rjags/0000755000176200001440000000000012104157021011351 5ustar liggesusersrjags/MD50000644000176200001440000000353012104157021011662 0ustar liggesuserse8bf2ab00f34b2d52dd33a26068d439a *DESCRIPTION 5c745f67b5390cc4afdf1e6851062918 *NAMESPACE 345ee0e341d915022138a2e044fae26e *R/dic.R 7e6be58c7c1fe3e482ddcd7973cd2cfc *R/jags.R 7c01c181d9b2a0714f8f75b3d0db7319 *R/jags.object.R 1c54a5c5d4e950f0262c30d8e0129f2b *R/mcarray.R 7b40586d83c767e12d1e8a483b204598 *R/read.data.R 8912de1cbd4a215ae0859770ad1e7384 *R/unix/progress.bar.R 0c0c06beefd4a9f3871c7bbded3bc0b6 *R/unix/zzz.R.in 2d4204f44dd6e05a89d114d8dac9d4ac *R/windows/progress.bar.R 5cf0db1a55c61dcd451f1c35e67bae87 *R/windows/zzz.R a72903f073b54477cc1f3ed2e402ba00 *README f97b5ca66dda8fcb7ddf9ee8e88eb55a *aclocal.m4 857c748215feff397d05cdaf65e7f6c2 *cleanup af63b73ab64dcef61b6e85e48511d556 *configure efcf3ff16bb8c587ec1e852459aa054b *configure.ac d41d8cd98f00b204e9800998ecf8427e *configure.win baf8a3492838ff4e0aa0b67b38bdd297 *data/LINE.RData c3b54600423ca00be8933fe14dabee48 *man/adapt.Rd ebad4022e6e49760f1abb9d6f7a6d3a2 *man/coda.samples.Rd 1ec1a6359ba06d662060b4c95b40245d *man/control.Rd 255e93fb2e1fca973dca8b037dc4973d *man/dic.samples.Rd 2273377e0a579d66418c13b52bc3b870 *man/diffdic.Rd e028317aee3b7052f4588865aee6306e *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 939955fd6870cdd4be7cc965138c1995 *src/Makevars.in 2bcb4f62b4cb348febbdaf470dc00517 *src/Makevars.win 64af6f8be200b34fad8213384ab49e03 *src/jags.cc d28e12672f1a07195072079117050bf5 *src/parallel.cc rjags/cleanup0000755000176200001440000000006012103761324012730 0ustar liggesusers#!/bin/sh rm -f src/Makevars rm -f R/unix/zzz.R rjags/configure.ac0000644000176200001440000000730612103761324013653 0ustar liggesusersAC_INIT([rjags], [3]) 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` AC_PREFIX_PROGRAM(jags) #Set optional include path with option --with-jags-include or #environment variable JAGS_INCLUDE AC_ARG_WITH([jags-include], AC_HELP_STRING([--with-jags-include=INCLUDE_PATH], [the location of JAGS header files]), [jags_include_path=$withval]) if test [ -n "$jags_include_path" ] ; then JAGS_INCLUDE=${jags_include_path} else if test [ -z "${JAGS_INCLUDE}" ] ; then if test [ "$prefix" = "NONE" ]; then AC_MSG_ERROR("Location of JAGS headers not defined. 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The user can set the environment variable LIBnn to # override this. if test -z "${LIBnn}"; then LIBnn=`"${R_HOME}/bin/R" CMD config LIBnn` fi #Set optional library path with option --with-jags-lib or #environment variable JAGS_LIBS AC_ARG_WITH([jags-lib], AC_HELP_STRING([--with-jags-lib=LIB_PATH], [the location of the JAGS library]), [jags_lib_path=$withval]) if test [ -n "$jags_lib_path" ] ; then JAGS_LIB=$jags_lib_path else if test [ -z "${JAGS_LIB}" ] ; then if test [ "${prefix}" = "NONE" ] ; then AC_MSG_ERROR("Location of JAGS library not defined. Use configure arg '--with-jags-lib' or environment variable 'JAGS_LIB'") else JAGS_LIB=${prefix}/${LIBnn} fi fi fi # 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. 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"write failure creating $CONFIG_STATUS" "$LINENO" 5 # configure is writing to config.log, and then calls config.status. # config.status does its own redirection, appending to config.log. # Unfortunately, on DOS this fails, as config.log is still kept open # by configure, so config.status won't be able to write to it; its # output is simply discarded. So we exec the FD to /dev/null, # effectively closing config.log, so it can be properly (re)opened and # appended to by config.status. When coming back to configure, we # need to make the FD available again. if test "$no_create" != yes; then ac_cs_success=: ac_config_status_args= test "$silent" = yes && ac_config_status_args="$ac_config_status_args --quiet" exec 5>/dev/null $SHELL $CONFIG_STATUS $ac_config_status_args || ac_cs_success=false exec 5>>config.log # Use ||, not &&, to avoid exiting from the if with $? = 1, which # would make configure fail if this is the last instruction. $ac_cs_success || as_fn_exit 1 fi if test -n "$ac_unrecognized_opts" && test "$enable_option_checking" != no; then { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: unrecognized options: $ac_unrecognized_opts" >&5 $as_echo "$as_me: WARNING: unrecognized options: $ac_unrecognized_opts" >&2;} fi rjags/src/0000755000176200001440000000000012104005550012137 5ustar liggesusersrjags/src/jags.cc0000644000176200001440000004153712104005550013404 0ustar liggesusers#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; /* Workaround length being remapped to Rf_length by the preprocessor */ unsigned long sarray_len(SArray const &s) { return s.length(); } int min2(int a, int b) { return std::min(a,b); } #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 Range makeRange(SEXP lower, SEXP upper) { if (lower == R_NilValue || upper == R_NilValue) { return Range(); } 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 Range r; try { r = Range(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) { Range 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) { Range 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/Makevars.in0000644000176200001440000000011112103761324014240 0ustar liggesusersPKG_CPPFLAGS=-I@JAGS_INCLUDE@ PKG_LIBS=-L@JAGS_LIB@ -ljags @JAGS_RPATH@ rjags/src/Makevars.win0000644000176200001440000000013012103761324014430 0ustar liggesusersPKG_CPPFLAGS=-I"$(JAGS_ROOT)/include" PKG_LIBS=-L"$(JAGS_ROOT)/${R_ARCH}/bin" -ljags-3 rjags/src/parallel.cc0000644000176200001440000000477512104005550014257 0ustar liggesusers/* Utility function to get parallel seeds for execution on a * * 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; #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/R/0000755000176200001440000000000012103771777011575 5ustar liggesusersrjags/R/jags.R0000644000176200001440000004333412103761324012636 0ustar liggesusers.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=sys.frame(sys.parent()), inits, n.chains = 1, n.adapt=1000, quiet=FALSE) { if (missing(file)) { stop("Model file name missing") } if (is.character(file)) { fname <- file file <- try(file(fname, "rt")) if (inherits(file, "try-error")) { stop(paste("Cannot open model file \"", fname, "\"", sep="")) } on.exit(close(file)) } else if (!inherits(file, "connection")) { stop("'file' must be a character string or connection") } ## 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) modfile <- tempfile() writeLines(model.code, modfile) 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") unlink(modfile) varnames <- .Call("get_variable_names", p, PACKAGE="rjags") 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 if (is.null(data)) { data <- list() } 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)) { checkParameters <- function(inits) { if(!is.list(inits)) return (FALSE) inames <- names(inits) if (is.null(inames) || any(nchar(inames) == 0)) return (FALSE) if (any(duplicated(inames))) return (FALSE) if (any(inames==".RNG.name")) { rngname <- inits[[".RNG.name"]] if (!is.character(rngname) || length(rngname) != 1) return (FALSE) inits[[".RNG.name"]] <- NULL } ## Strip null initial values, but give a warning null.inits <- sapply(inits, is.null) if (any(null.inits)) { warning("NULL initial values supplied for variable", paste(inames[null.inits], sep=",")) inits <- inits[!null.inits] } if (!all(sapply(inits, is.numeric))) return (FALSE) return (TRUE) } 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 (checkParameters(inits)) { ## Replicate initial values for all chains for (i in 1:n.chains) { init.values[[i]] <- inits } } else if (!all(sapply(inits, checkParameters))) { stop("Invalid initial values") } else { if (length(inits) != n.chains) { stop("Length mismatch in inits") } init.values <- inits } } for (i in 1:n.chains) { if (!checkParameters(init.values[[i]])) { stop("Invalid parameters for chain ", i) } 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) { ok <- adapt(model, n.adapt, end.adaptation=FALSE) 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$upper, as.integer(thin), type, PACKAGE="rjags") if (!any(status)) stop("No valid monitors set") update(model, n.iter, ...) ans <- .Call("get_monitored_values", model$ptr(), type, PACKAGE="rjags") for (i in seq(along=ans)) { class(ans[[i]]) <- "mcarray" } 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$ptr(), PACKAGE="rjags") } coda.samples <- function(model, variable.names=NULL, n.iter, thin=1, ...) { start <- model$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) } 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/read.data.R0000644000176200001440000000113412103761324013525 0ustar liggesusersread.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/R/jags.object.R0000644000176200001440000000431012103761324014072 0ustar liggesusersupdate.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) { by <- min(ceiling(n.iter/50), 100) } 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")) { update(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$ptr(), PACKAGE="rjags") } rjags/R/dic.R0000644000176200001440000000452012103761324012443 0ustar liggesusers"dic.samples" <- function(model, n.iter, thin=1, type="pD", ...) { if (nchain(model) == 1) { stop("2 or more parallel chains required") } if (!inherits(model, "jags")) stop("Invalid JAGS model") if (!is.numeric(n.iter) || length(n.iter) != 1 || n.iter <= 0) stop("n.iter must be a positive integer") load.module("dic", quiet=TRUE) limits <- vector("list",2) pdtype <- match.arg(type, c("pD","popt")) status <- .Call("set_monitors", model$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$deviance) cat("Mean deviance: ", format(deviance, digits=digits), "\n") psum <- sum(x[[2]]) cat(names(x)[[2]], format(mean(psum), digits=digits), "\n") cat("Penalized deviance:", format(deviance + psum, digits=digits), "\n") invisible(x) } "-.dic" <- function(e1, e2) { diffdic(e1, e2) } "diffdic" <- function(dic1,dic2) { if(!identical(names(dic1$deviance),names(dic2$deviance))) { stop("incompatible dic objects: variable names differ") } if (!identical(dic1$type, dic2$type)) { stop("incompatible dic object: different penalty types") } delta <- sapply(dic1$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/unix/0000755000176200001440000000000012104005550012534 5ustar liggesusersrjags/R/unix/progress.bar.R0000644000176200001440000000053612103761324015301 0ustar liggesusersupdatePB <- 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/unix/zzz.R.in0000644000176200001440000000143012104004320014111 0ustar liggesusers.onLoad <- function(lib, pkg) { ## Load the rjags wrapper. library.dynam("rjags", pkg, lib, local=FALSE) ## Set the module directory moddir <- "@JAGS_MODULES@" if (is.null(getOption("jags.moddir"))) { options("jags.moddir" = 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/mcarray.R0000644000176200001440000000364712103761324013353 0ustar liggesusersprint.mcarray <- function(x, ...) { if (is.null(dim(x)) || is.null(names(dim(x)))) { NextMethod() } print(summary(x, mean)) } summary.mcarray <- function(object, FUN, ...) { if (is.null(dim(object)) || is.null(names(dim(object)))) { NextMethod() } dn <- names(dim(object)) drop.dims <- dn %in% c("iteration","chain") ans <- list("stat"=apply(object, which(!drop.dims), FUN, ...), "drop.dims" = dim(object)[drop.dims]) class(ans) <- "summary.mcarray" return(ans) } print.summary.mcarray <- function(x, ...) { cat("mcarray:\n") print(x$stat,...) if (length(x$drop.dims) > 0) { cat("\nMarginalizing over:", paste(paste(names(x$drop.dims), "(", x$drop.dims,")" , sep=""), collapse=","),"\n") } } 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) x <- matrix(aperm(x, perm), nrow=niter, byrow=TRUE) ans <- mcmc.list(mcmc(x)) } 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) x <- aperm(x,perm) for (i in 1:nchain) { ans[[i]] <- mcmc(matrix(x[1:len + (i-1)*len], nrow=niter, byrow=TRUE)) } ans <- mcmc.list(ans) } return(ans) } rjags/R/windows/0000755000176200001440000000000012104004527013246 5ustar liggesusersrjags/R/windows/zzz.R0000644000176200001440000000574312104004527014237 0ustar liggesusers.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(paste("^JAGS-", major, "\\.", sep=""), 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 (!is.null(regkey[[i]][["InstallDir"]])) { 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, ".0.0.exe or higher 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 ## Try environment variable first jags.home <- Sys.getenv("JAGS_HOME") if (nchar(jags.home)==0) { ## Search the registry. We need to look for both machine-wide and ## user-specific installations jags.major <- 3 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 jags.home to the windows PATH, if not already present bindir <- file.path(jags.home, .Platform$r_arch, "bin") path <- Sys.getenv("PATH") split.path <- strsplit(path, .Platform$path.sep)$PATH if (!any(split.path == bindir)) { path <- paste(bindir, path, sep=.Platform$path.sep) Sys.setenv("PATH"=path) } ### Set the module directory, if the option jags.moddir is not already set if (is.null(getOption("jags.moddir"))) { options("jags.moddir" = file.path(jags.home, .Platform$r_arch, "modules")) } library.dynam("rjags", pkg, lib) 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/windows/progress.bar.R0000644000176200001440000000051012103761324016000 0ustar liggesusersupdatePB <- 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/configure.win0000644000176200001440000000000012103761324014045 0ustar liggesusersrjags/NAMESPACE0000644000176200001440000000142612103761324012601 0ustar liggesusersif(tools:::.OStype() == "windows") { importFrom(utils, readRegistry, winProgressBar, setWinProgressBar) } importFrom(utils, txtProgressBar, setTxtProgressBar) importFrom(stats, variable.names, sd, update) importFrom(coda, as.mcmc.list, as.mcmc, spectrum0) 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/data/0000755000176200001440000000000012103761324012270 5ustar liggesusersrjags/data/LINE.RData0000644000176200001440000000335212103761324013737 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~/$i \TI/r(IȤCg&lVñ0HIFB/=\闻_Hk TT tͅ[qaB0{my gK"nQmZo{W?UB֏?PG-^}%!%"N;m׬6 h5 cZ^؆&wq06nU1tQVKv'V^vRҲ|I%х]DDs{v+J>?T0󳳗\ PGٚ!CTNFd"X+` SaAfgU7\ (m)  fHqJ23 #`KMss0:j?l/F_ofކ.EҪ8B^kKfN7oVUoӓ?GX. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # PKG_PROG_PKG_CONFIG([MIN-VERSION]) # ---------------------------------- AC_DEFUN([PKG_PROG_PKG_CONFIG], [m4_pattern_forbid([^_?PKG_[A-Z_]+$]) m4_pattern_allow([^PKG_CONFIG(_PATH)?$]) AC_ARG_VAR([PKG_CONFIG], [path to pkg-config utility])dnl if test "x$ac_cv_env_PKG_CONFIG_set" != "xset"; then AC_PATH_TOOL([PKG_CONFIG], [pkg-config]) fi if test -n "$PKG_CONFIG"; then _pkg_min_version=m4_default([$1], [0.9.0]) AC_MSG_CHECKING([pkg-config is at least version $_pkg_min_version]) if $PKG_CONFIG --atleast-pkgconfig-version $_pkg_min_version; then AC_MSG_RESULT([yes]) else AC_MSG_RESULT([no]) PKG_CONFIG="" fi fi[]dnl ])# PKG_PROG_PKG_CONFIG # PKG_CHECK_EXISTS(MODULES, [ACTION-IF-FOUND], [ACTION-IF-NOT-FOUND]) # # Check to see whether a particular set of modules exists. Similar # to PKG_CHECK_MODULES(), but does not set variables or print errors. # # # Similar to PKG_CHECK_MODULES, make sure that the first instance of # this or PKG_CHECK_MODULES is called, or make sure to call # PKG_CHECK_EXISTS manually # -------------------------------------------------------------- AC_DEFUN([PKG_CHECK_EXISTS], [AC_REQUIRE([PKG_PROG_PKG_CONFIG])dnl if test -n "$PKG_CONFIG" && \ AC_RUN_LOG([$PKG_CONFIG --exists --print-errors "$1"]); then m4_ifval([$2], [$2], [:]) m4_ifvaln([$3], [else $3])dnl fi]) # _PKG_CONFIG([VARIABLE], [COMMAND], [MODULES]) # --------------------------------------------- m4_define([_PKG_CONFIG], [if test -n "$PKG_CONFIG"; then if test -n "$$1"; then pkg_cv_[]$1="$$1" else PKG_CHECK_EXISTS([$3], [pkg_cv_[]$1=`$PKG_CONFIG --[]$2 "$3" 2>/dev/null`], [pkg_failed=yes]) fi else pkg_failed=untried fi[]dnl ])# _PKG_CONFIG # _PKG_SHORT_ERRORS_SUPPORTED # ----------------------------- AC_DEFUN([_PKG_SHORT_ERRORS_SUPPORTED], [AC_REQUIRE([PKG_PROG_PKG_CONFIG]) if $PKG_CONFIG --atleast-pkgconfig-version 0.20; then _pkg_short_errors_supported=yes else _pkg_short_errors_supported=no fi[]dnl ])# _PKG_SHORT_ERRORS_SUPPORTED # PKG_CHECK_MODULES(VARIABLE-PREFIX, MODULES, [ACTION-IF-FOUND], # [ACTION-IF-NOT-FOUND]) # # # Note that if there is a possibility the first call to # PKG_CHECK_MODULES might not happen, you should be sure to include an # explicit call to PKG_PROG_PKG_CONFIG in your configure.ac # # # -------------------------------------------------------------- AC_DEFUN([PKG_CHECK_MODULES], [AC_REQUIRE([PKG_PROG_PKG_CONFIG])dnl AC_ARG_VAR([$1][_CFLAGS], [C compiler flags for $1, overriding pkg-config])dnl AC_ARG_VAR([$1][_LIBS], [linker flags for $1, overriding pkg-config])dnl pkg_failed=no AC_MSG_CHECKING([for $1]) _PKG_CONFIG([$1][_CFLAGS], [cflags], [$2]) _PKG_CONFIG([$1][_LIBS], [libs], [$2]) if test $pkg_failed = yes; then _PKG_SHORT_ERRORS_SUPPORTED if test $_pkg_short_errors_supported = yes; then $1[]_PKG_ERRORS=`$PKG_CONFIG --short-errors --errors-to-stdout --print-errors "$2"` else $1[]_PKG_ERRORS=`$PKG_CONFIG --errors-to-stdout --print-errors "$2"` fi # Put the nasty error message in config.log where it belongs echo "$$1[]_PKG_ERRORS" >&AS_MESSAGE_LOG_FD ifelse([$4], , [AC_MSG_ERROR(dnl [Package requirements ($2) were not met: $$1_PKG_ERRORS Consider adjusting the PKG_CONFIG_PATH environment variable if you installed software in a non-standard prefix. ])], [AC_MSG_RESULT([no]) $4]) elif test $pkg_failed = untried; then ifelse([$4], , [AC_MSG_FAILURE(dnl [The pkg-config script could not be found or is too old. Make sure it is in your PATH or set the PKG_CONFIG environment variable to the full path to pkg-config. To get pkg-config, see .])], [$4]) else $1[]_CFLAGS=$pkg_cv_[]$1[]_CFLAGS $1[]_LIBS=$pkg_cv_[]$1[]_LIBS AC_MSG_RESULT([yes]) ifelse([$3], , :, [$3]) fi[]dnl ])# PKG_CHECK_MODULES rjags/DESCRIPTION0000644000176200001440000000103612104157021013057 0ustar liggesusersPackage: rjags Version: 3-10 Date: 2013-02-04 Title: Bayesian graphical models using MCMC Authors@R: person("Martyn", "Plummer", role=c("aut","cre"), email="plummerm@iarc.fr") Depends: R (>= 2.14.0), coda (>= 0.13) SystemRequirements: jags (>= 3.0.0) Description: Interface to the JAGS MCMC library License: GPL (== 2) URL: http://mcmc-jags.sourceforge.net Packaged: 2013-02-04 19:26:32 UTC; martyn Author: Martyn Plummer [aut, cre] Maintainer: Martyn Plummer Repository: CRAN Date/Publication: 2013-02-05 11:25:21 rjags/man/0000755000176200001440000000000012104005550012123 5ustar liggesusersrjags/man/parallel.Rd0000644000176200001440000000313112103761324014213 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/jags.model.Rd0000644000176200001440000001215612103761324014451 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=sys.frame(sys.parent()), 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.} } \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.} } } \author{Martyn Plummer} \keyword{models} rjags/man/jags.object.Rd0000644000176200001440000000262212103761324014614 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/jags.samples.Rd0000644000176200001440000000226612103761324015016 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/man/adapt.Rd0000644000176200001440000000351312103761324013514 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 automatically 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}. By default, the \code{adapt()} function turns off adaptive mode on exit. Further calls to \code{adapt()} do nothing. The model may be maintained in adaptive mode with the 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/update.Rd0000644000176200001440000000312311511051760013700 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/dic.samples.Rd0000644000176200001440000000604412103761324014627 0ustar liggesusers\name{dic.samples} \alias{dic} \alias{dic.samples} %- Also NEED an '\alias' for EACH other topic documented here. \title{Generate penalized deviance samples} \description{ Function to extract random samples of the penalized deviance from a \code{jags} model. } \usage{ dic.samples(model, n.iter, thin = 1, type, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{model}{a jags model object} \item{n.iter}{number of iterations to monitor} \item{thin}{thinning interval for monitors} \item{type}{type of penalty to use} \item{...}{optional arguments passed to the update method for jags model objects} } \details{ The \code{dic.samples} function generates penalized deviance statistics for use in model comparison. 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/line.Rd0000644000176200001440000000052412103761324013351 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/coda.samples.Rd0000644000176200001440000000170212103761324014772 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, ...) } \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{...}{optional arguments that are passed to the update method for jags model objects} } \value{ An \code{mcmc.list} object. } \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.Rd0000644000176200001440000000332512103761324015114 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/rjags-package.Rd0000644000176200001440000000452011651537013015124 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/rjags-deprecated.Rd0000644000176200001440000000124012103761324015622 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.module.Rd0000644000176200001440000000153212103761324014632 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/mcarray.object.Rd0000644000176200001440000000322212103761324015323 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/control.Rd0000644000176200001440000000266612103761324014113 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/diffdic.Rd0000644000176200001440000000307112103761324014012 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/README0000644000176200001440000000372512103761324012246 0ustar liggesusersThe 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. Windows ------- At compile time, there is no way for Windows to guess where the JAGS library is installed, so you must supply the location yourself using the make variable JAGS_ROOT, defining it in the file HOME/.R/Makevars.win JAGS_ROOT=c:\Program Files\JAGS\JAGS-3.1.0 Unix ---- The configure script will try to guess the location of the JAGS library installation. It will look in //, where - is the determined from the location of the jags script that launches the command line interface. For example, if this is "/usr/local/bin/jags", then prefix will be "/usr/local" - is derived from the value of "LIBnn", returned by "R CMD config LIBnn". This is particularly important on 64-bit unix, where R is installed by default in /usr/local/lib64 and LIBnn is "lib64" The configure script will also look for JAGS headers in //include/JAGS If the configure script fails to find the JAGS headers or library, you can set the environment variables JAGS_INCLUDE and JAGS_LIB to point to the correct directory. Alternatively, you can use the configure options --with-jags-include=/path/to/jags/headers (default /usr/local/include/JAGS) --with-jags-lib=/path/to/jags/library (default /usr/local/[lib|lib64]) These options are onto the rjags configure script with: "R CMD INSTALL --configure-args='--with-jags...' Exceptionally, you may need to set the location of the JAGS modules. But this will only be necessary if you reset moddir when installing JAGS. This can be done by setting the environment variabel JAGS_MODULES or by using the configure option --with-jags-modules=/path/to/jags/modules (default /usr/local/[lib|lib64]/JAGS/modules-w.y.z) where w.y.z is the version number of JAGS