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jomo1ranconhr <-
function(Y, X=NULL, Z=NULL, clus, beta.start=NULL, u.start=NULL, l1cov.start=NULL, l2cov.start=NULL, l1cov.prior=NULL, l2cov.prior=NULL, nburn=1000, nbetween=1000, nimp=5, a=(ncol(Y)+50),a.prior=NULL, meth="random", output=1, out.iter=10) {
if (nimp<2) {
nimp=2
cat("Minimum number of imputations:2. For single imputation using function jomo1ranconhr.MCMCchain\n")
}
if (is.null(X)) X=matrix(1,nrow(Y),1)
if (is.null(Z)) Z=matrix(1,nrow(Y),1)
if (is.null(beta.start)) beta.start=matrix(0,ncol(X),ncol(Y))
if (is.null(l1cov.prior)) l1cov.prior=diag(1,ncol(beta.start))
if (is.null(a.prior)) a.prior=ncol(beta.start)
if (is_tibble(Y)) {
Y<-data.frame(Y)
warning("tibbles not supported. Y converted to standard data.frame. ")
}
if (is_tibble(X)) {
X<-data.frame(X)
warning("tibbles not supported. X converted to standard data.frame. ")
}
if (is_tibble(Z)) {
Z<-data.frame(Z)
warning("tibbles not supported. Z converted to standard data.frame. ")
}
clus<-factor(unlist(clus))
previous_levels_clus<-levels(clus)
levels(clus)<-0:(nlevels(clus)-1)
if (is.null(u.start)) u.start = matrix(0, nlevels(clus), ncol(Z)*ncol(Y))
if (is.null(l2cov.start)) l2cov.start = diag(1, ncol(u.start))
if (is.null(l2cov.prior)) l2cov.prior = diag(1, ncol(l2cov.start))
if (is.null(l1cov.start)) l1cov.start=matrix(diag(1,ncol(beta.start)),ncol(beta.start)*nlevels(clus),ncol(beta.start),2)
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
}
}
}
for (i in 1:ncol(X)) {
if (is.factor(X[,i])) X[,i]<-as.numeric(X[,i])
}
for (i in 1:ncol(Z)) {
if (is.factor(Z[,i])) Z[,i]<-as.numeric(Z[,i])
}
stopifnot((meth=="fixed"|meth=="random"),nrow(Y)==nrow(clus),nrow(Y)==nrow(X), nrow(beta.start)==ncol(X), ncol(beta.start)==ncol(Y),nrow(l1cov.start)==nrow(u.start)*ncol(l1cov.start), nrow(l1cov.start)==nrow(u.start)*ncol(Y), nrow(l1cov.prior)==ncol(l1cov.prior),nrow(l1cov.start)==nrow(u.start)*nrow(l1cov.prior), nrow(Z)==nrow(Y), ncol(l2cov.start)==ncol(u.start), ncol(u.start)==ncol(Z)*ncol(Y))
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]
}
uit=matrix(0,nrow(u.start),ncol(u.start))
for (i in 1:nrow(u.start)) {
for (j in 1:ncol(u.start)) uit[i,j]=u.start[i,j]
}
covuit=matrix(0,nrow(l2cov.start),ncol(l2cov.start))
for (i in 1:nrow(l2cov.start)) {
for (j in 1:ncol(l2cov.start)) covuit[i,j]=l2cov.start[i,j]
}
ait=as.numeric(a)
colnamy<-colnames(Y)
colnamx<-colnames(X)
colnamz<-colnames(Z)
Y<-data.matrix(Y)
storage.mode(Y) <- "numeric"
X<-data.matrix(X)
storage.mode(X) <- "numeric"
stopifnot(!any(is.na(X)))
Z<-data.matrix(Z)
storage.mode(Z) <- "numeric"
stopifnot(!any(is.na(Z)))
clus <- matrix(as.integer(levels(clus))[clus], ncol=1)
if (output!=1) out.iter=nburn+nbetween
imp=matrix(0,nrow(Y)*(nimp+1),ncol(Y)+ncol(X)+ncol(Z)+3)
imp[1:nrow(Y),1:ncol(Y)]=Y
imp[1:nrow(X), (ncol(Y)+1):(ncol(Y)+ncol(X))]=X
imp[1:nrow(Z), (ncol(Y)+ncol(X)+1):(ncol(Y)+ncol(X)+ncol(Z))]=Z
imp[1:nrow(clus), (ncol(Y)+ncol(X)+ncol(Z)+1)]=clus
imp[1:nrow(X), (ncol(Y)+ncol(X)+ncol(Z)+2)]=c(1:nrow(Y))
Yimp=Y
Yimp2=matrix(Yimp, nrow(Y),ncol(Y))
imp[(nrow(X)+1):(2*nrow(X)),(ncol(Y)+1):(ncol(Y)+ncol(X))]=X
imp[(nrow(Z)+1):(2*nrow(Z)), (ncol(Y)+ncol(X)+1):(ncol(Y)+ncol(X)+ncol(Z))]=Z
imp[(nrow(clus)+1):(2*nrow(clus)), (ncol(Y)+ncol(X)+ncol(Z)+1)]=clus
imp[(nrow(X)+1):(2*nrow(X)), (ncol(Y)+ncol(X)+ncol(Z)+2)]=c(1:nrow(Y))
imp[(nrow(X)+1):(2*nrow(X)), (ncol(Y)+ncol(X)+ncol(Z)+3)]=1
betapost<- array(0, dim=c(nrow(beta.start),ncol(beta.start),(nimp-1)))
bpost<-matrix(0,nrow(beta.start),ncol(beta.start))
upost<-matrix(0,nrow(u.start),ncol(u.start))
upostall<-array(0, dim=c(nrow(u.start),ncol(u.start),(nimp-1)))
omegapost<- array(0, dim=c(nrow(l1cov.start),ncol(l1cov.start),(nimp-1)))
opost<-matrix(0,nrow(l1cov.start),ncol(l1cov.start))
covupost<- array(0, dim=c(nrow(l2cov.start),ncol(l2cov.start),(nimp-1)))
cpost<-matrix(0,nrow(l2cov.start),ncol(l2cov.start))
meanobs<-colMeans(Y,na.rm=TRUE)
for (i in 1:nrow(Y)) for (j in 1:ncol(Y)) if (is.na(Yimp[i,j])) Yimp2[i,j]=rnorm(1,meanobs[j],1)
#for (i in 1:nrow(Y)) for (j in 1:ncol(Y)) if (is.na(Yimp[i,j])) Yimp[i,j]=rnorm(1,mean=meanobs[j], sd=0.01)
Y.cat<-Y.numcat<-(-999)
if (meth=="fixed") {
fixed=1
} else {
fixed=0
}
.Call("jomo1ranhrC", Y, Yimp, Yimp2, Y.cat, X, Z, clus,betait,uit,bpost,upost,covit,opost, covuit,cpost,nburn, l1cov.prior,l2cov.prior,Y.numcat, ncol(Y),ait, a.prior, out.iter, fixed, 0, miss.pat.id, n.patterns, PACKAGE = "jomo")
#betapost[,,1]=bpost
#upostall[,,1]=upost
#omegapost[,,(1)]=opost
#covupost[,,(1)]=cpost
bpost<-matrix(0,nrow(beta.start),ncol(beta.start))
opost<-matrix(0,nrow(l1cov.start),ncol(l1cov.start))
upost<-matrix(0,nrow(u.start),ncol(u.start))
cpost<-matrix(0,nrow(l2cov.start),ncol(l2cov.start))
imp[(nrow(Y)+1):(2*nrow(Y)),1:ncol(Y)]=Yimp2
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(Z)+1):((i+1)*nrow(Z)), (ncol(Y)+ncol(X)+1):(ncol(Y)+ncol(X)+ncol(Z))]=Z
imp[(i*nrow(clus)+1):((i+1)*nrow(clus)), (ncol(Y)+ncol(X)+ncol(Z)+1)]=clus
imp[(i*nrow(Z)+1):((i+1)*nrow(Z)), (ncol(Y)+ncol(X)+ncol(Z)+2)]=c(1:nrow(Y))
imp[(i*nrow(Z)+1):((i+1)*nrow(Z)), (ncol(Y)+ncol(X)+ncol(Z)+3)]=i
if (meth=="fixed") {
fixed=1
} else {
fixed=0
}
.Call("jomo1ranhrC", Y, Yimp, Yimp2, Y.cat, X, Z, clus,betait,uit,bpost,upost,covit,opost, covuit,cpost,nbetween, l1cov.prior,l2cov.prior,Y.numcat, ncol(Y),ait,a.prior,out.iter, fixed, 0, miss.pat.id, n.patterns, PACKAGE = "jomo")
betapost[,,(i-1)]=bpost
upostall[,,(i-1)]=upost
omegapost[,,(i-1)]=opost
covupost[,,(i-1)]=cpost
bpost<-matrix(0,nrow(beta.start),ncol(beta.start))
opost<-matrix(0,nrow(l1cov.start),ncol(l1cov.start))
upost<-matrix(0,nrow(u.start),ncol(u.start))
cpost<-matrix(0,nrow(l2cov.start),ncol(l2cov.start))
imp[(i*nrow(Y)+1):((i+1)*nrow(Y)),1:ncol(Y)]=Yimp2
if (output==1) cat("Imputation number ", i, "registered", "\n")
}
imp<-data.frame(imp)
imp[,(ncol(Y)+ncol(X)+ncol(Z)+1)]<-factor(imp[,(ncol(Y)+ncol(X)+ncol(Z)+1)])
levels(imp[,(ncol(Y)+ncol(X)+ncol(Z)+1)])<-previous_levels_clus
clus<-factor(clus)
levels(clus)<-previous_levels_clus
for (j in 1:(ncol(Y)+ncol(X)+ncol(Z))) {
imp[,j]=as.numeric(imp[,j])
}
if (is.null(colnamy)) colnamy=paste("Y", 1:ncol(Y), sep = "")
if (is.null(colnamz)) colnamz=paste("Z", 1:ncol(Z), sep = "")
if (is.null(colnamx)) colnamx=paste("X", 1:ncol(X), sep = "")
colnames(imp)<-c(colnamy,colnamx,colnamz,"clus","id","Imputation")
dimnames(betapost)[1] <- list(colnamx)
dimnames(betapost)[2] <- list(colnamy)
dimnames(omegapost)[1] <- list(paste(colnamy,rep(levels(clus),each=ncol(Y)), sep="."))
dimnames(omegapost)[2] <- list(colnamy)
colnamcovu<-paste(colnamy,rep(colnamz,each=ncol(omegapost)),sep="*")
dimnames(covupost)[1] <- list(colnamcovu)
dimnames(covupost)[2] <- list(colnamcovu)
dimnames(upostall)[1]<-list(levels(clus))
dimnames(upostall)[2]<-list(colnamcovu)
betapostmean<-data.frame(apply(betapost, c(1,2), mean))
upostmean<-data.frame(apply(upostall, c(1,2), mean))
omegapostmean<-data.frame(apply(omegapost, c(1,2), mean))
covupostmean<-data.frame(apply(covupost, c(1,2), mean))
if (output==1) {
cat("The posterior mean of the fixed effects estimates is:\n")
print(t(betapostmean))
cat("\nThe posterior mean of the random effects estimates is:\n")
print(upostmean)
cat("\nThe posterior mean of the level 1 covariance matrices is:\n")
print(omegapostmean)
cat("\nThe posterior mean of the level 2 covariance matrix is:\n")
print(covupostmean)
}
return(imp)
}
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