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jomo.lm <-
function(formula, data, beta.start=NULL, l1cov.start=NULL, l1cov.prior=NULL, nburn=1000, nbetween=1000, nimp=5, output=1, out.iter=10) {
cat("This function is beta software. Use carefully and please report any bug to the package mantainer\n")
if (nimp<2) {
nimp=2
cat("Minimum number of imputations:2. For single imputation using function jomo.lm.MCMCchain\n")
}
stopifnot(is.data.frame(data))
if (is_tibble(data)) {
data<-data.frame(data)
warning("tibbles not supported. data converted to standard data.frame. ")
}
if (isTRUE(any(sapply(df, is.character)))) stop("Character variables not allowed in data\n")
stopifnot(any(grepl("~",deparse(formula))))
fit.cr<-lm(formula,data=data, na.action = na.omit)
betaY.start<-coef(fit.cr)
varY.start<-(summary(fit.cr)$sigma)^2
varY.prior<-(summary(fit.cr)$sigma)^2
colnamysub<-all.vars(formula[[2]])
Ysub<-get(colnamysub,pos=data)
Ycov<-data.frame(mget(all.vars(formula[[3]]), envir =as.environment(data)))
terms.sub<-attr(terms(formula), "term.labels")
split.terms<-strsplit(terms.sub,":")
length.sub<-length(terms.sub)
order.sub<-attr(terms(formula), "order")
submod<-matrix(1,4,sum(order.sub))
Y.con<-NULL
Y.cat<-NULL
Y.numcat<-NULL
for (j in 1:ncol(Ycov)) {
if (is.numeric(Ycov[,j])) {
if (is.null(Y.con)) {
Y.con<-data.frame(Ycov[,j,drop=FALSE])
} else {
Y.con<-data.frame(Y.con,Ycov[,j,drop=FALSE])
}
}
if (is.factor(Ycov[,j])) {
if (is.null(Y.cat)) {
Y.cat<-data.frame(Ycov[,j,drop=FALSE])
} else {
Y.cat<-data.frame(Y.cat,Ycov[,j,drop=FALSE])
}
Y.numcat<-cbind(Y.numcat,nlevels(Ycov[,j]))
}
}
h<-1
for ( j in 1:length.sub) {
for ( k in 1:order.sub[j]) {
current.term<-split.terms[[j]][k]
current.term<-sub(".*I\\(","",current.term)
current.term<-sub("\\)","",current.term)
if (grepl("\\^",current.term)) {
submod[3,h]<-as.integer(sub(".*\\^","",current.term))
current.term<-sub("\\^.*","",current.term)
} else {
submod[3,h]<-1
}
if (length(which(colnames(Y.cat)==current.term))!=0) {
submod[1,h]<-which(colnames(Y.cat)==current.term)
submod[2,h]<-2
submod[4,h]<-Y.numcat[submod[1,h]]-1
} else if (length(which(colnames(Y.con)==current.term))!=0) {
submod[1,h]<-which(colnames(Y.con)==current.term)
submod[2,h]<-1
}
h<-h+1
}
}
Y.auxiliary<-data.frame(data[,-c(which(colnames(data)%in%colnames(Y.con)),which(colnames(data)%in%colnames(Y.cat)),which(colnames(data)==colnamysub)), drop=FALSE])
Y.aux.con<-NULL
Y.aux.cat<-NULL
Y.aux.numcat<-NULL
if (ncol(Y.auxiliary)>0) {
for (j in 1:ncol(Y.auxiliary)) {
if (is.numeric(Y.auxiliary[,j])) {
if (is.null(Y.aux.con)) Y.aux.con<-data.frame(Y.auxiliary[,j,drop=FALSE])
else Y.aux.con<-data.frame(Y.aux.con,Y.auxiliary[,j,drop=FALSE])
}
if (is.factor(Y.auxiliary[,j])) {
if (is.null(Y.aux.cat)) Y.aux.cat<-data.frame(Y.auxiliary[,j,drop=FALSE])
else Y.aux.cat<-data.frame(Y.aux.cat,Y.auxiliary[,j,drop=FALSE])
Y.aux.numcat<-cbind(Y.aux.numcat,nlevels(Y.auxiliary[,j]))
}
}
}
X=matrix(1,max(nrow(Y.cat),nrow(Y.con)),1)
if (is.null(beta.start)) beta.start=matrix(0,ncol(X),(max(as.numeric(!is.null(Y.con)),ncol(Y.con))+max(0,(sum(Y.numcat)-length(Y.numcat)))+max(as.numeric(!is.null(Y.aux.con)),ncol(Y.aux.con))+max(0,(sum(Y.aux.numcat)-length(Y.aux.numcat)))))
if (is.null(l1cov.start)) {
l1cov.start=diag(1,ncol(beta.start))
}
if (is.null(l1cov.prior)) l1cov.prior=diag(1,ncol(l1cov.start))
ncolYcon<-rep(NA,4)
ncolYcon[1]=max(as.numeric(!is.null(Y.con)),ncol(Y.con))+max(as.numeric(!is.null(Y.aux.con)),ncol(Y.aux.con))
ncolYcon[2]=max(as.numeric(!is.null(Y.con)),ncol(Y.con))
ncolYcon[3]=ncolYcon[1]+max(0,(sum(Y.numcat)-length(Y.numcat)))
ncolYcon[4]=max(0,ncol(Y.cat))
stopifnot(((!is.null(Y.con))||(!is.null(Y.cat)&!is.null(Y.numcat))))
if (!is.null(Y.cat)) {
isnullcat=0
previous_levels<-list()
Y.cat<-data.frame(Y.cat)
for (i in 1:ncol(Y.cat)) {
Y.cat[,i]<-factor(Y.cat[,i])
previous_levels[[i]]<-levels(Y.cat[,i])
levels(Y.cat[,i])<-1:nlevels(Y.cat[,i])
}
} else {
isnullcat=1
}
if (!is.null(Y.aux.cat)) {
isnullcataux=0
previous_levelsaux<-list()
Y.aux.cat<-data.frame(Y.aux.cat)
for (i in 1:ncol(Y.aux.cat)) {
Y.aux.cat[,i]<-factor(Y.aux.cat[,i])
previous_levelsaux[[i]]<-levels(Y.aux.cat[,i])
levels(Y.aux.cat[,i])<-1:nlevels(Y.aux.cat[,i])
}
} else {
isnullcataux=1
}
stopifnot(nrow(beta.start)==ncol(X), ncol(beta.start)==(ncolYcon[1]+max(0,(sum(Y.numcat)-length(Y.numcat)))+max(0,(sum(Y.aux.numcat)-length(Y.aux.numcat)))))
stopifnot(nrow(l1cov.start)==ncol(l1cov.start),nrow(l1cov.prior)==nrow(l1cov.start),nrow(l1cov.start)==ncol(beta.start))
stopifnot(nrow(l1cov.prior)==ncol(l1cov.prior))
betait=matrix(0,nrow(beta.start),ncol(beta.start))
for (i in 1:nrow(beta.start)) {
for (j in 1:ncol(beta.start)) betait[i,j]=beta.start[i,j]
}
covit=matrix(0,nrow(l1cov.start),ncol(l1cov.start))
for (i in 1:nrow(l1cov.start)) {
for (j in 1:ncol(l1cov.start)) covit[i,j]=l1cov.start[i,j]
}
if (!is.null(Y.con)) {
colnamycon<-colnames(Y.con)
Y.con<-data.matrix(Y.con)
storage.mode(Y.con) <- "numeric"
} else {
colnamycon<-NULL
}
if (isnullcat == 0) {
colnamycat <- colnames(Y.cat)
Y.cat <- data.matrix(Y.cat)
storage.mode(Y.cat) <- "numeric"
cnycatcomp<-rep(NA,(sum(Y.numcat)-length(Y.numcat)))
count=0
for ( j in 1:ncol(Y.cat)) {
for (k in 1:(Y.numcat[j]-1)) {
cnycatcomp[count+k]<-paste(colnamycat[j],k,sep=".")
}
count=count+Y.numcat[j]-1
}
} else {
cnycatcomp<-NULL
}
if (!is.null(Y.aux.con)) {
colnamyauxcon<-colnames(Y.aux.con)
Y.aux.con<-data.matrix(Y.aux.con)
storage.mode(Y.aux.con) <- "numeric"
} else {
colnamyauxcon<-NULL
}
if (isnullcataux == 0) {
colnamyauxcat <- colnames(Y.aux.cat)
Y.aux.cat <- data.matrix(Y.aux.cat)
storage.mode(Y.aux.cat) <- "numeric"
cnyauxcatcomp<-rep(NA,(sum(Y.aux.numcat)-length(Y.aux.numcat)))
count=0
for ( j in 1:ncol(Y.aux.cat)) {
for (k in 1:(Y.aux.numcat[j]-1)) {
cnyauxcatcomp[count+k]<-paste(colnamyauxcat[j],k,sep=".")
}
count=count+Y.aux.numcat[j]-1
}
} else {
cnyauxcatcomp<-NULL
}
colnamx<-colnames(X)
X<-data.matrix(X)
storage.mode(X) <- "numeric"
Y=cbind(Y.con,Y.aux.con,Y.cat, Y.aux.cat)
Yi=cbind(Y.con, Y.aux.con, switch(is.null(Y.cat)+1, matrix(0,nrow(Y),(sum(Y.numcat)-length(Y.numcat))), NULL), switch(is.null(Y.aux.cat)+1, matrix(0,nrow(Y.aux.cat),(sum(Y.aux.numcat)-length(Y.aux.numcat))), NULL))
h=1
if (isnullcat==0) {
for (i in 1:length(Y.numcat)) {
for (j in 1:nrow(Y)) {
if (is.na(Y.cat[j,i])) {
Yi[j,(ncolYcon[1]+h):(ncolYcon[1]+h+Y.numcat[i]-2)]=NA
}
}
h=h+Y.numcat[i]-1
}
}
if (isnullcataux==0) {
for (i in 1:length(Y.aux.numcat)) {
for (j in 1:nrow(Y)) {
if (is.na(Y.aux.cat[j,i])) {
Yi[j,(ncolYcon[1]+h):(ncolYcon[1]+h+Y.aux.numcat[i]-2)]=NA
}
}
h=h+Y.aux.numcat[i]-1
}
}
if (isnullcat==0||isnullcataux==0) {
Y.cat.tot<-cbind(Y.cat,Y.aux.cat)
Y.numcat.tot<-c(Y.numcat, Y.aux.numcat)
} else {
Y.cat.tot=-999
Y.numcat.tot=-999
}
Ysubimp<-Ysub
if (output == 0) out.iter=nburn+nbetween
imp=matrix(0,nrow(Y)*(nimp+1),ncol(Y)+3)
imp[1:nrow(Y),1]=Ysub
imp[1:nrow(Y),2:(1+ncol(Y))]=Y
imp[1:nrow(X), (ncol(Y)+2)]=c(1:nrow(Y))
Yimp=Yi
Yimp2=matrix(Yimp, nrow(Yimp),ncol(Yimp))
imp[(nrow(X)+1):(2*nrow(X)), (ncol(Y)+2)]=c(1:nrow(Y))
imp[(nrow(X)+1):(2*nrow(X)), (ncol(Y)+3)]=1
betapost<- array(0, dim=c(nrow(beta.start),ncol(beta.start),(nimp-1)))
betaYpost<- array(0, dim=c(1,length(betaY.start),(nimp-1)))
bpost<-matrix(0,nrow(beta.start),ncol(beta.start))
bYpost<-matrix(0,1,length(betaY.start))
omegapost<- array(0, dim=c(nrow(l1cov.start),ncol(l1cov.start),(nimp-1)))
opost<-matrix(0,nrow(l1cov.start),ncol(l1cov.start))
varYpost<-rep(0,(nimp-1))
vYpost<-matrix(0,1,1)
meanobs<-colMeans(Yi,na.rm=TRUE)
for (i in 1:nrow(Yi)) for (j in 1:ncol(Yi)) if (is.na(Yimp[i,j])) Yimp2[i,j]=meanobs[j]
for (i in 1:length(Ysubimp)) if (is.na(Ysubimp[i])) Ysubimp[i]=mean(Ysubimp, na.rm = TRUE)
.Call("jomo1smcC", Ysub, Ysubimp, 0, submod, order.sub, Y, Yimp, Yimp2, Y.cat.tot, X, betaY.start, bYpost, betait,bpost, varY.start, vYpost, covit,opost, nburn, varY.prior, l1cov.prior,Y.numcat.tot,1, ncolYcon,out.iter, 0, 0, PACKAGE = "jomo")
#betapost[,,1]=bpost
#omegapost[,,(1)]=opost
bpost<-matrix(0,nrow(beta.start),ncol(beta.start))
bYpost<-matrix(0,1,length(betaY.start))
opost<-matrix(0,nrow(l1cov.start),ncol(l1cov.start))
vYpost<-matrix(0,1,1)
imp[(nrow(Y)+1):(2*nrow(Y)),1]=Ysubimp
if (!is.null(Y.con)|!is.null(Y.aux.con)) {
imp[(nrow(Y)+1):(2*nrow(Y)),2:(1+max(0,ncol(Y.con))+max(0,ncol(Y.aux.con)))]=Yimp2[,1:(max(0,ncol(Y.con))+max(0,ncol(Y.aux.con)))]
}
if (isnullcat==0|isnullcataux==0) {
imp[(nrow(Y)+1):(2*nrow(Y)),(ncolYcon[1]+2):(1+ncol(Y))]=Y.cat.tot
}
if (output > 0) cat("First imputation registered.", "\n")
for (i in 2:nimp) {
#Yimp2=matrix(0, nrow(Yimp),ncol(Yimp))
imp[(i*nrow(X)+1):((i+1)*nrow(X)), (ncol(Y)+2)]=c(1:nrow(Y))
imp[(i*nrow(X)+1):((i+1)*nrow(X)), (ncol(Y)+3)]=i
.Call("jomo1smcC", Ysub, Ysubimp, 0, submod, order.sub, Y, Yimp, Yimp2, Y.cat.tot, X, betaY.start, bYpost, betait,bpost, varY.start, vYpost, covit,opost, nbetween, varY.prior, l1cov.prior,Y.numcat.tot,1, ncolYcon,out.iter, 0, 0, PACKAGE = "jomo")
betapost[,,(i-1)]=bpost
betaYpost[,,(i-1)]=bYpost
omegapost[,,(i-1)]=opost
varYpost[i-1]=vYpost
bpost<-matrix(0,nrow(beta.start),ncol(beta.start))
bYpost<-matrix(0,1,length(betaY.start))
opost<-matrix(0,nrow(l1cov.start),ncol(l1cov.start))
vYpost<-matrix(0,1,1)
imp[(i*nrow(X)+1):((i+1)*nrow(X)),1]=Ysubimp
if (!is.null(Y.con)|!is.null(Y.aux.con)) {
imp[(i*nrow(X)+1):((i+1)*nrow(X)),2:(1+max(0,ncol(Y.con))+max(0,ncol(Y.aux.con)))]=Yimp2[,1:(max(0,ncol(Y.con))+max(0,ncol(Y.aux.con)))]
}
if (isnullcat==0||isnullcataux==0) {
imp[(i*nrow(X)+1):((i+1)*nrow(X)),(ncolYcon[1]+2):(1+ncol(Y))]=Y.cat.tot
}
if (output > 0) cat("Imputation number ", i, "registered", "\n")
}
cnamycomp<-c(colnamycon, colnamyauxcon, cnycatcomp, cnyauxcatcomp)
dimnames(betapost)[1] <- list("(Intercept)")
dimnames(betapost)[2] <- list(cnamycomp)
dimnames(omegapost)[1] <- list(cnamycomp)
dimnames(omegapost)[2] <- list(cnamycomp)
betaYpostmean <- apply(betaYpost, c(1, 2), mean)
varYpostmean <- mean(varYpost)
betapostmean <- apply(betapost, c(1, 2), mean)
omegapostmean <- apply(omegapost, c(1, 2), mean)
colnames(betaYpostmean)<-names(fit.cr$coefficients)
rownames(betaYpostmean)<-colnamysub
if (output > 0) {
cat("The posterior mean of the substantive model fixed effects estimates is:\n")
print(betaYpostmean)
cat("The posterior mean of the substantive model residual variance is:\n")
print(varYpostmean)
if ( output == 2 ) {
cat("The posterior mean of the fixed effects estimates is:\n")
print(betapostmean)
cat("The posterior mean of the level 1 covariance matrix is:\n")
print(omegapostmean)
}
}
imp<-data.frame(imp)
if (isnullcat==0) {
for (i in 1:ncol(Y.cat)) {
imp[,(1+ncolYcon[1]+i)]<-as.factor(imp[,(1+ncolYcon[1]+i)])
levels(imp[,(1+ncolYcon[1]+i)])<-previous_levels[[i]]
}
}
if (isnullcataux==0) {
for (i in 1:ncol(Y.aux.cat)) {
imp[,(1+ncolYcon[1]+ncolYcon[4]+i)]<-as.factor(imp[,(1+ncolYcon[1]+ncolYcon[4]+i)])
levels(imp[,(1+ncolYcon[1]+ncolYcon[4]+i)])<-previous_levelsaux[[i]]
}
}
if (ncolYcon[1]>0) {
for (j in 1:(ncolYcon[1])) {
imp[,j+1]=as.numeric(imp[,j+1])
}
}
if (isnullcat==0) {
if (is.null(colnamycat)) colnamycat=paste("Ycat", 1:ncol(Y.cat), sep = "")
} else {
colnamycat=NULL
Y.cat=NULL
Y.numcat=NULL
}
if (isnullcataux==0) {
if (is.null(colnamyauxcat)) colnamyauxcat=paste("Ycat.aux", 1:ncol(Y.aux.cat), sep = "")
} else {
colnamyauxcat=NULL
Y.aux.cat=NULL
Y.aux.numcat=NULL
}
if (!is.null(Y.con)) {
if (is.null(colnamycon)) colnamycon=paste("Ycon", 1:ncol(Y.con), sep = "")
} else {
colnamycon=NULL
}
if (!is.null(Y.aux.con)) {
if (is.null(colnamyauxcon)) colnamyauxcon=paste("Ycon.aux", 1:ncol(Y.aux.con), sep = "")
} else {
colnamyauxcon=NULL
}
if (is.null(colnamysub)) colnamysub="Ysub"
colnames(imp)<-c(colnamysub,colnamycon,colnamyauxcon,colnamycat,colnamyauxcat,"id","Imputation")
return(imp)
}
|
