x.factor} for each level of \code{trace.factors}. \code{by.factors} is optional, but if present, it determines separate panels. Each element of this formula may be a single factor in the model, or a combination of factors using the \code{*} operator. } \item{type}{ As in \code{\link[=predict.ref.grid]{predict}}, this determines whether we want to inverse-transform the predictions (\samp{type="response"}) or not (any other choice). The default is \code{"link"}, unless the \code{"predict.type"} option is in force; see \code{\link{lsm.options}}. } \item{pch}{ The plotting characters to use for each group (i.e., levels of \code{trace.factors}). They are recycled as needed. } \item{lty}{ The line types to use for each group. Recycled as needed. } \item{col}{ The colors to use for each group, recycled as needed. If not specified, the default trellis colors are used. } \item{plotit}{ If \code{TRUE}, the plot is displayed. Otherwise, one may use the \code{"lattice"} attribute of the returned object and print it, perhaps after additional manipulation. } \item{\dots}{ Additional arguments passed to \code{\link{lsmeans}} or to \code{\link[lattice]{xyplot}}. } } \details{ If \code{object} is a fitted model, \code{\link{lsmeans}} is called with an appropriate specification to obtain least-squares means for each combination of the factors present in \code{formula} (in addition, any arguments in \code{\dots} that match \code{at}, \code{trend}, \code{cov.reduce}, or \code{fac.reduce} are passed to \code{lsmeans}). Otherwise, if \code{object} is an \code{lsmobj} object, its first element is used, and it must contain one \code{lsmean} value for each combination of the factors present in \code{formula}. The wrapper \code{pmmip} is provided for those who prefer the term \sQuote{predicted marginal means}. } \value{ (Invisibly), a \code{\link{data.frame}} with the table of least-squares means that were plotted, with an additional \code{"lattice"} attribute containing the \code{trellis} object for the plot. } \author{ Russell V. Lenth } \note{ This function uses the \code{\link[lattice]{xyplot}} function in the \code{lattice} package (an error is returned if \code{lattice} is not installed). Conceptually, it is equivalent to \code{\link{interaction.plot}} where the summarization function is the least-squares means. } \seealso{ \code{\link{interaction.plot}} } \examples{ require(lsmeans) require(lattice) #--- Two-factor example warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks) # Following plot is the same as the usual interaction plot of the data lsmip(warp.lm, wool ~ tension) #--- Three-factor example noise.lm = lm(noise ~ size * type * side, data = auto.noise) # Separate interaction plots of size by type, for each side lsmip(noise.lm, type ~ size | side) # One interaction plot, using combinations of size and side as the x factor lsmip(noise.lm, type ~ side * size) # One interaction plot using combinations of type and side as the trace factor # customize the colors, line types, and symbols to suggest these combinations lsmip(noise.lm, type * side ~ size, lty=1:2, col=1:2, pch=c(1,1,2,2)) # 3-way interaction is significant, but doesn't make a lot of visual difference... noise.lm2 = update(noise.lm, . ~ . - size:type:side) lsmip(noise.lm2, type * side ~ size, lty=1:2, col=1:2, pch=c(1,1,2,2)) } \keyword{ models } \keyword{ regression }