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jomo1mix <-
function(Y.con, Y.cat, Y.numcat, X=NULL, beta.start=NULL, l1cov.start=NULL, l1cov.prior=NULL, nburn=100, nbetween=100, nimp=5, output=1, out.iter=10) {
if (nimp<2) {
nimp=2
cat("Minimum number of imputations:2. For single imputation using function jomo1mix.MCMCchain\n")
}
if (is.null(X)) X=matrix(1,nrow(Y.cat),1)
if (is.null(beta.start)) beta.start=matrix(0,ncol(X),(ncol(Y.con)+(sum(Y.numcat)-length(Y.numcat))))
if (is.null(l1cov.start)) l1cov.start=diag(1,ncol(beta.start))
if (is.null(l1cov.prior)) l1cov.prior=diag(1,ncol(beta.start))
if (is_tibble(Y.con)) {
Y.con<-data.frame(Y.con)
warning("tibbles not supported. Y.con converted to standard data.frame. ")
}
if (is_tibble(Y.cat)) {
Y.cat<-data.frame(Y.cat)
warning("tibbles not supported. Y.cat converted to standard data.frame. ")
}
if (is_tibble(X)) {
X<-data.frame(X)
warning("tibbles not supported. X converted to standard data.frame. ")
}
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])
}
for (i in 1:ncol(X)) {
if (is.factor(X[,i])) X[,i]<-as.numeric(X[,i])
}
stopifnot(nrow(Y.con)==nrow(X), nrow(beta.start)==ncol(X), ncol(beta.start)==(ncol(Y.con)+(sum(Y.numcat)-length(Y.numcat))),nrow(l1cov.start)==ncol(l1cov.start), nrow(l1cov.start)==ncol(beta.start), nrow(l1cov.prior)==ncol(l1cov.prior),nrow(l1cov.prior)==nrow(l1cov.start))
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]
}
colnamycon<-colnames(Y.con)
colnamycat<-colnames(Y.cat)
colnamx<-colnames(X)
Y.con<-data.matrix(Y.con)
storage.mode(Y.con) <- "numeric"
Y.cat<-data.matrix(Y.cat)
storage.mode(Y.cat) <- "numeric"
X<-data.matrix(X)
storage.mode(X) <- "numeric"
stopifnot(!any(is.na(X)))
Y=cbind(Y.con,Y.cat)
if (any(is.na(Y))) {
if (ncol(Y)==1) {
miss.pat<-matrix(c(0,1),2,1)
n.patterns<-2
} else {
miss.pat<-md.pattern.mice(Y, plot=F)
miss.pat<-miss.pat[,colnames(Y)]
n.patterns<-nrow(miss.pat)-1
}
} else {
miss.pat<-matrix(0,2,ncol(Y))
n.patterns<-nrow(miss.pat)-1
}
miss.pat.id<-rep(0,nrow(Y))
for (i in 1:nrow(Y)) {
k <- 1
flag <- 0
while ((k <= n.patterns) & (flag == 0)) {
if (all(!is.na(Y[i,])==miss.pat[k,1:(ncol(miss.pat))])) {
miss.pat.id[i] <- k
flag <- 1
} else {
k <- k + 1
}
}
}
Yi=cbind(Y.con, matrix(0,nrow(Y.con),(sum(Y.numcat)-length(Y.numcat))))
h=1
for (i in 1:length(Y.numcat)) {
for (j in 1:nrow(Y)) {
if (is.na(Y.cat[j,i])) {
Yi[j,(ncol(Y.con)+h):(ncol(Y.con)+h+Y.numcat[i]-2)]=NA
}
}
h=h+Y.numcat[i]-1
}
if (output!=1) out.iter=nburn+nbetween
imp=matrix(0,nrow(Y)*(nimp+1),ncol(Y)+ncol(X)+2)
imp[1:nrow(Y),1:ncol(Y)]=Y
imp[1:nrow(X), (ncol(Y)+1):(ncol(Y)+ncol(X))]=X
imp[1:nrow(X), (ncol(Y)+ncol(X)+1)]=c(1:nrow(Y))
Yimp=Yi
Yimp2=matrix(Yimp, nrow(Yimp),ncol(Yimp))
imp[(nrow(X)+1):(2*nrow(X)),(ncol(Y)+1):(ncol(Y)+ncol(X))]=X
imp[(nrow(X)+1):(2*nrow(X)), (ncol(Y)+ncol(X)+1)]=c(1:nrow(Y))
imp[(nrow(X)+1):(2*nrow(X)), (ncol(Y)+ncol(X)+2)]=1
betapost<- array(0, dim=c(nrow(beta.start),ncol(beta.start),(nimp-1)))
bpost<-matrix(0,nrow(beta.start),ncol(beta.start))
omegapost<- array(0, dim=c(nrow(l1cov.start),ncol(l1cov.start),(nimp-1)))
opost<-matrix(0,nrow(l1cov.start),ncol(l1cov.start))
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]
.Call("jomo1C", Y, Yimp, Yimp2, Y.cat, X,betait,bpost,covit,opost, nburn, l1cov.prior,Y.numcat, ncol(Y.con),out.iter,0, miss.pat.id, n.patterns, PACKAGE = "jomo")
#betapost[,,1]=bpost
#omegapost[,,1]=opost
bpost<-matrix(0,nrow(beta.start),ncol(beta.start))
opost<-matrix(0,nrow(l1cov.start),ncol(l1cov.start))
imp[(nrow(Y)+1):(2*nrow(Y)),1:ncol(Y.con)]=Yimp2[,1:ncol(Y.con)]
imp[(nrow(Y)+1):(2*nrow(Y)),(ncol(Y.con)+1):ncol(Y)]=Y.cat
if (output==1) cat("First imputation registered.", "\n")
for (i in 2:nimp) {
imp[(i*nrow(X)+1):((i+1)*nrow(X)),(ncol(Y)+1):(ncol(Y)+ncol(X))]=X
imp[(i*nrow(X)+1):((i+1)*nrow(X)), (ncol(Y)+ncol(X)+1)]=c(1:nrow(Y))
imp[(i*nrow(X)+1):((i+1)*nrow(X)), (ncol(Y)+ncol(X)+2)]=i
.Call("jomo1C", Y, Yimp, Yimp2, Y.cat, X,betait,bpost,covit, opost, nbetween, l1cov.prior, Y.numcat, ncol(Y.con),out.iter,0, miss.pat.id, n.patterns, PACKAGE = "jomo")
betapost[,,(i-1)]=bpost
omegapost[,,(i-1)]=opost
bpost<-matrix(0,nrow(beta.start),ncol(beta.start))
opost<-matrix(0,nrow(l1cov.start),ncol(l1cov.start))
imp[(i*nrow(X)+1):((i+1)*nrow(X)),1:ncol(Y.con)]=Yimp2[,1:ncol(Y.con)]
imp[(i*nrow(X)+1):((i+1)*nrow(X)),(ncol(Y.con)+1):ncol(Y)]=Y.cat
if (output==1) cat("Imputation number ", i, "registered", "\n")
}
imp<-data.frame(imp)
for (i in 1:ncol(Y.cat)) {
imp[,(ncol(Y.con)+i)]<-as.factor(imp[,(ncol(Y.con)+i)])
levels(imp[,(ncol(Y.con)+i)])<-previous_levels[[i]]
}
if (is.null(colnamycat)) colnamycat=paste("Ycat", 1:ncol(Y.cat), sep = "")
if (is.null(colnamycon)) colnamycon=paste("Ycon", 1:ncol(Y.con), sep = "")
if (is.null(colnamx)) colnamx=paste("X", 1:ncol(X), sep = "")
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
}
cnamycomp<-c(colnamycon,cnycatcomp)
dimnames(betapost)[1] <- list(colnamx)
dimnames(betapost)[2] <- list(cnamycomp)
dimnames(omegapost)[1] <- list(cnamycomp)
dimnames(omegapost)[2] <- list(cnamycomp)
betapostmean<-data.frame(apply(betapost, c(1,2), mean))
omegapostmean<-data.frame(apply(omegapost, c(1,2), mean))
if (output==1) {
cat("The posterior mean of the fixed effects estimates is:\n")
print(t(betapostmean))
cat("\nThe posterior covariance matrix is:\n")
print(omegapostmean)
}
colnames(imp)<-c(colnamycon,colnamycat,colnamx,"id","Imputation")
return(imp)
}
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