[Depmix-commits] r281 - in pkg/depmix: . R data inst inst/doc man man/.Rd2dvi1150 src
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Jul 8 13:45:11 CEST 2009
Author: ingmarvisser
Date: 2009-07-08 13:45:10 +0200 (Wed, 08 Jul 2009)
New Revision: 281
Added:
pkg/depmix/CHANGES
pkg/depmix/COPYING
pkg/depmix/DESCRIPTION
pkg/depmix/R/
pkg/depmix/R/depmix-internal.R
pkg/depmix/R/depmix.R
pkg/depmix/R/dmm.R
pkg/depmix/R/generate.R
pkg/depmix/R/lca.R
pkg/depmix/R/markovdata.R
pkg/depmix/R/mgdmm.R
pkg/depmix/R/mixdmm.R
pkg/depmix/README
pkg/depmix/data/
pkg/depmix/data/discrimination.rda
pkg/depmix/data/speed.rda
pkg/depmix/inst/
pkg/depmix/inst/CITATION
pkg/depmix/inst/doc/
pkg/depmix/inst/doc/depmix-intro.pdf
pkg/depmix/man/
pkg/depmix/man/.Rd2dvi1150/
pkg/depmix/man/.Rd2dvi1150/Rd2.aux
pkg/depmix/man/.Rd2dvi1150/Rd2.idx
pkg/depmix/man/.Rd2dvi1150/Rd2.ilg
pkg/depmix/man/.Rd2dvi1150/Rd2.ind
pkg/depmix/man/.Rd2dvi1150/Rd2.log
pkg/depmix/man/.Rd2dvi1150/Rd2.out
pkg/depmix/man/.Rd2dvi1150/Rd2.pdf
pkg/depmix/man/.Rd2dvi1150/Rd2.tex
pkg/depmix/man/.Rd2dvi1150/Rd2.toc
pkg/depmix/man/depmix-internal.Rd
pkg/depmix/man/discrimination.Rd
pkg/depmix/man/dmm.Rd
pkg/depmix/man/fitdmm.Rd
pkg/depmix/man/generate.Rd
pkg/depmix/man/markovdata.Rd
pkg/depmix/man/mgdmm.Rd
pkg/depmix/man/mixdmm.Rd
pkg/depmix/man/speed.Rd
pkg/depmix/src/
pkg/depmix/src/depmix.so
pkg/depmix/src/derdist.c
pkg/depmix/src/derdist.h
pkg/depmix/src/globals.h
pkg/depmix/src/hessdist.c
pkg/depmix/src/hessdist.h
pkg/depmix/src/logl.cc
pkg/depmix/src/logl.h
pkg/depmix/src/matrix.cc
pkg/depmix/src/matrix.h
pkg/depmix/src/mdmm.cc
pkg/depmix/src/mdmm.h
pkg/depmix/src/mgdmm.cc
pkg/depmix/src/mgdmm.h
pkg/depmix/src/mmts.cc
pkg/depmix/src/mmts.h
pkg/depmix/src/mts.cc
pkg/depmix/src/mts.h
pkg/depmix/src/npsolfit.h
Log:
added depmix files to depmix subdirectory in pkg directory
Added: pkg/depmix/CHANGES
===================================================================
--- pkg/depmix/CHANGES (rev 0)
+++ pkg/depmix/CHANGES 2009-07-08 11:45:10 UTC (rev 281)
@@ -0,0 +1,33 @@
+Changes from 0.9.0 to 0.9.1
+
+1) Added case replicate-weights to save computational load in fitting
+latent class models on categorical data. Markovdata now has an extra
+argument which can be used to provide the number of replicates for each
+case.
+
+2) Updated references and added a vignette called depmix-intro.
+
+Changes from 0.9.1 to 0.9.2
+
+No changes except the addition that the package is now ORPHANED; this is
+an automatic change in the CRAN repositories because warnings occur.
+
+
+Changes from 0.9.2 to 0.9.3
+
+1) Optimization is now done using the Rdonlp2 package by Tamura (optional
+support for NPSOL also still exists); the major advantage of this is that
+donlp2 handles general linear constraints without problems.
+
+2) Options for optimization with nlm and optim are removed as they were
+functioning suboptimal in some cases and have become unnessary.
+
+
+Changes from 0.9.3 to 0.9.4
+
+1) Minor changes in C++ headers to make them compatible with new g++ versions.
+
+
+Changes from 0.9.4 to 0.9.5
+
+1) Minor changes; fixed some documentation and warning issues.
\ No newline at end of file
Added: pkg/depmix/COPYING
===================================================================
--- pkg/depmix/COPYING (rev 0)
+++ pkg/depmix/COPYING 2009-07-08 11:45:10 UTC (rev 281)
@@ -0,0 +1,340 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 2, June 1991
+
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Property changes on: pkg/depmix/COPYING
___________________________________________________________________
Name: svn:eol-style
+ native
Added: pkg/depmix/DESCRIPTION
===================================================================
--- pkg/depmix/DESCRIPTION (rev 0)
+++ pkg/depmix/DESCRIPTION 2009-07-08 11:45:10 UTC (rev 281)
@@ -0,0 +1,11 @@
+Package: depmix
+Version: 0.9.5
+Date: 2008-08-07
+Title: Dependent Mixture Models
+Author: Ingmar Visser <i.visser at uva.nl>
+Maintainer: Ingmar Visser <i.visser at uva.nl>
+Depends: R (>= 2.6.0), MASS
+Suggests: Rdonlp2
+Description: Fit (multigroup) mixtures of latent Markov models on mixed categorical and continuous (timeseries) data
+License: GPL
+Packaged: Mon Nov 26 10:29:03 2007; ivisser
Property changes on: pkg/depmix/DESCRIPTION
___________________________________________________________________
Name: svn:eol-style
+ native
Added: pkg/depmix/R/depmix-internal.R
===================================================================
--- pkg/depmix/R/depmix-internal.R (rev 0)
+++ pkg/depmix/R/depmix-internal.R 2009-07-08 11:45:10 UTC (rev 281)
@@ -0,0 +1,453 @@
+#
+# DEPMIX INTERNAL FUNCTIONS, NOT TO BE CALLED BY USER
+#
+
+#
+# AUXILIARIES FOR FITDMM
+#
+
+checkSetRecode <- function(dat,dmm,tdcov=0,printlevel=1) {
+
+## create the model to be fitted, changed into class mgd
+ if(class(dmm)[1]=="dmm") {
+ xgmod <- mixdmm(dmm=list(dmm))
+ xgmod <- mgdmm(dmm=xgmod)
+ }
+
+ if(class(dmm)[1]=="mixdmm") xgmod <- mgdmm(dmm=dmm)
+ if(class(dmm)[1]=="mgd") xgmod <- dmm
+
+
+## some correctness checks, far from complete
+ if(xgmod$ng==1) {
+ if(!(class(dat)=="md")) stop("Data should have class md")
+ dat <- list(dat)
+ }
+
+ if(length(dat)!=xgmod$ng) stop("Number of data sets does not match nr of groups.")
+ if(!(length(xgmod$nstates)==xgmod$nrcomp)) stop("Length of nstates and nrcomp do not match.")
+ if(!(length(xgmod$itemtypes)==(length(itemtypes(dat[[1]]))-ncov(dat[[1]])))) stop("Model incompatible with data, different numbers of items.")
+
+ ncov=ncov(dat[[1]])
+ if(tdcov==1 && ncov==0) stop("There are no covariates in the data so these cannot be used.")
+ if(tdcov) if(xgmod$td==FALSE) stop("Model does not specify covariates.")
+
+ if(printlevel>19) print("All checks okay.")
+
+ gc()
+
+## split data and covariates, data may have covariates, even when they're not fitted,
+## so the data allways has to be split
+ if(ncov>0) {
+ nrit=dim(dat[[1]])[2]-ncov
+ itt=rep(1,ncov)
+ dcov=list()
+ datsplit=list()
+ for(i in 1:xgmod$ng) {
+ dcov[[i]]=markovdata(dat=dat[[i]][,(nrit+1):(dim(dat[[i]])[2]),drop=FALSE],itemt=itt,nt=ntimes(dat[[i]]))
+ datsplit[[i]]=markovdata(dat=dat[[i]][,1:nrit,drop=FALSE],itemtypes=itemtypes(dat[[i]])[1:nrit],nt=ntimes(dat[[i]]),replicates=replicates(dat[[i]]))
+ }
+ dat=datsplit
+ if(printlevel>19) print("Data split okay.")
+ }
+
+## check missing data codes
+ xm=-9999
+ for(i in 1:xgmod$ng) {
+ dat[[i]]=replace(dat[[i]],which(is.na(dat[[i]])),xm)
+ if(ncov>0) datsplit[[i]]=replace(datsplit[[i]],which(is.na(datsplit[[i]])),xm)
+ }
+
+ if(printlevel>19) print("Missing data codes set.")
+
+## send td model to C routines
+ if(tdcov) {
+ nitems=length(xgmod$itemtypes)
+ z <- .C("covSetUp",
+ as.integer(xgmod$ng),
+ as.integer(xgmod$nrcomp),
+ as.integer(xgmod$nstates),
+ as.integer(nitems),
+ as.integer(xgmod$itemtypes),
+ as.double(xgmod$pars[(xgmod$npars+1):(2*xgmod$npars)]),
+ as.integer(xm),
+ as.integer(printlevel),
+ PACKAGE="depmix")
+
+ ## ... and set covariates in C
+ mcov <- .C("multiCovSetUp",
+ as.integer(length(dcov)),
+ as.integer(printlevel),
+ PACKAGE="depmix")
+ datset <- list()
+ for(i in 1:xgmod$ng) {
+ catit=0
+ dc=numeric(0)
+ datset[[i]] <- .C("ngCovSetUp",
+ as.integer(i),
+ as.double(t(dcov[[i]])),
+ as.integer(dim(dcov[[i]])[2]),
+ as.integer(itemtypes(dcov[[i]])),
+ as.integer(length(ntimes(dcov[[i]]))),
+ as.integer(ntimes(dcov[[i]])),
+ as.double(replicates(dcov[[i]])),
+ as.integer(xm),
+ as.integer(printlevel),
+ PACKAGE="depmix")
+ }
+ if(printlevel>19) print("Td model set in C")
+ }
+
+ nitems=length(xgmod$itemtypes)
+ pars=xgmod$pars[1:xgmod$npars] # only the normal model pars, tdpars are already set
+
+ if(printlevel>19) print("Preparing to send model to C routines.")
+
+## send model to C routines
+ z <- .C("mixModelSetUp",
+ as.integer(xgmod$ng),
+ as.integer(xgmod$nrcomp),
+ as.integer(xgmod$nstates),
+ as.integer(nitems),
+ as.integer(xgmod$itemtypes),
+ as.double(pars),
+ as.integer(xm),
+ as.integer(printlevel),
+ PACKAGE="depmix")
+
+ if(printlevel>19) print("Model set up finished in C.")
+
+## send data to C routines
+ mdat <- .C("multiDataSetUp",
+ as.integer(length(dat)),
+ as.integer(printlevel),
+ PACKAGE="depmix")
+ datset <- list()
+
+ for(i in 1:xgmod$ng) {
+ attr(dat[[i]],"itemtypes")=replace(itemtypes(dat[[i]]),which(itemtypes(dat[[i]])!="categorical"),1)
+ itt=rep(0,dim(dat[[i]])[2])
+ ## recode itemtypes
+ for(it in 1:dim(dat[[i]])[2]) {
+ if(itemtypes(dat[[i]])[it]=="categorical") itt[it]=length(setdiff(unique(dat[[i]][,it]),xm))
+ else itt[it]=1
+ }
+ attr(dat[[i]],"itemtypes")=itt
+ # recode the data if necessary
+ catit=0
+ dc=numeric(0)
+ for(it in 1:dim(dat[[i]])[2]) {
+ if(itemtypes(dat[[i]])[it]>1) {
+ catit=catit+1
+ datacats=sort(setdiff(unique(dat[[i]][,it]),xm))
+ if(all.equal(datacats,(1:itt[it]))==TRUE) dc[catit]=1
+ else dc[catit]=0
+ }
+ }
+ if(all(dc)) recdat=dat[[i]]
+ else recdat=recode(dat[[i]],xm)
+
+
+ datset[[i]] <- .C("ngDataSetUp",
+ as.integer(i),
+ as.double(t(recdat)),
+ as.integer(dim(dat[[i]])[2]),
+ as.integer(itemtypes(dat[[i]])),
+ as.integer(length(ntimes(dat[[i]]))),
+ as.integer(ntimes(dat[[i]])),
+ as.double(replicates(dat[[i]])),
+ as.integer(xm),
+ as.integer(printlevel),
+ PACKAGE="depmix")
+ }
+
+ if(printlevel>19) print("Data set up finished in C\n")
+
+ return(xgmod)
+}
+
+recode <- function(dat,xm) {
+ recdat=dat
+ nit=length(itemtypes(dat))
+ for(it in 1:nit) {
+ if(itemtypes(dat)[it]>1) {
+ original=sort(setdiff(unique(dat[,it]),xm))
+ normvals=1:itemtypes(dat)[it]
+ for(i in 1:itemtypes(dat)[it]) {
+ recdat[which(dat[,it]==original[i]),it]=normvals[i]
+ }
+ }
+ }
+ recdat
+}
+
+#
+# AUXILIARIES FOR DMM
+#
+
+# function fblo gives lower bounds of parameters for each density
+fblo<-function(x,i,bigB) {
+ if(x > 1) return(rep(0,x)) # multinomial
+ if(x== 1) return(c(-bigB,-bigB)) # normal (mu - mean, sigma - stddev)
+}
+
+# function fbuo gives upper bounds of parameters for each density
+fbuo<-function(x,i,bigB) {
+ ubshift=+bigB
+ if(x > 1) return(rep(1,x)) # multinomial
+ if(x==1) return(c(+bigB,+bigB)) # normal (mu - mean, sigma - stddev)
+}
+
+# function ppar generates random start values of parameters for each density and pastes stvals together
+ppar<-function(x,z) {
+ if(x > 1) {y<-runif(x);y<-y/sum(y)} # multinomial
+ if(x==1) y<-c(rnorm(1), rnorm(1,1,0.1)) # normal (mu - mean, sigma - stddev)
+ return(c(z,y))
+}
+
+#recode itemtypes from characters to magic numbers
+recitt <- function(itemtypes) {
+ itemtypes[itemtypes=="gaussian"|itemtypes=="normal"|itemtypes=="norm"]=1
+ itemtypes=as.numeric(itemtypes)
+ itemtypes
+}
+
+# function pp generates vector with parameter names for each itemtype
+pp<-function(x){
+ if(x > 1) return(paste("p",1:x)) # multinomial
+ if(x== 1) return(c("mean", "stddev")) # normal
+}
+
+# returns the number of parameters for each density
+np <- function(x) {
+ if(x > 1) {
+ return(x)
+ } else {
+ if(x==1) {
+ return(2)
+ } else {
+ stop("Unknown itemtype code in function np.")
+ }
+ }
+}
+
+# convert conpat vector to rows of constraint matrix
+pa2conr <- function(x) {
+ fix=as.logical(x)
+ x=replace(x,list=which(x==1),0)
+ un=setdiff(unique(x),0)
+ y=matrix(0,0,length(x))
+ for(i in un) {
+ z=which(x==i)
+ for(j in 2:length(z)) {
+ k=rep(0,length(x))
+ k[z[1]]=1
+ k[z[j]]=-1
+ y=rbind(y,k)
+ }
+ }
+ pa = list(fix=fix,conr=y)
+ return(pa)
+}
+
+##################################
+# #
+# PARAMETER INDEX GENERATION #
+# #
+##################################
+
+paridx <- function(nstates,itemtypes,mat,idx1=0,idx2=0,it=0,comp=1,group=1) {
+# all denotes all parameters of a component model, ie not for a group, and hence
+# it does not include the mixing proportions
+ MATS=c("mix","trans","obser","init","all")
+ mat = pmatch(mat,MATS)
+ nitems=length(itemtypes)
+ nrcomp=length(nstates)
+ nstates=as.vector(nstates)
+ ## nr of pars per item, the sum of this is the nr of pars per state
+ # for the response parameters
+ lobs=sapply(itemtypes,FUN=np)
+ ## nr of pars per component
+ nrcp = rep(0,nrcomp)
+ for(i in 1:nrcomp) nrcp[i]=nstates[i]*(nstates[i]+1)+sum(lobs)*nstates[i]
+ ngpars=nrcomp+sum(nrcp)
+ mix=1:nrcomp
+ # mix prop pars
+ if(mat==1) if(idx1==0) idx=mix else idx=mix[idx1]
+ # trans pars
+ if(mat==2) {
+ tridx <- matrix(1:(nstates[comp]*nstates[comp]),nstates[comp],nstates[comp],byrow=TRUE)
+ if(idx1==0) idx=c(t(tridx))
+ else { if(idx2==0) {
+ idx=tridx[idx1,]
+ }
+ else idx=tridx[idx1,idx2]
+ }
+ }
+ # obser pars
+ if(mat==3) {
+ en=nstates[comp]*nstates[comp]
+ obidx=matrix((en+1):(nstates[comp]*sum(lobs)+en),nstates[comp],sum(lobs),byrow=TRUE)
+ if(it==0) {
+ if(idx1==0) { idx=c(t(obidx)) }
+ else {
+ if(idx2==0) {
+ idx=obidx[idx1,]
+ }
+ else idx=obidx[idx1,idx2]
+ }
+ } else { # it>0
+ if(nitems>1 && it>1) npprev=sum(sapply(itemtypes[1:(it-1)],FUN=np))
+ else npprev=0
+ if(itemtypes[it]==1) np=2
+ else np=itemtypes[it]
+ if(idx1==0) { #return indices of item parameters for all states
+ idx=c(t(obidx[,(npprev+1):(npprev+np)]))
+ } else {
+ if(idx2==0) idx=obidx[idx1,(npprev+1):(npprev+np)]
+ else idx=obidx[idx1,npprev+idx2]
+ }
+ }
+ }
+ # init pars
+ if(mat==4) {
+ inidx=(nrcp[comp]-nstates[comp]+1):(nrcp[comp])
+ if(idx1==0) { idx=inidx }
+ else idx=inidx[idx1]
+ }
+ if(mat==5) idx=1:nrcp[comp]
+ if(comp>1) idx=idx+sum(nrcp[1:(comp-1)])
+ if(mat!=1) idx=idx+nrcomp
+ if(group>1) idx = idx + (group-1)*ngpars
+ return(idx)
+}
+
+# function fresp generates random deviates for itemtype x, given 'pars', ie the item distribution parameters
+fresp <- function(x,pars) {
+ if(x > 1) resp = which(rmultinom(1,size=1,prob=pars)==1) # multinomial
+ if(x==1) resp = rnorm(1,pars[1],pars[2]) # normal (mu - mean, sigma - stddev)
+ return(resp)
+}
+
+# convert a list of matrices x to a block diagonal matrix
+# shamelessly stolen from package assist
+bdiag <- function (x) {
+ if (is.matrix(x))
+ return(x)
+ if (!is.list(x))
+ stop("dismatched input")
+ n <- length(x)
+ x <- lapply(x, function(y) if (length(y))
+ as.matrix(y)
+ else stop("Zero-length component in x"))
+ d <- array(unlist(lapply(x, dim)), c(2, n))
+ rr <- d[1, ]
+ cc <- d[2, ]
+ rsum <- sum(rr)
+ csum <- sum(cc)
+ out <- array(0, c(rsum, csum))
+ ind <- array(0, c(4, n))
+ rcum <- cumsum(rr)
+ ccum <- cumsum(cc)
+ ind[1, -1] <- rcum[-n]
+ ind[2, ] <- rcum
+ ind[3, -1] <- ccum[-n]
+ ind[4, ] <- ccum
+ imat <- array(1:(rsum * csum), c(rsum, csum))
+ iuse <- apply(ind, 2, function(y, imat) {
+ imat[(y[1] + 1):y[2], (y[3] + 1):y[4]]
+ }, imat = imat)
+ iuse <- as.vector(unlist(iuse))
+ out[iuse] <- unlist(x)
+ out
+}
+
+#
+# STARTING VALUE GENERATION (AUXILIARY) FUNCTIONS
+#
+
+kmstart <- function(dat,dmm) {
+ nst=dmm$nstates
+ kmc=try(kmeans(na.omit(dat[,1:(nitems(dat)-ncov(dat))]),nst)$cluster,silent=TRUE)
+ if(class(kmc)=="try-error") {
+ while(class(kmc)=="try-error") {
+ kmc=try(kmeans(dat[,1:(nitems(dat)-ncov(dat))],nst)$cluster,silent=TRUE)
+ }
+ }
+ st=cl2st(kmc,dat,dmm)
+ st
+}
+
+poststart <- function(dat,dmm) {
+ post=posterior(dat,dmm)
+ st=cl2st(post$states[[1]][,2],dat,dmm)
+ st
+}
+
+cl2st <- function(cluster,dat,dmm) {
+ nst=dmm$nstates
+ stin=numeric(nst)
+ if("lcm"%in%class(dmm) || dmm$nst==1) {
+ sttr=diag(nst)
+ for(i in 1:nst) {
+ stin[i]=length(cluster[which(cluster==i)])
+ }
+ } else {
+ sttr=matrix(0,nst,nst)
+ for(i in 1:ind(dat)) {
+ bg=ifelse(i>1,sum(ntimes(dat)[1:(i-1)])+1,1)
+ en=bg+ntimes(dat)[i]-1
+ # add diagonal elements to make sure that the transition table is complete, which
+ # is not neccessarily so for each case, ie not all transitions necc occur
+ cl1=c(as.numeric(gl(nst,2)),cluster[bg:en])
+ cl2=cl1[-1]
+ cl1=cl1[-length(cl1)]
+ sttr=sttr+matrix(table(cl1,cl2),nst,nst)
+ stin[cluster[bg]]=stin[cluster[bg]]+1
+ # correct for the added transition from nst to the start state
+ sttr[nst,cluster[bg]]=sttr[nst,cluster[bg]]-1
+ }
+ # correct for the added transitions between i and i+1
+ for(i in 1:nst) {
+ sttr[i,i]=sttr[i,i]-ind(dat)
+ if(i<nst) sttr[i,i+1]=sttr[i,i+1]-ind(dat)
+ }
+ sttr=sttr/rowSums(sttr)
+ }
+ if(any(stin==0)) stin=stin+0.05
+ stin=stin/sum(stin)
+ stob=cl2stob(cluster,dat,dmm)
+ st=c(1,t(sttr),stob,stin)
+ st
+}
+
+tr2stin <- function(sttr) {
+ etr=eigen(t(sttr))
+ stin=etr$vectors[,which(etr$values==1)]
+ stin=stin/sum(stin)
+ stin
+}
+
+cl2stob <- function(cluster,dat,dmm) {
+ stob=numeric(0)
+ nst=dmm$nstates
+ for(i in 1:nst) {
+ for(j in 1:length(dmm$itemtypes)) {
+ if(dmm$itemtypes[j]==1) {
+ meanob=mean(dat[which(cluster==i),j])
+ sdob=sd(dat[which(cluster==i),j])
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/depmix -r 281
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