rjags/0000755000176200001440000000000012661717157011375 5ustar liggesusersrjags/configure.ac0000644000176200001440000001532112643166746013667 0ustar liggesusersAC_INIT([rjags], [4]) if test -z "${R_HOME}"; then AC_MSG_ERROR("R_HOME is not defined") fi CC=`"${R_HOME}/bin/R" CMD config CC` CXX=`"${R_HOME}/bin/R" CMD config CXX` CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS` CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXXFLAGS` CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` # Try using pkg-config to set compiler flags PKG_CONFIG_OK="FALSE" AC_PATH_PROG([PKG_CONFIG], [pkg-config], [], [$PATH:/usr/local/bin]) if test [ -n "${PKG_CONFIG}" ] ; then if `${PKG_CONFIG} --exists jags`; then if `${PKG_CONFIG} --exists --atleast-version=${PACKAGE_VERSION}.0.0 jags` && `${PKG_CONFIG} --exists --max-version=${PACKAGE_VERSION}.99.99 jags`; then JAGS_LIBS=`${PKG_CONFIG} --libs jags` JAGS_CFLAGS=`${PKG_CONFIG} --cflags jags` JAGS_LIBDIR=`${PKG_CONFIG} --variable libdir jags` if test [ -z "${JAGS_LIBS}" -a -z "${JAGS_CFLAGS}" ] ; then AC_MSG_WARN([Cannot get information about jags ${PACKAGE_VERSION} from pkg-config]) else AC_MSG_NOTICE([Setting compile and link flags according to pkg-config]) AC_MSG_NOTICE([Compile flags are $JAGS_CFLAGS]) AC_MSG_NOTICE([Link flags are $JAGS_LIBS]) PKG_CONFIG_OK="TRUE" fi else jags_installed_version=`${PKG_CONFIG} --modversion jags` AC_MSG_ERROR([pkg-config found JAGS ${jags_installed_version} but rjags requires JAGS ${PACKAGE_VERSION}.x.y]) fi else AC_MSG_WARN([pkg-config file for jags ${PACKAGE_VERSION} unavailable]) AC_MSG_WARN([Consider adding the directory containing `jags.pc`]) AC_MSG_WARN([to the PKG_CONFIG_PATH environment variable]) fi fi ########################## Begin legacy configuration ##################### if test "${PKG_CONFIG_OK}" != TRUE; then AC_MSG_NOTICE([Attempting legacy configuration of rjags]) # Set JAGS_PREFIX # # For installations in a non-standard location, set the prefix for # JAGS manually, using either option --with-jags-prefix or environment # variable JAGS_PREFIX. Otherwise we get the prefix from the jags # executable. AC_ARG_WITH([jags-prefix], AC_HELP_STRING([--with-jags-prefix=PREFIX_PATH], [Prefix used when JAGS was installed]), [jags_prefix_path=$withval], [jags_prefix_path=""]) if test [ -n "$jags_prefix_path" ] ; then JAGS_PREFIX=${jags_prefix_path} else if test [ -z "${JAGS_PREFIX}" ] ; then AC_PATH_PROG(jags_bin_path, [jags]) if test -n "$jags_bin_path"; then jags_bin_path=`AS_DIRNAME(["$jags_bin_path"])` JAGS_PREFIX=`AS_DIRNAME(["$jags_bin_path"])` else AC_MSG_ERROR("automatic detection of JAGS failed. Please use pkg-config to locate the JAGS library. See the INSTALL file for details.") fi fi fi # Set include path with option --with-jags-includedir or environment # variable JAGS_INCLUDEDIR. By default, this is determined from # JAGS_PREFIX. AC_ARG_WITH([jags-includedir], AC_HELP_STRING([--with-jags-includedir=INCLUDE_PATH], [the value of includedir used when JAGS was installed]), [jags_include_path=$withval], [jags_include_path=""]) if test [ -n "$jags_include_path" ] ; then JAGS_INCLUDEDIR=${jags_include_path} else if test [ -z "${JAGS_INCLUDEDIR}" ] ; then JAGS_INCLUDEDIR=${JAGS_PREFIX}/include fi fi JAGS_PKGINCLUDEDIR=${JAGS_INCLUDEDIR}/JAGS AC_LANG_PUSH(C++) OCPPFLAGS=${CPPFLAGS} CPPFLAGS=-I${JAGS_PKGINCLUDEDIR} AC_CHECK_HEADER(Console.h, [], [AC_MSG_ERROR("cannot find header file ${JAGS_PKGINCLUDEDIR}/Console.h. Please use pkg-config to locate the JAGS library. See the INSTALL file for details.")]) CPPFLAGS=${OCPPFLAGS} AC_LANG_POP() # There is no consensus on where 64- versus 32-bit libraries should be # installed. We take a hint from R, assuming that if R is installed # in, say, /usr/LIBnn/R then we can find the jags library in # ${JAGS_PREFIX}/LIBnn. # # This is a reasonable assumption if both JAGS and R have been # properly packaged, but may not be true if either has been manually # installed. The user can set the environment variable LIBnn to # override this. if test -z "${LIBnn}"; then LIBnn=`"${R_HOME}/bin/R" CMD config LIBnn` fi # Set optional library path with option --with-jags-libdir or # environment variable JAGS_LIBDIR. By default this is determined by # JAGS_PREFIX and LIBnn. AC_ARG_WITH([jags-libdir], AC_HELP_STRING([--with-jags-libdir=LIB_PATH], [the value of libdir used when JAGS was installed]), [jags_lib_path=$withval]) if test [ -n "$jags_lib_path" ] ; then JAGS_LIBDIR=$jags_lib_path else if test [ -z "${JAGS_LIBDIR}" ] ; then JAGS_LIBDIR=${JAGS_PREFIX}/${LIBnn} fi fi JAGS_CFLAGS="-I${JAGS_PKGINCLUDEDIR}" JAGS_LIBS="-L${JAGS_LIBDIR} -ljags" AC_MSG_NOTICE([Compile flags are $JAGS_CFLAGS]) AC_MSG_NOTICE([Link flags are $JAGS_LIBS]) fi ####################### END legacy configuration ########################## # Support for private installations of JAGS. If the jags library is not # on the linker path then it helps to hard-code the location of JAGS into # the DSO for rjags. Suggested by Doug Bates, April 2011. AC_ARG_ENABLE([rpath], AC_HELP_STRING([--enable-rpath], [Hard link the location of the JAGS library])) if test [ "x$enable_rpath" = "xyes"]; then JAGS_RPATH="-Wl,-rpath ${JAGS_LIBDIR}" fi # Sanity checks. OLDFLAGS=${LDFLAGS} OCFLAGS=${CFLAGS} AC_LANG_PUSH(C) CFLAGS=${JAGS_CFLAGS} # Can we link to the library? LDFLAGS="-L${JAGS_LIBDIR}" AC_CHECK_LIB(jags, jags_version, [], [AC_MSG_ERROR("cannot link to JAGS library in ${JAGS_LIBDIR}.")]) AC_LANG_POP(C) CFLAGS=${OCFLAGS} # Does the library have the correct major version number? AC_LANG_PUSH(C++) OCXXFLAGS=${CXXFLAGS} CXXFLAGS=${JAGS_CFLAGS} LDFLAGS="${JAGS_LIBS} ${JAGS_RPATH}" AC_MSG_CHECKING([version of JAGS library]) # Need to run this trivial test program first to detect runtime linkage problems AC_RUN_IFELSE( [AC_LANG_SOURCE([ #include int main() { return 0; }])], [], [AC_MSG_RESULT([link error]) AC_MSG_ERROR([Runtime link error: See the INSTALL file for help])]) # Now we can test AC_RUN_IFELSE( [AC_LANG_SOURCE([ #include int main() { return JAGS_MAJOR == ${PACKAGE_VERSION} ? 0 : 1; }])], [AC_MSG_RESULT([OK])], [AC_MSG_RESULT([wrong version]) AC_MSG_ERROR([rjags requires JAGS version ${PACKAGE_VERSION}.x.y])]) AC_LANG_POP() LDFLAGS=${OLDFLAGS} CXXFLAGS=${OCXXFLAGS} # Set location of modules JAGS_MODDIR=${JAGS_LIBDIR}/JAGS/modules-${PACKAGE_VERSION} AC_SUBST(JAGS_LIBS) AC_SUBST(JAGS_CFLAGS) AC_SUBST(JAGS_MODDIR) AC_SUBST(JAGS_RPATH) AC_OUTPUT(src/Makevars) AC_OUTPUT(R/unix/zzz.R) rjags/src/0000755000176200001440000000000012661614402012151 5ustar liggesusersrjags/src/jags.cc0000644000176200001440000004346712661614452013427 0ustar liggesusers/* * R package rjags file src/jags.cc * Copyright (C) 2006-2016 Martyn Plummer * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * version 2 as published by the Free Software Foundation. * * This program 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. * * A copy of the GNU General Public License is available at * http://www.r-project.org/Licenses/ */ #include #include #include #include #include #include #include #include #include #include #include #include using std::string; using std::map; using std::pair; using std::vector; using std::copy; using std::fopen; using std::fclose; using std::FILE; using jags::SArray; using jags::Console; using jags::SimpleRange; using jags::DUMP_DATA; using jags::DUMP_PARAMETERS; using jags::FactoryType; using jags::SAMPLER_FACTORY; using jags::MONITOR_FACTORY; using jags::RNG_FACTORY; using jags::RNG; /* Workaround length being remapped to Rf_length by the preprocessor */ static unsigned int sarray_len(SArray const &s) { return s.length(); } #include #include #include #include std::ostringstream jags_out; //Output stream std::ostringstream jags_err; //Error stream static SEXP JAGS_console_tag; //Run-time type checking for external pointer static bool quiet=false; //Suppress information messages static void checkConsole (SEXP s) { if (TYPEOF(s) != EXTPTRSXP || R_ExternalPtrTag(s) != JAGS_console_tag) { error("bad JAGS console pointer"); } } static int intArg(SEXP arg) { if (!isNumeric(arg)) { error("Invalid integer parameter"); } SEXP intarg; PROTECT(intarg = AS_INTEGER(arg)); int i = INTEGER(intarg)[0]; UNPROTECT(1); //intarg return i; } static char const *stringArg(SEXP arg, unsigned int i = 0) { if (!isString(arg)) { error("Invalid string parameter"); } return R_CHAR(STRING_ELT(arg,i)); } static bool boolArg(SEXP arg) { if (!isLogical(arg)) { error("Invalid logical parameter"); } return LOGICAL_POINTER(arg)[0]; } static Console * ptrArg(SEXP ptr) { checkConsole(ptr); Console *console = static_cast(R_ExternalPtrAddr(ptr)); if (console == NULL) error("JAGS model must be recompiled"); return console; } static void printMessages(bool status) { /* Print any messages from JAGS and clear the stream buffer */ if(!jags_out.str().empty()) { if (!quiet) Rprintf("%s\n", jags_out.str().c_str()); jags_out.str(""); } string msg; if (!jags_err.str().empty()) { msg = jags_err.str(); jags_err.str(""); } if (status == true) { if (!msg.empty()) { warning("%s\n", msg.c_str()); } } else { //Something bad happened if (!msg.empty()) { error("%s\n", msg.c_str()); } else { error("Internal error in JAGS library"); } } } static void setSArrayValue(SArray &sarray, SEXP e) { vector v(length(e)); copy(NUMERIC_POINTER(e), NUMERIC_POINTER(e) + length(e), v.begin()); //Replace R missing values with JAGS missing values for (vector::iterator p = v.begin(); p != v.end(); ++p) { if (ISNA(*p)) { *p = JAGS_NA; } } sarray.setValue(v); } /* Write data from an R list into a JAGS data table */ static void writeDataTable(SEXP data, map &table) { SEXP names = getAttrib(data, R_NamesSymbol); if (!isNewList(data)) { error("data must be a list"); } if (length(names) != length(data)) { error("data must be a named list"); } for (int i = 0; i < length(data); ++i) { SEXP e = AS_NUMERIC(VECTOR_ELT(data, i)); if (length(e) > 0) { string ename = CHAR(STRING_ELT(names, i)); SEXP dim = getAttrib(VECTOR_ELT(data, i), R_DimSymbol); if (dim == R_NilValue) { // Scalar or vector entry. SArray sarray(vector(1, length(e))); setSArrayValue(sarray, e); table.insert(pair(ename, sarray)); } else { // Array entry int ndim = length(dim); vector idim(ndim); for (int j = 0; j < ndim; ++j) { idim[j] = INTEGER(dim)[j]; } SArray sarray(idim); setSArrayValue(sarray, e); table.insert(pair(ename, sarray)); } } } } static SimpleRange makeRange(SEXP lower, SEXP upper) { if (lower == R_NilValue || upper == R_NilValue) { return SimpleRange(); } if (length(lower) != length(upper)) { error("length mismatch between lower and upper limits"); } int n = length(lower); SEXP il, iu; PROTECT(il = AS_INTEGER(lower)); PROTECT(iu = AS_INTEGER(upper)); vector lvec(n), uvec(n); copy(INTEGER(il), INTEGER(il) + n, lvec.begin()); copy(INTEGER(iu), INTEGER(iu) + n, uvec.begin()); UNPROTECT(2); //il, iu SimpleRange r; try { r = SimpleRange(lvec, uvec); } catch (std::logic_error except) { error("Invalid range"); } return r; } /* Read data from a JAGS data table into an R list */ static SEXP readDataTable(map const &table) { int N = table.size(); SEXP data; PROTECT(data = allocVector(VECSXP, N)); int i; map::const_iterator p; for (i = 0, p = table.begin(); p != table.end(); ++p, ++i) { int len = sarray_len(p->second); //Allocate new numeric vector SEXP e; PROTECT(e = allocVector(REALSXP, len)); //Copy values vector const &value = p->second.value(); for (int j = 0; j < len; ++j) { if (value[j] == JAGS_NA) { NUMERIC_POINTER(e)[j] = NA_REAL; } else { NUMERIC_POINTER(e)[j] = value[j]; } } if (p->second.ndim(false) > 1) { //Set dim attribute vector const &idim = p->second.dim(false); unsigned int ndim = idim.size(); SEXP dim; PROTECT(dim = allocVector(INTSXP, ndim)); for (unsigned int k = 0; k < ndim; ++k) { INTEGER(dim)[k] = idim[k]; } //Set names of the dimensions vector const &names = p->second.dimNames(); if (!names.empty()) { SEXP dimnames; PROTECT(dimnames = allocVector(STRSXP, ndim)); for (unsigned int k = 0; k < ndim; ++k) { SET_STRING_ELT(dimnames, k, mkChar(names[k].c_str())); } setAttrib(dim, R_NamesSymbol, dimnames); UNPROTECT(1); //dimnames } SET_DIM(e, dim); UNPROTECT(1); //dim //Set S dimnames bool set_s_dimnames = false; for (unsigned int k = 0; k < ndim; ++k) { if (!p->second.getSDimNames(k).empty()) { set_s_dimnames = true; break; } } if (set_s_dimnames) { SEXP sdimnames; PROTECT(sdimnames = allocVector(VECSXP, ndim)); for (unsigned int k = 0; k < ndim; ++k) { vector const &names_k = p->second.getSDimNames(k); if (names_k.empty()) { SET_VECTOR_ELT(sdimnames, k, R_NilValue); } else { SEXP snames_k; PROTECT(snames_k = allocVector(STRSXP, names_k.size())); for (unsigned int l = 0; l < names_k.size(); ++l) { SET_STRING_ELT(sdimnames, l, mkChar(names_k[l].c_str())); } UNPROTECT(1); //snames_k } } setAttrib(e, R_DimNamesSymbol, sdimnames); UNPROTECT(1); //sdimnames } } else if (!p->second.getSDimNames(0).empty()) { //Set names attribute SEXP snames; vector const &names = p->second.getSDimNames(0); PROTECT(snames = allocVector(STRSXP, names.size())); for (unsigned int l = 0; l < names.size(); ++l) { SET_STRING_ELT(snames, l, mkChar(names[l].c_str())); } setAttrib(e, R_NamesSymbol, snames); UNPROTECT(1); //snames } SET_ELEMENT(data, i, e); UNPROTECT(1); //e } //Set names SEXP names; PROTECT(names = allocVector(STRSXP, table.size())); for (i = 0, p = table.begin() ; p != table.end(); ++p, ++i) { SET_STRING_ELT(names, i, mkChar(p->first.c_str())); } setAttrib(data, R_NamesSymbol, names); UNPROTECT(2); //names, data return data; } static FactoryType asFactoryType(SEXP type) { string ft = stringArg(type); FactoryType ans = SAMPLER_FACTORY; if (ft == "sampler") { ans = SAMPLER_FACTORY; } else if (ft == "rng") { ans = RNG_FACTORY; } else if (ft == "monitor") { ans = MONITOR_FACTORY; } else { error("Invalid factory type"); } return ans; } extern "C" { void quietMessages(SEXP s) { quiet = boolArg(s); } void R_init_rjags(DllInfo *info) { JAGS_console_tag = install("JAGS_CONSOLE_TAG"); } void R_unload_rjags(DllInfo *info) { //FIXME: Need to zero console pointers vector loaded_modules = Console::listModules(); for (vector::reverse_iterator p = loaded_modules.rbegin(); p != loaded_modules.rend(); ++p) { Console::unloadModule(*p); } } SEXP clear_console(SEXP s) { /* Finalizer for console pointers. Frees the external memory and zeroes the pointer when the R object is deleted */ checkConsole(s); Console *console = static_cast(R_ExternalPtrAddr(s)); if (console != NULL) { delete console; R_ClearExternalPtr(s); } return R_NilValue; } SEXP make_console() { void *p = static_cast(new Console(jags_out, jags_err)); SEXP ptr = R_MakeExternalPtr(p, JAGS_console_tag, R_NilValue); R_RegisterCFinalizer(ptr, (R_CFinalizer_t) clear_console); return ptr; } SEXP check_model(SEXP ptr, SEXP name) { /* Name should be a name of a file containing the model */ string sname = R_ExpandFileName(stringArg(name)); FILE *file = fopen(sname.c_str(), "r"); if (!file) { jags_err << "Failed to open file " << sname << "\n"; return R_NilValue; } else { bool status = ptrArg(ptr)->checkModel(file); printMessages(status); fclose(file); return R_NilValue; } } SEXP compile(SEXP ptr, SEXP data, SEXP nchain, SEXP gendata) { if (!isNumeric(nchain)) { error("nchain must be numeric"); } if (!isVector(data)) { error("invalid data"); } map table; writeDataTable(data, table); bool status = ptrArg(ptr)->compile(table, intArg(nchain), boolArg(gendata)); printMessages(status); return R_NilValue; } SEXP set_parameters(SEXP ptr, SEXP data, SEXP nchain) { map data_table; writeDataTable(data, data_table); bool status = ptrArg(ptr)->setParameters(data_table, intArg(nchain)); printMessages(status); return R_NilValue; } SEXP set_rng_name(SEXP ptr, SEXP name, SEXP chain) { bool status = ptrArg(ptr)->setRNGname(stringArg(name), intArg(chain)); printMessages(status); return R_NilValue; } SEXP initialize(SEXP ptr) { bool status = ptrArg(ptr)->initialize(); printMessages(status); return R_NilValue; } SEXP check_adaptation(SEXP ptr) { Console *console = ptrArg(ptr); bool status = true; console->checkAdaptation(status); return ScalarLogical(status); } SEXP is_adapting(SEXP ptr) { Console *console = ptrArg(ptr); return ScalarLogical(console->isAdapting()); } SEXP adapt_off(SEXP ptr) { Console *console = ptrArg(ptr); console->adaptOff(); return R_NilValue; } SEXP update(SEXP ptr, SEXP rniter) { int niter = intArg(rniter); Console *console = ptrArg(ptr); if (!console->update(niter)) { Rprintf("\n"); printMessages(false); } return R_NilValue; } SEXP set_monitors(SEXP ptr, SEXP names, SEXP lower, SEXP upper, SEXP thin, SEXP type) { if (!isString(names)) { error("names must be a character vector"); } int n = length(names); if (length(lower) != n || length(upper) != n) { error("length of names must match length of lower and upper"); } SEXP status; //Was attempt to set monitor successful? PROTECT(status = allocVector(LGLSXP, n)); for (int i = 0; i < n; ++i) { SimpleRange range = makeRange(VECTOR_ELT(lower, i), VECTOR_ELT(upper, i)); bool ok = ptrArg(ptr)->setMonitor(stringArg(names,i), range, intArg(thin), stringArg(type)); printMessages(true); LOGICAL_POINTER(status)[i] = ok; } UNPROTECT(1); //status return status; } SEXP clear_monitor(SEXP ptr, SEXP name, SEXP lower, SEXP upper, SEXP type) { SimpleRange range = makeRange(lower, upper); bool status = ptrArg(ptr)->clearMonitor(stringArg(name), range, stringArg(type)); printMessages(status); return R_NilValue; } SEXP get_monitored_values(SEXP ptr, SEXP type) { map data_table; bool status = ptrArg(ptr)->dumpMonitors(data_table, stringArg(type), false); printMessages(status); return readDataTable(data_table); } //FIXME: lazy cut-and-paste here SEXP get_monitored_values_flat(SEXP ptr, SEXP type) { map data_table; bool status = ptrArg(ptr)->dumpMonitors(data_table, stringArg(type), true); printMessages(status); return readDataTable(data_table); } SEXP get_data(SEXP ptr) { map data_table; string rngname; //Not actually needed bool status = ptrArg(ptr)->dumpState(data_table, rngname, DUMP_DATA, 1); printMessages(status); return readDataTable(data_table); } SEXP get_state(SEXP ptr) { Console *console = ptrArg(ptr); unsigned int nchain = console->nchain(); if (nchain == 0) { return R_NilValue; } //ans is the list that contains the state for each chain SEXP ans; PROTECT(ans = allocVector(VECSXP, nchain)); for (unsigned int n = 0; n < nchain; ++n) { string srng; map param_table; console->dumpState(param_table, srng, DUMP_PARAMETERS, n+1); //Read the parameter values into an R list SEXP params; PROTECT(params = readDataTable(param_table)); int nparam = length(params); SEXP names = getAttrib(params, R_NamesSymbol); //Now we have to make a copy of the list with an extra element SEXP staten, namesn; PROTECT(staten = allocVector(VECSXP, nparam + 1)); PROTECT(namesn = allocVector(STRSXP, nparam + 1)); for (int j = 0; j < nparam; ++j) { SET_ELEMENT(staten, j, VECTOR_ELT(params, j)); SET_STRING_ELT(namesn, j, STRING_ELT(names, j)); } //Assign .RNG.name as the last element SEXP rngname; PROTECT(rngname = mkString(srng.c_str())); SET_ELEMENT(staten, nparam, rngname); SET_STRING_ELT(namesn, nparam, mkChar(".RNG.name")); setAttrib(staten, R_NamesSymbol, namesn); //And we're done with this chain SET_ELEMENT(ans, n, staten); UNPROTECT(4); //rngname, namesn, staten, params } UNPROTECT(1); //ans return ans; } SEXP get_variable_names(SEXP ptr) { Console *console = ptrArg(ptr); vector const &namevec = console->variableNames(); SEXP varnames; PROTECT(varnames = allocVector(STRSXP,namevec.size())); for (unsigned int i = 0; i < namevec.size(); ++i) { SET_STRING_ELT(varnames, i, mkChar(namevec[i].c_str())); } UNPROTECT(1); //varnames return varnames; } SEXP get_samplers(SEXP ptr) { Console *console = ptrArg(ptr); vector > samplers; bool status = console->dumpSamplers(samplers); printMessages(status); unsigned int n = samplers.size(); SEXP node_list, sampler_names; PROTECT(node_list = allocVector(VECSXP, n)); PROTECT(sampler_names = allocVector(STRSXP, n)); for (unsigned int i = 0; i < n; ++i) { int nnode = samplers[i].size() - 1; SEXP e; PROTECT(e=allocVector(STRSXP, nnode)); for (int j = 0; j < nnode; ++j) { SET_STRING_ELT(e, j, mkChar(samplers[i][j+1].c_str())); } SET_ELEMENT(node_list, i, e); SET_STRING_ELT(sampler_names, i, mkChar(samplers[i][0].c_str())); UNPROTECT(1); //e } setAttrib(node_list, R_NamesSymbol, sampler_names); UNPROTECT(2); //node_list, sampler_names return node_list; } SEXP get_factories(SEXP type) { FactoryType ft = asFactoryType(type); vector > factories = Console::listFactories(ft); unsigned int n = factories.size(); SEXP fac_list; PROTECT(fac_list = allocVector(VECSXP, 2)); SEXP names, status; PROTECT(names = allocVector(STRSXP, n)); PROTECT(status = allocVector(LGLSXP, n)); for (unsigned int i = 0; i < n; ++i) { SET_STRING_ELT(names, i, mkChar(factories[i].first.c_str())); LOGICAL_POINTER(status)[i] = factories[i].second; } SET_ELEMENT(fac_list, 0, names); SET_ELEMENT(fac_list, 1, status); UNPROTECT(2); //names, status SEXP fac_names; PROTECT(fac_names = allocVector(STRSXP,2)); SET_STRING_ELT(fac_names, 0, mkChar("factory")); SET_STRING_ELT(fac_names, 1, mkChar("status")); setAttrib(fac_list, R_NamesSymbol, fac_names); UNPROTECT(1); //fac_names UNPROTECT(1); //fac_list return fac_list; } SEXP set_factory_active(SEXP name, SEXP type, SEXP status) { Console::setFactoryActive(stringArg(name), asFactoryType(type), boolArg(status)); return R_NilValue; } SEXP get_iter(SEXP ptr) { return ScalarInteger(ptrArg(ptr)->iter()); } SEXP get_nchain(SEXP ptr) { return ScalarInteger(ptrArg(ptr)->nchain()); } SEXP load_module(SEXP name) { return ScalarLogical(Console::loadModule(stringArg(name))); } SEXP unload_module(SEXP name) { return ScalarLogical(Console::unloadModule(stringArg(name))); } SEXP get_modules() { vector modules = Console::listModules(); unsigned int n = modules.size(); SEXP mod_list; PROTECT(mod_list = allocVector(STRSXP, n)); for (unsigned int i = 0; i < n; ++i) { SET_STRING_ELT(mod_list, i, mkChar(modules[i].c_str())); } UNPROTECT(1); //mod_list return mod_list; } SEXP get_version() { return mkString(jags_version()); } } rjags/src/parallel.cc0000644000176200001440000000630412661614452014264 0ustar liggesusers/* * R package rjags file src/parallel.cc * Copyright (C) 2011-2013 Martyn Plummer * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * version 2 as published by the Free Software Foundation. * * This program 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. * * A copy of the GNU General Public License is available at * http://www.r-project.org/Licenses/ */ /* Utility function to get parallel seeds for execution in parallel * * Makes extensive use of JAGS internals and is potentially unsafe on Windows * if an exception is thrown. */ #include #include #include #include #include #include #include using std::string; using std::pair; using std::vector; using std::list; using jags::RNG; using jags::RNGFactory; using jags::Model; #include #include #include //Cut and pasted from jags.cc static int intArg(SEXP arg) { if (!isNumeric(arg)) { error("Invalid integer parameter"); } SEXP intarg; PROTECT(intarg = AS_INTEGER(arg)); int i = INTEGER(intarg)[0]; UNPROTECT(1); //intarg return i; } static char const *stringArg(SEXP arg, unsigned int i = 0) { if (!isString(arg)) { error("Invalid string parameter"); } return R_CHAR(STRING_ELT(arg,i)); } extern "C" { SEXP parallel_seeds(SEXP fac, SEXP n) { unsigned int nchain = intArg(n); string factory = stringArg(fac); vector rngvec; list > const &flist = Model::rngFactories(); for (list >::const_iterator p = flist.begin(); p != flist.end(); ++p) { if (p->first->name() == factory) { if (p->second) { rngvec = p->first->makeRNGs(nchain); break; } else { string msg = string("RNG factory not active: ") + factory; error(msg.c_str()); } } } if (rngvec.empty()) { string msg = string("RNG factory not found: ") + factory; error(msg.c_str()); } SEXP rng_list; PROTECT(rng_list = allocVector(VECSXP, rngvec.size())); SEXP elt_names; PROTECT(elt_names = allocVector(STRSXP, 2)); SET_STRING_ELT(elt_names, 0, mkChar(".RNG.name")); SET_STRING_ELT(elt_names, 1, mkChar(".RNG.state")); for (unsigned int i = 0; i < rngvec.size(); ++i) { SEXP rngname; PROTECT(rngname = mkString(rngvec[i]->name().c_str())); SEXP rngstate; vector istate; rngvec[i]->getState(istate); PROTECT(rngstate = allocVector(INTSXP, istate.size())); for (unsigned int j = 0; j < istate.size(); ++j) { INTEGER(rngstate)[j] = istate[j]; } SEXP rng_i; PROTECT(rng_i = allocVector(VECSXP, 2)); SET_ELEMENT(rng_i, 0, rngname); SET_ELEMENT(rng_i, 1, rngstate); UNPROTECT(2); //istate, rngstate setAttrib(rng_i, R_NamesSymbol, elt_names); SET_ELEMENT(rng_list, i, rng_i); UNPROTECT(1); //rng_i; } UNPROTECT(2); //elt_names, rng_list return rng_list; } } rjags/src/Makevars.in0000644000176200001440000000007612661614452014262 0ustar liggesusersPKG_CPPFLAGS=@JAGS_CFLAGS@ PKG_LIBS=@JAGS_LIBS@ @JAGS_RPATH@ rjags/src/Makevars.win0000644000176200001440000000013012661614452014440 0ustar liggesusersPKG_CPPFLAGS=-I"$(JAGS_ROOT)/include" PKG_LIBS=-L"$(JAGS_ROOT)/${R_ARCH}/bin" -ljags-4 rjags/NAMESPACE0000644000176200001440000000155512612424177012613 0ustar liggesusersif(.Platform$OS.type == "windows") { importFrom(utils, readRegistry, winProgressBar, setWinProgressBar, packageVersion, file_test) } importFrom(utils, txtProgressBar, setTxtProgressBar) importFrom(stats, variable.names, sd, update) importFrom(coda, as.mcmc.list, as.mcmc, mcmc.list, mcmc, spectrum0, "varnames<-") export ( jags.model, jags.samples, coda.samples, load.module, unload.module, list.modules, dic.samples, diffdic, adapt, list.samplers, list.factories, set.factory, read.data, read.jagsdata, read.bugsdata, parallel.seeds ) S3method(print, jags) S3method(coef, jags) S3method(update, jags) S3method(variable.names, jags) S3method(print, mcarray) S3method(summary, mcarray) S3method(print, summary.mcarray) S3method(as.mcmc.list, mcarray) S3method("-", dic) S3method(print, dic) S3method(print, diffdic) rjags/INSTALL0000644000176200001440000001716512643206534012430 0ustar liggesusersInstallation instructions for the rjags package ----------------------------------------------- The rjags package is an interface to the JAGS library. In order to build a binary package, or install from source, you must have a matching installation of the JAGS library. Contents: 1 Windows 2 Unix/Linux: pkg-config 3 Unix/Linux: runtime linkage 4 Unix/Linux: legacy configuration 1 Windows --------- If you are compiling the rjags package from source on Windows then you must supply the location of JAGS using the make variable JAGS_ROOT, defining it in the file HOME/.R/Makevars.win, e.g. JAGS_ROOT=c:/Progra~1/JAGS/JAGS-4.0.0 where HOME is the default starting directory for an R session launched from the start menu (typically your Documents folder). If you are installing the Windows binary rjags package, then R will consult the Windows registry to find the location of JAGS. Alternatively, you can specify the location of JAGS by setting the environment variable JAGS_HOME. 2 Unix/Linux: pkg-config ------------------------ If you have pkg-config installed then the configure script will use it to detect the JAGS library and set the compiler and linker flags for rjags. You can test this capability in a unix shell by typing pkg-config --modversion jags If this does not detect your JAGS installation then it means that the file 'jags.pc' is not on the search path of pkg-config. In this case you should add it by setting the environment variable PKG_CONFIG_PATH. For example if the full path to 'jags.pc' is '/usr/local/lib64/pkgconfig/jags.pc', then set export PKG_CONFIG_PATH=/usr/local/lib64/pkgconfig before installing rjags. Recall that most Linux distributions divide software into `user' and `developer' versions, so if you are using a pre-built JAGS binary package for your Linux distribution then remember to install the developer version before attempting to install the rjags package. 3 Unix/Linux: runtime linkage ----------------------------- Although pkg-config helps to find the right flags for linking to the JAGS library at compile time, it does not help at runtime. In order to detect potential problems with runtime linkage, the configure script of the rjags package will compile and run a test program that links to the JAGS library. If this fails with the message "runtime link error" then you may take the following actions to fix the problem: If you have administrative privileges (sudo or root access): 1) Add the directory containing the JAGS library to the linker path, typically by editing the file /etc/ld.so.conf. 2) If the directory is already on the linker path then you may need to run /sbin/ldconfig as root to update the linker cache. If you do not have administrative privileges: 3) Set the environment variable LD_RUN_PATH to the directory containing the JAGS library before installing rjags, or 4) Use the --enable-rpath option when installing rjags, i.e. from the unix shell: R CMD INSTALL --configure-args='--enable-rpath' rjags or, within R > install.packages("rjags", configure.args="--enable-rpath") You may also consider: 5) Set the environment variable LD_LIBRARY_PATH to the directory containing the JAGS library. Unlike options 3) and 4) if you use LD_LIBRARY_PATH then it must be set every time you load the rjags package, not just at compile time. Thus it is best to set this in the file .bash_profile (or the equivalent file if you using another shell). 4 Unix/Linux: legacy configuration ---------------------------------- Legacy configuration is used whenever pkg-config is not installed or otherwise fails to detect the JAGS library. Legacy configuration is deprecated and will be removed in a future version. If you experience problems with installing rjags then it is strongly recommended that you try to get pkg-config working rather than use the options below. The configure script will try to guess the location of the JAGS library. It does this by querying the R and jags executables, so both of these need to be on your PATH for automatic detection to work properly. If you have installed JAGS in a non-standard place then you may need to supply hints to the configure script. This can be done by passing configure arguments or setting environment variables (see below). The options/environment variables correspond to options that were used when JAGS was installed. They are shown in the table below: JAGS configure rjags configure Environmnent default option option variable -------------- --------------- ------------ ------- --prefix --with-jags-prefix JAGS_PREFIX /usr/local --libdir --with-jags-libdir JAGS_LIBDIR ${prefix}/${LIBnn} --includedir --with-jags-includedir JAGS_INCLUDEDIR ${prefix}/include By default LIBnn is the value returned by `R CMD config LIBnn`. This can be overridden by setting the environment variable LIBnn. 4.1 Setting the prefix ---------------------- By default, all JAGS files are installed in subdirectories of /usr/local. This is the prefix of the installation, and it can be changed to any value by supplying the --prefix argument when installing JAGS. - The jags script goes in ${prefix}/bin - The jags executable launched by the script goes in ${prefix}/libexec - The libraries go in ${prefix}/lib - The modules go in ${prefix}/lib/JAGS - The headers go in ${prefix}/include/JAGS Suppose that when you installed JAGS, you used the configure argument '--prefix=/opt' to install JAGS in the directory /opt instead of the default /usr/local. If /opt/bin is on your PATH then the configure script should still automatically detect JAGS for you. Otherwise, you need to supply the configure argument '--with-jags-prefix=/opt' when installing rjags. Alternatively you can set the environment variable JAGS_PREFIX before building rjags: export JAGS_PREFIX=/opt 4.2 Setting the library directory --------------------------------- Suppose you are on a 64-bit system and installed the JAGS library in /usr/local/lib64 instead of the default /usr/local/lib. If R_HOME is /usr/lib64/R then the configure script should automatically look in the right place for the JAGS library. Otherwise, you need to supply the configure argument '--with-jags-libdir=/usr/local/lib64' when installing rjags. Alternatively you can set the environment variable JAGS_LIBDIR before building rjags: export JAGS_LIBDIR=/usr/local/lib64 If the library directory is in the right prefix, but just has a different name (e.g. ${prefix}/lib64 or ${prefix}/lib32 instead of the default ${prefix}/lib), then you can just set the environment variable LIBnn. export LIBnn=lib64 4.3 Setting the include directory --------------------------------- It is unlikely that you will need to set the include directory, as there is no good reason for changing the default. Nevertheless, this option is included for completeness. By default, the header files go into ${prefix}/include, in a sub-directory JAGS. If you have put this subdirectory somewhere other than ${prefix}/include by supplying the --includedir option to configure when you built JAGS, then the corresponding option for rjags is --with-jags-includedir. Alternatively, you may set the environment variable JAGS_INCLUDEDIR. 4.4 Passing configure arguments ------------------------------- Configure options for rjags are passed via the --configure-args argument to R CMD INSTALL: R CMD INSTALL --configure-args='--with-jags...' or, if you are using the install.packages() function from an R session, using the configure.args argument: install.packages("rjags", configure.args="--with-jags...") If configure arguments are set, they always override the corresponding environment variables. rjags/data/0000755000176200001440000000000012611461563012276 5ustar liggesusersrjags/data/LINE.RData0000644000176200001440000000335212611461563013745 0ustar liggesusersrDTr^)-iN@MڴPa|59hgScs+H"ض>݃SV_Y!HQdnZJ镱o`vHv;OKKN胍 )J/=BGh/> ʇ$}3'G rݦ5EvhWJl 'ꝝfbG3VYN =qo"llRY}\fZ{|Iz<զB"INsB@ô`G#7|@8**X&nx^.OpfKtܶ+u{0pYb9$S6vYe7fwxOXH\]Cn{"N@c߇ϰDS7Kh ;!ط{r_rnC0Fy4CB_%DP5䖣]b405a?ièHXMJLtFjjK1DMfG+1:8焯 k 4X'y~/$i \TI/r(IȤCg&lVñ0HIFB/=\闻_Hk TT tͅ[qaB0{my gK"nQmZo{W?UB֏?PG-^}%!%"N;m׬6 h5 cZ^؆&wq06nU1tQVKv'V^vRҲ|I%х]DDs{v+J>?T0󳳗\ PGٚ!CTNFd"X+` SaAfgU7\ (m)  fHqJ23 #`KMss0:j?l/F_ofކ.EҪ8B^kKfN7oVUoӓ?GX 0) { cat("\nMarginalizing over:", paste(paste(names(x$drop.dims), "(", x$drop.dims,")" , sep=""), collapse=","),"\n") } } make.coda.names <- function(basename, dim) { if (all(dim == 1)) { return(basename) } else { ll <- lapply(as.list(dim), function(n) seq(from=1, to=n)) elements <- expand.grid(ll) elt.names <- apply(elements, 1, paste, collapse=",") elt.names <- paste0(basename, "[", elt.names, "]") return(elt.names) } } as.mcmc.list.mcarray <- function(x, ...) { if (is.null(dim(x)) || is.null(names(dim(x)))) { NextMethod() } xdim <- dim(x) ndim <- length(xdim) dn <- names(xdim) which.iter <- which(dn=="iteration") if (length(which.iter) != 1) { stop("Bad iteration dimension in mcarray") } which.chain <- which(dn=="chain") if (length(which.chain) > 1) { stop("Bad chain dimension in mcarray") } niter <- xdim[which.iter] if (length(which.chain) == 0) { perm <- c((1:ndim)[-which.iter], which.iter) y <- matrix(aperm(x, perm), nrow=niter, byrow=TRUE) ans <- mcmc.list(mcmc(y)) } else { nchain <- xdim[which.chain] ans <- vector("list",nchain) len <- prod(xdim[-which.chain]) perm <- c((1:ndim)[-c(which.iter,which.chain)], which.iter, which.chain) y <- aperm(x,perm) for (i in 1:nchain) { ans[[i]] <- mcmc(matrix(y[1:len + (i-1)*len], nrow=niter, byrow=TRUE)) } ans <- mcmc.list(ans) } bugs.name <- attr(x, "varname", exact=TRUE) if (!is.null(bugs.name)) { elt.names <- make.coda.names(bugs.name, xdim[-c(which.iter, which.chain)]) ### Work around bug in coda::varnames<- for (i in 1:nchain) { colnames(ans[[i]]) <-elt.names } } return(ans) } rjags/R/jags.R0000644000176200001440000004733012611462372012643 0ustar liggesusers# R package rjags file R/jags.R # Copyright (C) 2006-2013 Martyn Plummer # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License version # 2 as published by the Free Software Foundation. # # This program 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. # # A copy of the GNU General Public License is available at # http://www.r-project.org/Licenses/ # .quiet.messages <- function(quiet) { .Call("quietMessages", quiet, PACKAGE="rjags") } print.jags <- function(x, ...) { cat("JAGS model:\n\n") model <- x$model() for (i in 1:length(model)) { cat(model[i],"\n",sep="") } data <- x$data() full <- !sapply(lapply(data, is.na), any) if (any(full)) { cat("Fully observed variables:\n", names(data)[full], "\n") } part <- !full & !sapply(lapply(data, is.na), all) if (any(part)) { cat("Partially observed variables:\n", names(data)[part], "\n") } } jags.model <- function(file, data=NULL, inits, n.chains = 1, n.adapt=1000, quiet=FALSE) { if (missing(file)) { stop("Model file name missing") } if (is.character(file)) { modfile <- file ## Check file exists and can be opened in text mode con <- try(file(modfile, "rt")) if (inherits(con, "try-error")) { stop(paste("Cannot open model file \"", modfile, "\"", sep="")) } close(con) ## Need this for print method and for recompile function model.code <- readLines(file, warn=FALSE) } else if (inherits(file, "connection")) { modfile <- tempfile() ## JAGS library requires a physical file, so we need to copy ## the contents of the connection to a temporary file model.code <- readLines(file, warn=FALSE) writeLines(model.code, modfile) } else { stop("'file' must be a character string or connection") } if (quiet) { .quiet.messages(TRUE) on.exit(.quiet.messages(FALSE), add=TRUE) } p <- .Call("make_console", PACKAGE="rjags") .Call("check_model", p, modfile, PACKAGE="rjags") if (!is.character(file)) { unlink(modfile) #Remove temporary copy } varnames <- .Call("get_variable_names", p, PACKAGE="rjags") if (missing(data) || is.null(data)) { data <- list() } else if (is.environment(data)) { ##Get a list of numeric objects from the supplied environment data <- mget(varnames, envir=data, mode="numeric", ifnotfound=list(NULL)) ##Strip null entries data <- data[!sapply(data, is.null)] } else if (is.list(data)) { v <- names(data) if (is.null(v) && length(v) != 0) { stop("data must be a named list") } if (any(nchar(v)==0)) { stop("unnamed variables in data list") } if (any(duplicated(v))) { stop("Duplicated names in data list: ", paste(v[duplicated(v)], collapse=" ")) } relevant.variables <- v %in% varnames data <- data[relevant.variables] unused.variables <- setdiff(v, varnames) for (i in seq(along=unused.variables)) { warning("Unused variable \"", unused.variables[i], "\" in data") } ### Check for data frames df <- which(as.logical(sapply(data, is.data.frame))) for (i in seq(along=df)) { if (all(sapply(data[[df[i]]], is.numeric))) { #Turn numeric data frames into matrices data[[df[i]]] <- as.matrix(data[[df[i]]]) } else { stop("Data frame with non-numeric elements provided as data: ", names(data)[df[i]]) } } } else { stop("data must be a list or environment") } .Call("compile", p, data, as.integer(n.chains), TRUE, PACKAGE="rjags") ### Setting initial values if (!missing(inits) && !is.null(inits)) { checkParameters <- function(inits) { if(!is.list(inits)) { return("inits parameter must be a list") } inames <- names(inits) if (is.null(inames) || any(nchar(inames) == 0)) { return("No variable names supplied for the initial values") } dupinames <- duplicated(inames) if (any(dupinames)) { return(paste("Duplicated initial values for variable(s): ", paste0(unique(inames[dupinames]), collapse = ", ") ) ) } if (any(inames==".RNG.name")) { rngname <- inits[[".RNG.name"]] if (!is.character(rngname) || length(rngname) != 1) { return("Incorrect .RNG.name value") } inits[[".RNG.name"]] <- NULL } ## Strip null initial values, but give a warning null.inits <- sapply(inits, is.null) if (any(null.inits)) { warning(paste("NULL initial value supplied for variable(s) ", paste(inames[null.inits], collapse=", "), sep="")) inits <- inits[!null.inits] } num_vals <- sapply(inits, is.numeric) if (any(!num_vals)) { return(paste("Non-numeric initial values supplied for variable(s) ", paste(inames[!num_vals], collapse=", "), sep="")) } return ("ok") } setParameters <- function(inits, chain) { if (!is.null(inits[[".RNG.name"]])) { .Call("set_rng_name", p, inits[[".RNG.name"]], as.integer(chain), PACKAGE="rjags") inits[[".RNG.name"]] <- NULL } .Call("set_parameters", p, inits, as.integer(chain), PACKAGE="rjags") } init.values <- vector("list", n.chains) if (is.function(inits)) { if (any(names(formals(inits)) == "chain")) { for (i in 1:n.chains) { init.values[[i]] <- inits(chain=i) } } else { for (i in 1:n.chains) { init.values[[i]] <- inits() } } } else if (is.list(inits)) { if ( !is.null(names(inits)) ) { ## Replicate initial values for all chains for (i in 1:n.chains) { init.values[[i]] <- inits } } else { if (length(inits) != n.chains) { stop("Length mismatch between inits and n.chains") } init.values <- inits } } for (i in 1:n.chains) { msg <- checkParameters(init.values[[i]]) if (!identical(msg, "ok")) { stop("Invalid parameters for chain ", i, ":\n", msg); } setParameters(init.values[[i]], i) unused.inits <- setdiff(names(init.values[[i]]), varnames) unused.inits <- setdiff(unused.inits, c(".RNG.seed", ".RNG.state", ".RNG.name")) for (j in seq(along=unused.inits)) { warning("Unused initial value for \"", unused.inits[j], "\" in chain ", i) } } } .Call("initialize", p, PACKAGE="rjags") model.state <- .Call("get_state", p, PACKAGE="rjags") model.data <- .Call("get_data", p, PACKAGE="rjags") model <- list("ptr" = function() {p}, "data" = function() {model.data}, "model" = function() {model.code}, "state" = function(internal=FALSE) { if(!internal) { for(i in 1:n.chains) { model.state[[i]][[".RNG.state"]] <- NULL model.state[[i]][[".RNG.name"]] <- NULL } } return(model.state) }, "nchain" = function() { .Call("get_nchain", p, PACKAGE="rjags") }, "iter" = function() { .Call("get_iter", p, PACKAGE="rjags") }, "sync" = function() { model.state <<- .Call("get_state", p, PACKAGE="rjags") }, "recompile" = function() { ## Clear the console .Call("clear_console", p, PACKAGE="rjags") p <<- .Call("make_console", PACKAGE="rjags") ## Write the model to a temporary file so we can re-read it mf <- tempfile() writeLines(model.code, mf) .Call("check_model", p, mf, PACKAGE="rjags") unlink(mf) ## Re-compile .Call("compile", p, data, n.chains, FALSE, PACKAGE="rjags") ## Re-initialize if (!is.null(model.state)) { if (length(model.state) != n.chains) { stop("Incorrect number of chains in saved state") } for (i in 1:n.chains) { statei <- model.state[[i]] rng <- statei[[".RNG.name"]] if (!is.null(rng)) { .Call("set_rng_name", p, rng, i, PACKAGE="rjags") statei[[".RNG.name"]] <- NULL } .Call("set_parameters", p, statei, i, PACKAGE="rjags") } .Call("initialize", p, PACKAGE="rjags") ## Redo adaptation adapting <- .Call("is_adapting", p, PACKAGE="rjags") if(n.adapt > 0 && adapting) { cat("Adapting\n") .Call("update", p, n.adapt, PACKAGE="rjags") if (!.Call("check_adaptation", p, PACKAGE="rjags")) { warning("Adaptation incomplete"); } } model.state <<- .Call("get_state", p, PACKAGE="rjags") } invisible(NULL) }) class(model) <- "jags" if (n.adapt > 0) { pb <- if(quiet) NULL else getOption("jags.pb") ok <- adapt(model, n.adapt, end.adaptation=FALSE, progress.bar=pb) if (ok) { .Call("adapt_off", p, PACKAGE="rjags") } else { warning("Adaptation incomplete") } } return(model) } parse.varname <- function(varname) { ## Try to parse string of form "a" or "a[n,p:q,r]" where "a" is a ## variable name and n,p,q,r are integers v <- try(parse(text=varname, n=1), silent=TRUE) if (!is.expression(v) || length(v) != 1) return(NULL) v <- v[[1]] if (is.name(v)) { ##Full node array requested return(list(name=deparse(v))) } else if (is.call(v) && identical(deparse(v[[1]]), "[") && length(v) > 2) { ##Subset requested ndim <- length(v) - 2 lower <- upper <- numeric(ndim) if (any(nchar(sapply(v, deparse)) == 0)) { ##We have to catch empty indices here or they will cause trouble ##below return(NULL) } for (i in 1:ndim) { index <- v[[i+2]] if (is.numeric(index)) { ##Single index lower[i] <- upper[i] <- index } else if (is.call(index) && length(index) == 3 && identical(deparse(index[[1]]), ":") && is.numeric(index[[2]]) && is.numeric(index[[3]])) { ##Index range lower[i] <- index[[2]] upper[i] <- index[[3]] } else return(NULL) } if (any(upper < lower)) return (NULL) return(list(name = deparse(v[[2]]), lower=lower, upper=upper)) } return(NULL) } parse.varnames <- function(varnames) { names <- character(length(varnames)) lower <- upper <- vector("list", length(varnames)) for (i in seq(along=varnames)) { y <- parse.varname(varnames[i]) if (is.null(y)) { stop(paste("Invalid variable subset", varnames[i])) } names[i] <- y$name if (!is.null(y$lower)) { lower[[i]] <- y$lower } if (!is.null(y$upper)) { upper[[i]] <- y$upper } } return(list(names=names, lower=lower, upper=upper)) } jags.samples <- function(model, variable.names, n.iter, thin=1, type="trace", ...) { if (class(model) != "jags") stop("Invalid JAGS model") if (!is.character(variable.names) || length(variable.names) == 0) stop("variable.names must be a character vector") if (!is.numeric(n.iter) || length(n.iter) != 1 || n.iter <= 0) stop("n.iter must be a positive integer") if (!is.character(type)) stop("type must be a character vector") pn <- parse.varnames(variable.names) status <- .Call("set_monitors", model$ptr(), pn$names, pn$lower, pn$upper, as.integer(thin), type, PACKAGE="rjags") if (!any(status)) stop("No valid monitors set") update.jags(model, n.iter, ...) ans <- .Call("get_monitored_values", model$ptr(), type, PACKAGE="rjags") for (i in seq(along=ans)) { class(ans[[i]]) <- "mcarray" attr(ans[[i]], "varname") <- names(ans)[i] } for (i in seq(along=variable.names)) { if (status[i]) { .Call("clear_monitor", model$ptr(), pn$names[i], pn$lower[[i]], pn$upper[[i]], type, PACKAGE="rjags") } } return(ans) } list.samplers <- function(object) { if (!inherits(object, "jags")) { stop("not a jags model object") } .Call("get_samplers", object$ptr(), PACKAGE="rjags") } list.factories <- function(type) { type = match.arg(type, c("sampler","monitor","rng")) as.data.frame(.Call("get_factories", type, PACKAGE="rjags")) } set.factory <- function(name, type, state) { if (!is.character(name) || length(name) != 1) stop("invalid name") if (!is.character(type) || length(type) != 1) stop("invalid name") if (length(state) != 1) stop("invalid state") type <- match.arg(type, c("sampler","rng","monitor")) .Call("set_factory_active", name, type, as.logical(state), PACKAGE="rjags") } coda.names <- function(basename, dim) { ## Utility function used to get the names of the individual elements ## of a node array if (prod(dim) == 1) return(basename) ##Default lower and upper limits ndim <- length(dim) lower <- rep(1, ndim) upper <- dim ##If the node name is a subset, we try to parse it to get the ##names of its elements. For example, if basename is "A[2:3]" ##we want to return names "A[2]", "A[3]" not "A[2:3][1]", "A[2:3][2]". pn <- parse.varname(basename) if (!is.null(pn) && !is.null(pn$lower) && !is.null(pn$upper)) { if (length(pn$lower) == length(pn$upper)) { dim2 <- pn$upper - pn$lower + 1 if (isTRUE(all.equal(dim[dim!=1], dim2[dim2!=1], check.attributes=FALSE))) { basename <- pn$name lower <- pn$lower upper <- pn$upper ndim <- length(dim2) } } } indices <- as.character(lower[1]:upper[1]) if (ndim > 1) { for (i in 2:ndim) { indices <- outer(indices, lower[i]:upper[i], FUN=paste, sep=",") } } paste(basename,"[",as.vector(indices),"]",sep="") } nchain <- function(model) { if (!inherits(model, "jags")) stop("Invalid JAGS model object in nchain") .Call("get_nchain", model$ptr(), PACKAGE="rjags") } coda.samples <- function(model, variable.names=NULL, n.iter, thin=1, na.rm = TRUE, ...) { start <- model$iter() + thin out <- jags.samples(model, variable.names, n.iter, thin, type="trace", ...) ans <- vector("list", nchain(model)) for (ch in 1:nchain(model)) { ans.ch <- vector("list", length(out)) vnames.ch <- NULL for (i in seq(along=out)) { varname <- names(out)[[i]] d <- dim(out[[i]]) if (length(d) < 3) { stop("Invalid dimensions for sampled output") } vardim <- d[1:(length(d)-2)] nvar <- prod(vardim) niter <- d[length(d) - 1] nchain <- d[length(d)] values <- as.vector(out[[i]]) var.i <- matrix(NA, nrow=niter, ncol=nvar) for (j in 1:nvar) { var.i[,j] <- values[j + (0:(niter-1))*nvar + (ch-1)*niter*nvar] } vnames.ch <- c(vnames.ch, coda.names(varname, vardim)) ans.ch[[i]] <- var.i } ans.ch <- do.call("cbind", ans.ch) colnames(ans.ch) <- vnames.ch ans[[ch]] <- mcmc(ans.ch, start=start, thin=thin) } if (isTRUE(na.rm)) { ## Drop variables that are missing for all iterations in at least ## one chain all.missing <- sapply(ans, function(x) {apply(is.na(x), 2, any)}) drop.vars <- if (is.matrix(all.missing)) { apply(all.missing, 1, any) } else { any(all.missing) } ans <- lapply(ans, function(x) return(x[, !drop.vars, drop=FALSE])) } mcmc.list(ans) } load.module <- function(name, path, quiet=FALSE) { if (name %in% list.modules()) { ## This is a stop-gap measure as JAGS 2.1.0 does allow you ## to load the same module twice. This should be fixed in ## later versions. return(invisible()) #Module already loaded } if (missing(path)) { path = getOption("jags.moddir") if (is.null(path)) { stop("option jags.moddir is not set") } } if (!is.character(path) || length(path) != 1) stop("invalid path") if (!is.character(name) || length(name) != 1) stop("invalid name") file <- file.path(path, paste(name, .Platform$dynlib.ext, sep="")) if (!file.exists(file)) { stop("File not found: ", file) } if (!isDLLLoaded(file)) { ## We must avoid calling dyn.load twice on the same DLL This ## may result in the DLL being unloaded and then reloaded, ## which will invalidate pointers to the distributions, ## functions and factories in the module. dyn.load(file) } ok <- .Call("load_module", name, PACKAGE="rjags") if (!ok) { stop(paste("module", name, "not found\n")) } else if (!quiet) { message("module ", name, " loaded") } invisible() } unload.module <- function(name, quiet=FALSE) { if (!is.character(name) || length(name) != 1) stop("invalid name") ok <- .Call("unload_module", name, PACKAGE="rjags") if (!ok) { warning(paste("module", name, "not loaded")) } else if (!quiet) { cat("Module", name, "unloaded\n", sep=" ") } invisible() } list.modules <- function() { .Call("get_modules", PACKAGE="rjags"); } isDLLLoaded <- function(file) { dll.list <- getLoadedDLLs() for (i in seq(along=dll.list)) { if (dll.list[[i]]["path"][1] == file) return(TRUE) } return(FALSE) } parallel.seeds <- function(factory, nchain) { .Call("parallel_seeds", factory, nchain, PACKAGE="rjags") } rjags/R/jags.object.R0000644000176200001440000000605112611461731014101 0ustar liggesusers# R package rjags file R/jags.object.R # Copyright (C) 2007-2011 Martyn Plummer # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License version # 2 as published by the Free Software Foundation. # # This program 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. # # A copy of the GNU General Public License is available at # http://www.r-project.org/Licenses/ # update.jags <- function(object, n.iter = 1, by, progress.bar, ...) { if (!is.numeric(n.iter) || n.iter < 1) { stop("Invalid n.iter") } adapting <- .Call("is_adapting", object$ptr(), PACKAGE="rjags") on.exit(object$sync()) if (missing(progress.bar)) { progress.bar <- getOption("jags.pb") } if (!is.null(progress.bar)) { match.arg(progress.bar, c("text","gui","none")) if (progress.bar=="none") progress.bar <- NULL } do.pb <- interactive() && !is.null(progress.bar) && n.iter >= 100 if (do.pb) { start.iter <- object$iter() end.iter <- start.iter + n.iter pb <- switch(progress.bar, "text" = txtProgressBar(start.iter, end.iter, initial = start.iter, style=3, width=50, char=ifelse(adapting,"+","*")), "gui" = updatePB(start.iter, end.iter, adapting)) } ## Set refresh frequency for progress bar if (missing(by) || by <= 0) { ##In JAGS 3.x.y there is a memory reallocation bug when ##monitoring that slows down updates. Drop refresh ##frequency to avoid triggering memory reallocations. ##by <- min(ceiling(n.iter/50), 100) by <- ceiling(n.iter/50) } else { by <- ceiling(by) } ## Do updates n <- n.iter while (n > 0) { .Call("update", object$ptr(), min(n,by), PACKAGE="rjags") if (do.pb) { switch(progress.bar, "text" = setTxtProgressBar(pb, object$iter()), "gui" = setPB(pb, object$iter())) } n <- n - by } if (do.pb) { close(pb) } invisible(NULL) } adapt <- function(object, n.iter, end.adaptation = FALSE, ...) { if(.Call("is_adapting", object$ptr(), PACKAGE="rjags")) { if(n.iter > 0) update.jags(object, n.iter, ...) ok <- .Call("check_adaptation", object$ptr(), PACKAGE="rjags") if (end.adaptation) { .Call("adapt_off", object$ptr(), PACKAGE="rjags") } return(ok) } else { return(TRUE) } } coef.jags <- function(object, chain = 1, ...) { if (!is.numeric(chain) || chain < 1 || chain > object$nchain()) { stop("Invalid chain") } object$state(internal=FALSE)[[chain]] } variable.names.jags <- function(object, ...) { .Call("get_variable_names", object$ptr(), PACKAGE="rjags") } rjags/R/dic.R0000644000176200001440000000642512611461563012457 0ustar liggesusers# R package rjags file R/dic.R # Copyright (C) 2009-2013 Martyn Plummer # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License version # 2 as published by the Free Software Foundation. # # This program 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. # # A copy of the GNU General Public License is available at # http://www.r-project.org/Licenses/ # "dic.samples" <- function(model, n.iter, thin=1, type="pD", ...) { if (nchain(model) == 1) { stop("2 or more parallel chains required") } if (!inherits(model, "jags")) stop("Invalid JAGS model") if (!is.numeric(n.iter) || length(n.iter) != 1 || n.iter <= 0) stop("n.iter must be a positive integer") load.module("dic", quiet=TRUE) limits <- vector("list",2) pdtype <- match.arg(type, c("pD","popt")) status <- .Call("set_monitors", model$ptr(), c("deviance",pdtype), limits, limits, as.integer(thin), "mean", PACKAGE="rjags") if (!any(status)) { stop("Failed to set monitors") } update(model, n.iter = as.integer(n.iter), ...) dev <- .Call("get_monitored_values_flat", model$ptr(), "mean", PACKAGE="rjags") for (i in seq(along=dev)) { class(dev[[i]]) <- "mcarray" } if (status[1]) { .Call("clear_monitor", model$ptr(), "deviance", NULL, NULL, "mean", PACKAGE="rjags") } if (status[2]) { .Call("clear_monitor", model$ptr(), pdtype, NULL, NULL, "mean", PACKAGE="rjags") } ans <- list("deviance" = dev$deviance, "penalty" = dev[[type]], "type" = type) class(ans) <- "dic" return(ans) } "print.dic" <- function(x, digits= max(3, getOption("digits") - 3), ...) { deviance <- sum(x$deviance) cat("Mean deviance: ", format(deviance, digits=digits), "\n") psum <- sum(x[[2]]) cat(names(x)[[2]], format(mean(psum), digits=digits), "\n") cat("Penalized deviance:", format(deviance + psum, digits=digits), "\n") invisible(x) } "-.dic" <- function(e1, e2) { diffdic(e1, e2) } "diffdic" <- function(dic1,dic2) { if (!identical(dic1$type, dic2$type)) { stop("incompatible dic object: different penalty types") } n1 <- names(dic1$deviance) n2 <- names(dic2$deviance) if (!identical(n1, n2)) { ### Try matching names in lexicographic order if(!identical(sort(n1), sort(n2))) { stop("incompatible dic objects: variable names differ") } ### Reset names to order of the first argument ord1 <- order(n1) ord2 <- order(n2) dic2$deviance[ord1] <- dic2$deviance[ord2] dic2$penalty[ord1] <- dic2$penalty[ord2] } delta <- sapply(dic1$deviance, mean) + sapply(dic1$penalty, mean) - sapply(dic2$deviance, mean) - sapply(dic2$penalty, mean) class(delta) <- "diffdic" return(delta) } "print.diffdic" <- function(x, ...) { cat("Difference: ", sum(x), "\n", sep="") cat("Sample standard error: ", sqrt(length(x)) * sd(x), "\n", sep="") invisible(x) } rjags/R/windows/0000755000176200001440000000000012611464432013256 5ustar liggesusersrjags/R/windows/progress.bar.R0000644000176200001440000000167212611461563016020 0ustar liggesusers# R package rjags file R/windows/progress.bar.R # Copyright (C) 2009 Martyn Plummer # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License version # 2 as published by the Free Software Foundation. # # This program 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. # # A copy of the GNU General Public License is available at # http://www.r-project.org/Licenses/ # updatePB <- function(start.iter, end.iter, adapting) { winProgressBar(title=ifelse(adapting, "Adapting","Updating"), label="Iteration 0", min = start.iter, max=end.iter, initial=start.iter) } setPB <- function(pb, iter) { setWinProgressBar(pb, iter, label=paste("Iteration",iter)) } rjags/R/windows/zzz.R0000644000176200001440000001130512611464432014236 0ustar liggesusers.check.jags.home <- function(jags.home, major) { ## Check that folder jags.home actually exists and contains the DLL ## in the appropriate sub-folder. ## Registry entries created by the JAGS instsaller may be invalid ## if the user removes JAGS by manually deleting files rather than ## using the uninstaller. So this function is used to check that ## the installation still exists. if (is.null(jags.home)) return(FALSE) if (!is.vector(jags.home, mode="character") || length(jags.home) != 1) { return(FALSE) } if (!file_test("-d", jags.home)) return(FALSE) bindir <- file.path(jags.home, .Platform$r_arch, "bin") jags.dll <- file.path(bindir, paste("libjags-", major, .Platform$dynlib.ext, sep="")) return(file.exists(jags.dll)) } .findJAGS <- function(hive, major) { ## Returns the registry key corresponding to the latest release of ## JAGS-major.x.y, or NULL if no release is found regkey <- try(readRegistry("SOFTWARE\\JAGS", hive = hive, maxdepth = 2, view="32-bit"), silent = TRUE) if (inherits(regkey, "try-error")) { return(NULL) } keynames <- names(regkey) keynames <- keynames[grep(paste0("^JAGS-", major, "\\."), keynames)] if (length(keynames) == 0) { return(NULL) } else { keynames <- rev(keynames) #Search in reverse order of release number regkey <- regkey[keynames] for (i in seq(along=keynames)) { if(.check.jags.home(regkey[[i]][["InstallDir"]], major)) { return(regkey[i]) } } return(NULL) } } .noJAGS <- function(major) { paste("Failed to locate any version of JAGS version ", major, "\n\n", "The rjags package is just an interface to the JAGS library\n", "Make sure you have installed JAGS-", major, ".x.y.exe (for any x >=0, y>=0) from\n", "http://www.sourceforge.net/projects/mcmc-jags/files\n", sep="") } .onLoad <- function(lib, pkg) { ### First task is to get installation directory of JAGS ## Major version of JAGS library should match major version ## of the rjags package jags.major <- packageVersion(pkg, lib)$major ## Try environment variable first jags.home <- Sys.getenv("JAGS_HOME") if (nchar(jags.home) > 0) { if (!.check.jags.home(jags.home, jags.major)) { stop("The environment variable JAGS_HOME is set to\n", jags.home, "\nbut no JAGS installation can be found there\n") } } else { ## Search the registry. We need to look for both machine-wide and ## user-specific installations key1 <- .findJAGS("HLM", jags.major) key2 <- .findJAGS("HCU", jags.major) if (is.null(key1)) { if (is.null(key2)) { stop(.noJAGS(jags.major)) } else { latest <- key2 } } else if (is.null(key2) || names(key2) < names(key1)) { latest <- key1 } else { latest <- key2 } jags.home <- latest[[1]][["InstallDir"]] } ### Add the JAGS bin to the windows PATH, if not already present path <- Sys.getenv("PATH") split.path <- strsplit(path, .Platform$path.sep)$PATH bindir <- file.path(jags.home, .Platform$r_arch, "bin") if (!any(split.path == bindir)) { path <- paste(bindir, path, sep=.Platform$path.sep) if (!Sys.setenv("PATH"=path)) { stop("Failed to add the rjags bin directory to the PATH:\n", bindir) } } ### Load the rjags dll library.dynam("rjags", pkg, lib) ### Set the module directory, if the option jags.moddir is not already set if (is.null(getOption("jags.moddir"))) { options("jags.moddir" = file.path(jags.home, .Platform$r_arch, "modules")) } ### Check that the module directory actually exists moddir <- getOption("jags.moddir") if (!file.exists(moddir)) { stop(moddir, " not found\n\n", "rjags is looking for the JAGS modules in\n", moddir, "\nbut this folder does not exist\n") } load.module("basemod", quiet=TRUE) load.module("bugs", quiet=TRUE) ### Set progress bar type if (is.null(getOption("jags.pb"))) { options("jags.pb"="text") } } .onAttach <- function(lib, pkg) { packageStartupMessage("Linked to JAGS ", .Call("get_version", PACKAGE="rjags")) packageStartupMessage("Loaded modules: ", paste(list.modules(), collapse=",")) } .onUnload <- function(libpath) { library.dynam.unload("rjags", libpath) } rjags/R/unix/0000755000176200001440000000000012611461563012551 5ustar liggesusersrjags/R/unix/zzz.R.in0000644000176200001440000000140212611461563014133 0ustar liggesusers.onLoad <- function(lib, pkg) { ## Load the rjags wrapper. library.dynam("rjags", pkg, lib, local=FALSE) ## Set the module directory if (is.null(getOption("jags.moddir"))) { options("jags.moddir" = "@JAGS_MODDIR@") } load.module("basemod", quiet=TRUE) load.module("bugs", quiet=TRUE) ## Set progress bar type if (is.null(getOption("jags.pb"))) { options("jags.pb"="text") } } .onAttach <- function(lib, pkg) { packageStartupMessage("Linked to JAGS ", .Call("get_version", PACKAGE="rjags")) packageStartupMessage("Loaded modules: ", paste(list.modules(), collapse=",")) } .onUnload <- function(libpath) { library.dynam.unload("rjags", libpath) } rjags/R/unix/progress.bar.R0000644000176200001440000000172012611461563015303 0ustar liggesusers# R package rjags file R/unix/progress.bar.R # Copyright (C) 2009-2013 Martyn Plummer # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License version # 2 as published by the Free Software Foundation. # # This program 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. # # A copy of the GNU General Public License is available at # http://www.r-project.org/Licenses/ # updatePB <- function(start.iter, end.iter, adapting) { tcltk::tkProgressBar(title=ifelse(adapting, "Adapting","Updating"), label="Iteration 0", min = start.iter, max=end.iter, initial=start.iter) } setPB <- function(pb, iter) { tcltk::setTkProgressBar(pb, iter, label=paste("Iteration",iter)) } rjags/R/read.data.R0000644000176200001440000000231012611461563013530 0ustar liggesusers# R package rjags file R/read.data.R # Copyright (C) 2009-2011 Martyn Plummer # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License version # 2 as published by the Free Software Foundation. # # This program 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. # # A copy of the GNU General Public License is available at # http://www.r-project.org/Licenses/ # read.data <- function(file, format=c("jags","bugs")) { .Deprecated("read.jagsdata", package="rjags") format <- match.arg(format) switch(format, "jags"=read.jagsdata(file), "bugs"=read.bugsdata(file)) } read.jagsdata <- function(file) { e <- new.env() eval(parse(file), e) return(as.list(e)) } read.bugsdata <- function(file) { bugs.dat <- dget(file) for (n in names(bugs.dat)) { if (!is.null(dim(bugs.dat[[n]]))) { dim(bugs.dat[[n]]) <- rev(dim(bugs.dat[[n]])) bugs.dat[[n]] <- aperm(bugs.dat[[n]]) } } return(bugs.dat) } rjags/MD50000644000176200001440000000345412661717157011713 0ustar liggesusers0b3452301b7ee00338b3bc30b018b433 *DESCRIPTION e256cb2da52e175d0cc40289ff92e9c7 *INSTALL de66cc2266d358254b2aab3ae485b96d *NAMESPACE 5fcd1fe183901e91067edf32f69da754 *R/dic.R 1c36106e8fdb63425fcac41d604ce2d9 *R/jags.R 1ed51e4f1e0e39360c280ecdadfe0284 *R/jags.object.R fb121e03013181040bb43241fc7fde5e *R/mcarray.R 77c07ef7017558fbf12288dd45a742b0 *R/read.data.R a4bb7181ba06fab0cdd88b07ef471b8b *R/unix/progress.bar.R 68045d38e4ecad47ce33d8b1cf2e17a0 *R/unix/zzz.R.in 921d90c18a3aac1ff6ce243e187a0fae *R/windows/progress.bar.R dcd59bf5f902704c278ff26d1b79ddb1 *R/windows/zzz.R 857c748215feff397d05cdaf65e7f6c2 *cleanup 87989973e91e5c5eeed4ac234b92979c *configure 70aeb53e32969f93a8b9c20737a4ea32 *configure.ac d41d8cd98f00b204e9800998ecf8427e *configure.win baf8a3492838ff4e0aa0b67b38bdd297 *data/LINE.RData e295c53351091ac56d81803c2e087938 *man/adapt.Rd 25612ace3e2d1be654a4bc6ea7c1e156 *man/coda.samples.Rd 1ec1a6359ba06d662060b4c95b40245d *man/control.Rd 255e93fb2e1fca973dca8b037dc4973d *man/dic.samples.Rd 2273377e0a579d66418c13b52bc3b870 *man/diffdic.Rd 5e0b4a154ed2f8087a8e5836ff8476c9 *man/jags.model.Rd acc639648f722ef4a740a87a6e97d3c6 *man/jags.module.Rd 1452d48e130bbd652b8dacb166647091 *man/jags.object.Rd 5c427dd5774bb2cf5c3e9511d3cea57d *man/jags.samples.Rd 0e6f792e3882b9c359ce6addec9b6031 *man/line.Rd 221b944e4a73614658a0344f5b663619 *man/mcarray.object.Rd e1d4581b80556b14c34f61b804629b93 *man/parallel.Rd fc82322b1e9e147f33cfb2b214975009 *man/read.jagsdata.Rd 9d9b4c1924d2af1664ff825fa5418941 *man/rjags-deprecated.Rd 2957a9323cff4aa7e32d53ae95341e41 *man/rjags-package.Rd 696040753b8b498dbb9a68b0436b8e9c *man/update.Rd 9e833b0dd9f06c7464486c908b71eda5 *src/Makevars.in aa71142021fe413b879ad36bf926e399 *src/Makevars.win 191c72e02425466da05464f6313efd41 *src/jags.cc 934cc6b1e926cfce7dc12e0c597b91f6 *src/parallel.cc rjags/DESCRIPTION0000644000176200001440000000135012661717157013102 0ustar liggesusersPackage: rjags Version: 4-6 Date: 2016-02-19 Title: Bayesian Graphical Models using MCMC Authors@R: c(person("Martyn", "Plummer", role=c("aut","cre"), email="plummerm@iarc.fr"), person("Alexey", "Stukalov", role="ctb"), person("Matt", "Denwood", role="ctb")) Depends: R (>= 2.14.0), coda (>= 0.13) SystemRequirements: JAGS 4.x.y URL: http://mcmc-jags.sourceforge.net Suggests: tcltk Description: Interface to the JAGS MCMC library. 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The two alternative penalized deviance statistics generated by \code{dic.samples} are the deviance information criterion (DIC) and the penalized expected deviance. These are chosen by giving the values ``pD'' and ``popt'' respectively as the \code{type} argument. DIC (Spiegelhalter et al 2002) is calculated by adding the ``effective number of parameters'' (\code{pD}) to the expected deviance. The definition of \code{pD} used by \code{dic.samples} is the one proposed by Plummer (2002) and requires two or more parallel chains in the model. DIC is an approximation to the penalized plug-in deviance, which is used when only a point estimate of the parameters is of interest. The DIC approximation only holds asymptotically when the effective number of parameters is much smaller than the sample size, and the model parameters have a normal posterior distribution. The penalized expected deviance (Plummer 2008) is calculated by adding the optimism (\code{popt}) to the expected deviance. The \code{popt} penalty is at least twice the size of the \code{pD} penalty, and penalizes complex models more severely. } \value{ An object of class ``dic''. This is a list containing the following elements: \item{deviance}{A numeric vector, with one element for each observed stochastic node, containing the mean deviance for that node} \item{penalty}{A numeric vector, with one element for each observed stochastic node, containing an estimate of the contribution towards the penalty} \item{type}{A string identifying the type of penalty: ``pD'' or ``popt''} } \note{ The \code{popt} penalty is estimated by importance weighting, and may be numerically unstable. } \author{Martyn Plummer} \references{ Spiegelhalter, D., N. Best, B. Carlin, and A. van der Linde (2002), Bayesian measures of model complexity and fit (with discussion). \emph{Journal of the Royal Statistical Society Series B} \bold{64}, 583-639. Plummer, M. (2002), Discussion of the paper by Spiegelhalter et al. \emph{Journal of the Royal Statistical Society Series B} \bold{64}, 620. Plummer, M. (2008) Penalized loss functions for Bayesian model comparison. \emph{Biostatistics} doi: 10.1093/biostatistics/kxm049 } \seealso{\code{\link{diffdic}}} \keyword{models} rjags/man/coda.samples.Rd0000644000176200001440000000353412611461563015005 0ustar liggesusers\name{coda.samples} \alias{coda.samples} \title{Generate posterior samples in mcmc.list format} \description{ This is a wrapper function for \code{jags.samples} which sets a trace monitor for all requested nodes, updates the model, and coerces the output to a single \code{mcmc.list} object. } \usage{ coda.samples(model, variable.names, n.iter, thin = 1, na.rm=TRUE, ...) } \arguments{ \item{model}{a jags model object} \item{variable.names}{a character vector giving the names of variables to be monitored} \item{n.iter}{number of iterations to monitor} \item{thin}{thinning interval for monitors} \item{na.rm}{logical flag that indicates whether variables containing missing values should be omitted. See details.} \item{...}{optional arguments that are passed to the update method for jags model objects} } \value{ An \code{mcmc.list} object. } \details{ If \code{na.rm=TRUE} (the default) then elements of a variable that are missing (\code{NA}) for any iteration in at least one chain will be dropped. This argument was added to handle incompletely defined variables. From JAGS version 4.0.0, users may monitor variables that are not completely defined in the BUGS language description of the model, e.g. if \code{y[i]} is defined in a \code{for} loop starting from \code{i=3} then \code{y[1], y[2]} are not defined. The user may still monitor variable \code{y} and the monitored values corresponding to \code{y[1], y[2]} will have value \code{NA} for all iterations in all chains. Most of the functions in the \pkg{coda} package cannot handle missing values so these variables are dropped by default. } \examples{ data(LINE) LINE$recompile() LINE.out <- coda.samples(LINE, c("alpha","beta","sigma"), n.iter=1000) summary(LINE.out) } \author{Martyn Plummer} \seealso{\code{\link{jags.samples}}} \keyword{models} rjags/man/read.jagsdata.Rd0000644000176200001440000000332512611461563015122 0ustar liggesusers\name{read.jagsdata} \alias{read.jagsdata} \alias{read.bugsdata} \title{Read data files for jags models} \description{ Read data for a JAGS model from a file. } \usage{ read.jagsdata(file) read.bugsdata(file) } \arguments{ \item{file}{name of a file containing a text repesentation of the data for a jags model} } \value{ A named list of numeric vectors or arrays. } \details{ The command line interface for JAGS reads data and initial values from a text file. The data format used for jags data files is the same as the R \code{dump} function. Thus the data values can be read into an R session using the \code{source} function, but this will create objects in the global environment. The \code{read.jagsdata} function is a simple wrapper that reads the data into a list instead. OpenBUGS also reads data and initial values from a text file. The format of these files is described as "S-PLUS" format by the OpenBUGS authors. It superficially resembles the format used by the \code{dput} function (and in fact can be parsed by the \code{dget} function). However, in BUGS "S-PLUS" format, arrays are stored in row-major order instead of the column-major order used by R. The \code{read.bugsdata} function reads OpenBUGS "S-PLUS" format files and permutes the elements of arrays so that they appear in the correct order. Either function returns a list which can be used as the \code{data} or \code{inits} argument of \code{jags.model}. } \note{ Earlier versions of the rjags package had a \code{read.data} function which read data in either format, but the function name was ambiguous (There are many data file format in R) so this is now deprecated. } \author{Martyn Plummer} \keyword{file} rjags/man/adapt.Rd0000644000176200001440000000352512611461731013522 0ustar liggesusers\name{adapt} \alias{adapt} \title{Adaptive phase for JAGS models} \description{ Update the model in adaptive mode. } \usage{ adapt(object, n.iter, end.adaptation=FALSE, \ldots) } \arguments{ \item{object}{a \code{jags} model object} \item{n.iter}{length of the adaptive phase} \item{end.adaptation}{logical flag. If \code{TRUE} then adaptive mode will be turned off on exit.} \item{\ldots}{additional arguments to the update method} } \value{ Returns \code{TRUE} if all the samplers in the model have successfully adapted their behaviour to optimum performance and \code{FALSE} otherwise. } \details{ This function is not normally called by the user. It is called by the \code{jags.model} function when the model object is created. When a JAGS model is compiled, it may require an initial sampling phase during which the samplers adapt their behaviour to maximize their efficiency (e.g. a Metropolis-Hastings random walk algorithm may change its step size). The sequence of samples generated during this adaptive phase is not a Markov chain, and therefore may not be used for posterior inference on the model. The \code{adapt} function updates the model for \code{n.iter} iterations in adaptive mode. Then each sampler reports whether it has acheived optimal performance (e.g. whether the rejection rate of a Metropolis-Hasting sampler is close to the theoretical optimum). If any sampler reports failure of this test then \code{adapt} returns \code{FALSE}. If \code{end.adaptation = TRUE}, then adaptive mode is turned off on exit, and further calls to \code{adapt()} do nothing. The model may be maintained in adaptive mode with the default option \code{end.adaptation = FALSE} so that successive calls to \code{adapt()} may be made until adaptation is satisfactory. } \author{Martyn Plummer} \keyword{models} rjags/man/rjags-package.Rd0000644000176200001440000000452012611461563015127 0ustar liggesusers\name{rjags-package} \alias{rjags-package} \alias{rjags} \docType{package} \title{ Bayesian graphical models using MCMC } \description{ The rjags package provides an interface from R to the JAGS library for Bayesian data analysis. JAGS uses Markov Chain Monte Carlo (MCMC) to generate a sequence of dependent samples from the posterior distribution of the parameters. } \details{ JAGS is a clone of BUGS (Bayesian analysis Using Gibbs Sampling). See Lunn et al (2009) for a history of the BUGS project. Note that the rjags package does not include a copy of the JAGS library: you must install this separately. For instructions on downloading JAGS, see the home page at \url{http://mcmc-jags.sourceforge.net}. To fully understand how JAGS works, you need to read the \href{http://sourceforge.net/projects/mcmc-jags/files/Manuals/3.x/jags_user_manual.pdf}{JAGS User Manual}. The manual explains the basics of modelling with JAGS and shows the functions and distributions available in the dialect of the BUGS language used by JAGS. It also describes the command line interface. The \pkg{rjags} package does not use the command line interface but provides equivalent functionality using R functions. Analysis using the \pkg{rjags} package proceeds in steps: \enumerate{ \item Define the model using the BUGS language in a separate file. \item Read in the model file using the \code{jags.model} function. This creates an object of class \dQuote{jags}. \item Update the model using the \code{update} method for \dQuote{jags} objects. This constitutes a \sQuote{burn-in} period. \item Extract samples from the model object using the \code{coda.samples} function. This creates an object of class \dQuote{mcmc.list} which can be used to summarize the posterior distribution. The \pkg{coda} package also provides convergence diagnostics to check that the output is valid for analysis (see Plummer et al 2006). } } \author{ Martyn Plummer } \references{ Lunn D, Spiegelhalter D, Thomas A, Best N. (2009) The BUGS project: Evolution, critique and future directions. \emph{Statistics in Medicine}, \bold{28}:3049-67. Plummer M, Best N, Cowles K, Vines K (2006). CODA: Convergence Diagnosis and Output Analysis for MCMC, \emph{R News}, \bold{6}:7-11. } \keyword{ package } %\examples{ % %} rjags/man/update.Rd0000644000176200001440000000312312611461563013710 0ustar liggesusers\name{update} \alias{update.jags} \title{Update jags models} \description{ Update the Markov chain associated with the model. } \usage{ \method{update}{jags}(object, n.iter=1, by, progress.bar, \ldots) } \arguments{ \item{object}{a \code{jags} model object} \item{n.iter}{number of iterations of the Markov chain to run} \item{by}{refresh frequency for progress bar. See Details} \item{progress.bar}{type of progress bar. Possible values are \code{"text"}, \code{"gui"}, and \code{"none"}. See Details.} \item{\ldots}{additional arguments to the update method (ignored)} } \value{ The \code{update} method for \code{jags} model objects modifies the original object and returns \code{NULL}. } \details{ Since MCMC calculations are typically long, a progress bar is displayed during the call to \code{update}. The type of progress bar is determined by the \code{progress.bar} argument. Type \code{"text"} is displayed on the R console. Type \code{"gui"} is a graphical progress bar in a new window. The progress bar is suppressed if \code{progress.bar} is \code{"none"} or \code{NULL}, if the update is less than 100 iterations, or if R is not running interactively. The default progress bar type is taken from the option \code{jags.pb}. The progress bar is refreshed every \code{by} iterations. The update can only be interrupted when the progress bar is refreshed. Therefore it is advisable not to set \code{by} to a very large value. By default \code{by} is either \code{n.iter/50} or \code{100}, whichever is smaller. } \author{Martyn Plummer} \keyword{models} rjags/man/parallel.Rd0000644000176200001440000000313112611461563014221 0ustar liggesusers\name{parallel} \alias{parallel.seeds} \title{Get initial values for parallel RNGs} \description{ On a multi-processor system, you may wish to run parallel chains using multiple \code{jags.model} objects, each running a single chain on a separate processor. This function returns a list of values that may be used to initialize the random number generator of each chain. } \usage{ parallel.seeds(factory, nchain) } \arguments{ \item{factory}{Name of the RNG factory to use.} \item{nchain}{Number of chains for which to initialize RNGs.} } \value{ \code{parallel.seeds} returns a list of RNG states. Each element is a list of length 2 with the following elements: \item{.RNG.name}{The name of the RNG} \item{.RNG.state}{An integer vector giving the state of the RNG.} } \note{ It is not yet possible to make the results of \code{parallel.seeds} reproducible. This will be fixed in a future version of JAGS. } \examples{ ##The BaseRNG factory generates up to four distinct types of RNG. If ##more than 4 chains are requested, it will recycle the RNG types, but ##use different initial values parallel.seeds("base::BaseRNG", 3) ## The lecuyer module provides the RngStream factory, which allows large ## numbers of independent parallel RNGs to be generated. load.module("lecuyer") list.factories(type="rng") parallel.seeds("lecuyer::RngStream", 5); } \author{Martyn Plummer} \seealso{ \code{\link{jags.model}}, section \dQuote{Random number generators}, for further details on RNG initialization; \code{\link{list.factories}} to find the names of available RNG factories. } \keyword{models} rjags/man/mcarray.object.Rd0000644000176200001440000000322212611461563015331 0ustar liggesusers\name{mcarray.object} \alias{mcarray.object} \alias{print.mcarray} \alias{summary.mcarray} \alias{as.mcmc.list.mcarray} \title{Objects for representing MCMC output} \description{ An \code{mcarray} object is used by the \code{jags.samples} function to represent MCMC output from a JAGS model. It is an array with named dimensions, for which the dimensions "iteration" and "chain" have a special status } \usage{ \method{summary}{mcarray}(object, FUN, \ldots) \method{print}{mcarray}(x, \ldots) \method{as.mcmc.list}{mcarray}(x, \ldots) } \arguments{ \item{object,x}{an \code{mcarray} object} \item{FUN}{a function to be used to generate summary statistics} \item{\ldots}{additional arguments to the call} } \value{ The \code{summary} method for \code{mcarray} objects applies the given function to the array, marginalizing the "chain" and "iteration" dimensions. The \code{print} method applies the summary function with \code{FUN=mean}. The \code{as.mcmc.list} method coerces an \code{mcarray} to an \code{mcmc.list} object so that the diagnostics provided by the \code{coda} package can be applied to the MCMC output it represents. } \details{ The \code{coda} package defines \code{mcmc} objects for representing output from an MCMC sampler, and \code{mcmc.list} for representing output from multiple parallel chains. These objects emphasize the time-series aspect of the MCMC output, but lose the original array structure of the variables they represent. The \code{mcarray} class attempts to rectify this by preserving the dimensions of the original node array defined in the JAGS model. } \author{Martyn Plummer} \keyword{models} rjags/man/jags.object.Rd0000644000176200001440000000262212611461563014622 0ustar liggesusers\name{jags.object} \alias{variable.names.jags} \alias{coef.jags} \alias{list.samplers} \title{Functions for manipulating jags model objects} \description{ A \code{jags} object represents a Bayesian graphical model described using the BUGS language. } \usage{ \method{coef}{jags}(object, chain=1, \ldots) \method{variable.names}{jags}(object, \ldots) list.samplers(object) } \arguments{ \item{object}{a \code{jags} model object} \item{chain}{chain number to query} \item{\ldots}{additional arguments to the call (ignored)} } \value{ The \code{coef} function returns a list with an entry for each Node array that contains an unobserved Node. Elements corresponding to observed Nodes or deterministic Nodes are given missing values. The \code{variable.names} function returns a character vector of names of node arrays used in the model. The \code{list.samplers} function returns a named list with an entry for each Sampler used by the model. Each list element is a character vector containing the names of stochastic Nodes that are updated together in a block. The names of the list elements indicate the sampling methods that are used to update each block. Stochastic nodes that are updated by forward sampling from the prior are not listed. } \examples{ data(LINE) LINE$recompile() coef(LINE) variable.names(LINE) list.samplers(LINE) } \author{Martyn Plummer} \keyword{models} rjags/man/diffdic.Rd0000644000176200001440000000307112611461563014020 0ustar liggesusers\name{diffdic} \alias{diffdic} \title{Differences in penalized deviance} \description{ Compare two models by the difference of two \code{dic} objects. } \usage{ \special{dic1 - dic2} diffdic(dic1, dic2) } \arguments{ \item{dic1, dic2}{Objects inheriting from class ``dic''} } \details{ A \code{diffdic} object represents the difference in penalized deviance between two models. A negative value indicates that \code{dic1} is preferred and vice versa. } \value{ An object of class ``diffdic''. This is a numeric vector with an element for each observed stochastic node in the model. The \code{diffdic} class has its own print method, which will display the sum of the differences, and its sample standard deviation. } \note{ The problem of determining what is a noteworthy difference in DIC (or other penalized deviance) between two models is currently unsolved. Following the results of Ripley (1996) on the Akaike Information Criterion, Plummer (2008) argues that there is no absolute scale for comparison of two penalized deviance statistics, and proposes that the difference should be calibrated with respect to the sample standard deviation of the individual contributions from each observed stochastic node. } \author{Martyn Plummer} \references{ Ripley, B. (1996) \emph{Statistical Pattern Recognition and Neural Networks.} Cambridge University Press. Plummer, M. (2008) Penalized loss functions for Bayesian model comparison. \emph{Biostatistics} doi: 10.1093/biostatistics/kxm049 } \seealso{\code{\link{dic}}} \keyword{models} rjags/man/line.Rd0000644000176200001440000000052412611461563013357 0ustar liggesusers\name{line} \docType{data} \alias{LINE} \title{Linear regression example} \description{ The LINE model is a trivial linear regression model with only 5 observations. It's main use is to allow automated checks of the \pkg{rjags} package. } \format{ A \code{jags.model} object, which must be recompiled before use. } \keyword{datasets} rjags/man/jags.module.Rd0000644000176200001440000000153212611461563014640 0ustar liggesusers\name{jags.module} \alias{load.module} \alias{unload.module} \alias{list.modules} \title{Dynamically load JAGS modules} \description{ A JAGS module is a dynamically loaded library that extends the functionality of JAGS. These functions load and unload JAGS modules and show the names of the currently loaded modules. } \usage{ load.module(name, path, quiet=FALSE) unload.module(name, quiet=FALSE) list.modules() } \arguments{ \item{name}{name of the load module to be loaded} \item{path}{file path to the location of the DLL. If omitted, the option \code{jags.moddir} is used to locate the modules} \item{quiet}{a logical. If \code{TRUE}, no message will be printed about loading the package} } \examples{ list.modules() load.module("glm") list.modules() unload.module("glm") list.modules() } \author{Martyn Plummer} \keyword{interface} rjags/man/control.Rd0000644000176200001440000000266612611461563014121 0ustar liggesusers\name{control} \alias{list.factories} \alias{set.factory} \title{Advanced control over JAGS} \description{ JAGS modules contain factory objects for samplers, monitors, and random number generators for a JAGS model. These functions allow fine-grained control over which factories are active. } \usage{ list.factories(type) set.factory(name, type, state) } \arguments{ \item{name}{name of the factory to set} \item{type}{type of factory to query or set. Possible values are \code{"sampler"}, \code{"monitor"}, or \code{"rng"}} \item{state}{a logical. If \code{TRUE} then the factory will be active, otherwise the factory will become inactive.} } \value{ \code{list.factories} returns a data frame with two columns, the first column shows the names of the factory objects in the currently loaded modules, and the second column is a logical vector indicating whether the corresponding factory is active or not. \code{set.factory} is called to change the future behaviour of factory objects. If a factory is set to inactive then it will be skipped. } \note{ When a module is loaded, all of its factory objects are active. This is also true if a module is unloaded and then reloaded. } \examples{ list.factories("sampler") list.factories("monitor") list.factories("rng") set.factory("base::Slice", "sampler", FALSE) list.factories("sampler") set.factory("base::Slice", "sampler", TRUE) } \author{Martyn Plummer} \keyword{models} rjags/man/rjags-deprecated.Rd0000644000176200001440000000124012611461563015630 0ustar liggesusers\name{rjags-deprecated} \alias{rjags-deprecated} \alias{read.data} \title{Deprecated Functions in the rjags package} \description{ These functions are provided for compatibility with older versions of the rjags package and will soon be defunct. } \usage{ read.data(file, format=c("jags","bugs")) } \arguments{ \item{file}{name of a file containing a text repesentation of the data for a jags model} \item{format}{format of the data} } \details{ \code{read.data} with \code{format="jags"} is a deprecated synonym for \code{\link{read.jagsdata}} and with \code{format="bugs"} is a deprecated synonym for \code{\link{read.bugsdata}}. } \keyword{misc} rjags/man/jags.model.Rd0000644000176200001440000001244712611462760014462 0ustar liggesusers\name{jags.model} \alias{jags.model} \title{Create a JAGS model object} \description{ \code{jags.model} is used to create an object representing a Bayesian graphical model, specified with a BUGS-language description of the prior distribution, and a set of data. } \usage{ jags.model(file, data, inits, n.chains = 1, n.adapt=1000, quiet=FALSE) } \arguments{ \item{file}{the name of the file containing a description of the model in the JAGS dialect of the BUGS language. Alternatively, \code{file} can be a readable text-mode connection, or a complete URL.} \item{data}{a list or environment containing the data. Any numeric objects in \code{data} corresponding to node arrays used in \code{file} are taken to represent the values of observed nodes in the model.} \item{inits}{optional specification of initial values in the form of a list or a function (see \code{Initialization} below). If omitted, initial values will be generated automatically. It is an error to supply an initial value for an observed node.} \item{n.chains}{the number of parallel chains for the model} \item{n.adapt}{the number of iterations for adaptation. See \code{\link{adapt}} for details. If \code{n.adapt = 0} then no adaptation takes place.} \item{quiet}{if \code{TRUE} then messages generated during compilation will be suppressed, as well as the progress bar during adaptation.} } \value{ \code{jags.model} returns an object inheriting from class \code{jags} which can be used to generate dependent samples from the posterior distribution of the parameters An object of class \code{jags} is a list of functions that share a common environment. This environment encapsulates the state of the model, and the functions can be used to query or modify the model state. \item{ptr()}{Returns an external pointer to an object created by the JAGS library} \item{data()}{Returns a list containing the data that define the observed nodes in the model} \item{model()}{Returns a character vector containing the BUGS-language representation of the model} \item{state(internal=FALSE)}{Returns a list of length equal to the number of parallel chains in the model. Each element of the list is itself a list containing the current parameter values in that chain. if \code{internal=TRUE} then the returned lists also include the RNG names (\code{.RNG.name}) and states (\code{.RNG.state}). This is not the user-level interface: use the \code{\link{coef.jags}} method instead.} \item{update(n.iter)}{Updates the model by \code{n.iter} iterations. This is not the user-level interface: use the \code{\link{update.jags}} method instead.} } \section{Initialization}{ There are various ways to specify initial values for a JAGS model. If no initial values are supplied, then they will be generated automatically by JAGS. See the JAGS User Manual for details. Otherwise, the options are as follows: \enumerate{ \item A list of numeric values. Initial values for a single chain may supplied as a named list of numeric values. If there are multiple parallel chains then the same list is re-used for each chain. \item A list of lists. Distinct initial values for each chain may be given as a list of lists. In this case, the list should have the same length as the number of chains in the model. \item A function. A function may be supplied that returns a list of initial values. The function is called repeatedly to generate initial values for each chain. Normally this function should call some random number generating functions so that it returns different values every time it is called. The function should either have no arguments, or have a single argument named \code{chain}. In the latter case, the supplied function is called with the chain number as argument. In this way, initial values may be generated that depend systematically on the chain number. } } \section{Random number generators}{ Each chain in a model has its own random number generator (RNG). RNGs and their initial seed values are assigned automatically when the model is created. The automatic seeds are calculated from the current time. If you wish to make the output from the model reproducible, you may specify the RNGs to be used for each chain, and their starting seeds as part of the \code{inits} argument (see \code{Initialization} above). This is done by supplementing the list of initial parameter values for a given chain with two additional elements named \dQuote{.RNG.name}, and \dQuote{.RNG.seed}: \describe{ \item{\code{.RNG.name}}{a character vector of length 1. The names of the RNGs supplied in the base module are: \itemize{ \item \dQuote{base::Wichmann-Hill} \item \dQuote{base::Marsaglia-Multicarry} \item \dQuote{base::Super-Duper} \item \dQuote{base::Mersenne-Twister} } If the lecuyer module is loaded, it provides \dQuote{lecuyer::RngStream} } \item{\code{.RNG.seed}}{a numeric vector of length 1 containing an integer value.} } Note that it is also possible to specify \dQuote{.RNG.state} rather than \dQuote{.RNG.seed} - see for example the output of \code{\link{parallel.seeds}} } \author{Martyn Plummer} \keyword{models} rjags/man/jags.samples.Rd0000644000176200001440000000226612611461563015024 0ustar liggesusers\name{jags.samples} \alias{jags.samples} \title{Generate posterior samples} \description{ Function to extract random samples from the posterior distribution of the parameters of a \code{jags} model. } \usage{ jags.samples(model, variable.names, n.iter, thin = 1, type="trace", ...) } \arguments{ \item{model}{a jags model object} \item{variable.names}{a character vector giving the names of variables to be monitored} \item{n.iter}{number of iterations to monitor} \item{thin}{thinning interval for monitors} \item{type}{type of monitor} \item{...}{optional arguments passed to the update method for jags model objects} } \details{ The \code{jags.samples} function creates monitors for the given variables, runs the model for \code{n.iter} iterations and returns the monitored samples. } \value{ A list of \code{mcarray} objects, with one element for each element of the \code{variable.names} argument. } \examples{ data(LINE) LINE$recompile() LINE.samples <- jags.samples(LINE, c("alpha","beta","sigma"), n.iter=1000) LINE.samples } \author{Martyn Plummer} \seealso{\code{\link{jags.model}}, \code{\link{coda.samples}}} \keyword{models} rjags/configure.win0000644000176200001440000000000012611461563014053 0ustar liggesusersrjags/cleanup0000755000176200001440000000006012611461563012736 0ustar liggesusers#!/bin/sh rm -f src/Makevars rm -f R/unix/zzz.R