#' @rdname predictY
#' @export
#'
predictY.multlcmm <- function(x,newdata,var.time,methInteg=0,nsim=20,draws=FALSE,ndraws=2000,na.action=1,...)
{
if(missing(newdata)) stop("The argument newdata should be specified")
if(missing(x)) stop("The argument x should be specified")
if (!inherits(x, "multlcmm")) stop("use only with \"lcmm\" or \"multlcmm\" objects")
if (!all(x$Xnames2 %in% colnames(newdata))) stop(paste(c("newdata should at least include the following covariates: ","\n",x$Xnames2),collapse=" "))
if (!inherits(newdata, "data.frame")) stop("newdata should be a data.frame object")
#if(missing(var.time)) stop("missing argument 'var.time'")
#if(!(var.time %in% colnames(newdata))) stop("'var.time' should be included in newdata")
if(any(x$linktype==3))
{
if(methInteg==0) stop("predictions for ordinal outcomes are only available with MC method. Please use methInteg=1 and set nsim argument.")
# if(any(x$linktype!=3)) stop("predictions for mixed outcomes (ordinal and continuous) are not available yet.")
# if(any(x$idcor>0)) stop("predictions with BM or AR correlations are not available yet.")
}
if(x$conv==1 | x$conv==2 | x$conv==3)
{
if(x$conv>1 & draws==TRUE)
{
cat("No confidence interval will be provided since the program did not converge properly \n")
draws <- FALSE
}
if(!(na.action%in%c(1,2)))stop("only 1 for 'na.omit' or 2 for 'na.fail' are required in na.action argument")
call_fixed <- x$call$fixed[3]
if(is.null(x$call$random)) {call_random <- -1} else call_random <- x$call$random[2]
if(is.null(x$call$classmb)) {call_classmb <- -1} else call_classmb <- x$call$classmb[2]
if(is.null(x$call$mixture)) {call_mixture <- -1} else call_mixture <- x$call$mixture[2]
X1 <- NULL
X2 <- NULL
b1 <- NULL
b2 <- NULL
if(!(na.action%in%c(1,2)))stop("only 1 for 'na.omit' or 2 for 'na.fail' are required in na.action argument")
if(na.action==1){
na.action=na.omit
}else{
na.action=na.fail
}
## transform to factor is the variable appears in levels$levelsdata
for(v in colnames(newdata))
{
if(v %in% names(x$levels$levelsdata))
{
if(!is.null(x$levels$levelsdata[[v]]))
{
newdata[,v] <- factor(newdata[,v], levels=x$levels$levelsdata[[v]])
}
}
}
call_fixed <- gsub("factor","",call_fixed)
call_fixed <- gsub("contrast","",call_fixed)
call_random <- gsub("factor","",call_random)
call_classmb <- gsub("factor","",call_classmb)
call_mixture <- gsub("factor","",call_mixture)
call_mixture <- formula(paste("~",call_mixture,sep=""))
call_random <- formula(paste("~",call_random,sep=""))
call_classmb <- formula(paste("~",call_classmb,sep=""))
## Traitement des donnees manquantes
mcall <- match.call()[c(1,match(c("data","subset","na.action"),names(match.call()),0))]
mcall$na.action <- na.action
mcall$data <- newdata
## fixed
m <- mcall
m$formula <- formula(paste("~",call_fixed,sep=""))
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.fixed <- attr(m,"na.action")
## mixture
na.mixture <- NULL
if(call_mixture != -1)
{
m <- mcall
m$formula <- call_mixture
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.mixture <- attr(m,"na.action")
}
## random
na.random <- NULL
if(call_random != -1)
{
m <- mcall
m$formula <- call_random
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.random <- attr(m,"na.action")
}
## classmb
na.classmb <- NULL
if(call_classmb != -1)
{
m <- mcall
m$formula <- call_classmb
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.classmb <- attr(m,"na.action")
}
## cor
na.cor <- NULL
if(x$N[7]>0)
{
z <- which(x$idcor0==1)
var.cor <- newdata[,x$Xnames[z]]
na.cor <- which(is.na(var.cor))
}
##var.time
if(!missing( var.time))
{
if(!(var.time %in% colnames(newdata))) stop("'var.time' should be included in newdata")
times <- newdata[,var.time,drop=FALSE]
}
else
{
times <- newdata[,1,drop=FALSE]
}
## Table sans donnees manquante: newdata
na.action <- unique(c(na.fixed,na.mixture,na.random,na.classmb,na.cor))
if(length(na.action)){
newdata1 <- newdata[-na.action,]
times <- times[-na.action,,drop=FALSE]
} else {
newdata1 <- newdata
}
## create one data frame for each formula (useful with factors)
newdata1fixed <- newdata1
for(v in colnames(newdata1fixed))
{
if(v %in% names(x$levels$levelsfixed))
{
if(!is.null(x$levels$levelsfixed[[v]]))
{
newdata1fixed[,v] <- factor(newdata1fixed[,v], levels=x$levels$levelsfixed[[v]])
if(any(is.na(newdata1fixed[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
newdata1mixture <- newdata1
for(v in colnames(newdata1mixture))
{
if(v %in% names(x$levels$levelsmixture))
{
if(!is.null(x$levels$levelsmixture[[v]]))
{
newdata1mixture[,v] <- factor(newdata1mixture[,v], levels=x$levels$levelsmixture[[v]])
if(any(is.na(newdata1mixture[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
newdata1random <- newdata1
for(v in colnames(newdata1random))
{
if(v %in% names(x$levels$levelsrandom))
{
if(!is.null(x$levels$levelsrandom[[v]]))
{
newdata1random[,v] <- factor(newdata1random[,v], levels=x$levels$levelsrandom[[v]])
if(any(is.na(newdata1random[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
newdata1classmb <- newdata1
for(v in colnames(newdata1classmb))
{
if(v %in% names(x$levels$levelsclassmb))
{
if(!is.null(x$levels$levelsclassmb[[v]]))
{
newdata1classmb[,v] <- factor(newdata1classmb[,v], levels=x$levels$levelsclassmb[[v]])
if(any(is.na(newdata1classmb[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
## Construction de nouvelles var eplicatives sur la nouvelle table
X_fixed <- X_mixture <- X_random <- X_classmb <- NULL
## fixed
X_fixed <- model.matrix(formula(paste("~",call_fixed,sep="")),data=newdata1fixed)
if(colnames(X_fixed)[1]=="(Intercept)"){
colnames(X_fixed)[1] <- "intercept"
}
## mixture
if(call_mixture != ~-1){
X_mixture <- model.matrix(call_mixture,data=newdata1mixture)
if(colnames(X_mixture)[1]=="(Intercept)"){
colnames(X_mixture)[1] <- "intercept"
}
}
## random
if(call_random != ~-1){
X_random <- model.matrix(call_random,data=newdata1random)
if(colnames(X_random)[1]=="(Intercept)"){
colnames(X_random)[1] <- "intercept"
}
}
## classmb
if(call_classmb != ~-1){
X_classmb <- model.matrix(call_classmb,data=newdata1classmb)
colnames(X_classmb)[1] <- "intercept"
}
##cor
if(x$N[7]>0) #on reprend la variable de temps de cor (sans NA)
{
z <- which(x$idcor0==1)
var.cor <- newdata1[,x$Xnames[z]]
}
## pour mettre les var dans le bon ordre
newdata1 <- X_fixed
colX <- colnames(X_fixed)
if(!is.null(X_mixture)){
for(i in 1:length(colnames(X_mixture))){
if((colnames(X_mixture)[i] %in% colnames(newdata1))==FALSE){
newdata1 <- cbind(newdata1,X_mixture[,i])
colnames(newdata1) <- c(colX,colnames(X_mixture)[i])
colX <- colnames(newdata1)
}
}
}
if(!is.null(X_random)){
for(i in 1:length(colnames(X_random))){
if((colnames(X_random)[i] %in% colnames(newdata1))==FALSE){
newdata1 <- cbind(newdata1,X_random[,i])
colnames(newdata1) <- c(colX,colnames(X_random)[i])
colX <- colnames(newdata1)
}
}
}
if(!is.null(X_classmb)){
for(i in 1:length(colnames(X_classmb))){
if((colnames(X_classmb)[i] %in% colnames(newdata1))==FALSE){
newdata1 <- cbind(newdata1,X_classmb[,i])
colnames(newdata1) <- c(colX,colnames(X_classmb)[i])
colX <- colnames(newdata1)
}
}
}
if(x$N[7]>0)
{
if( x$idg0[z]==0 & x$idea0[z]==0 & x$idprob0[z]==0)
{
newdata1 <- cbind(newdata1,var.cor)
colnames(newdata1) <- c(colX,x$Xnames[z])
colX <- colnames(newdata1)
}
}
#arguments pour predictMult
maxmes <- dim(newdata1)[1]
X0 <- as.vector(newdata1)
ncor <- x$N[7]
nalea <- x$N[6]
nv <- dim(newdata1)[2]
ny <- x$N[8]
best <- x$best
nef <- x$N[3]
nvc <- x$N[4]
if(nvc>0)
{
if(x$idiag==0) best[nef+1:nvc] <- x$cholesky[-1]
else best[nef+1:nvc] <- x$cholesky[c((1:(nvc+1)*2:(nvc+2))/2)[-1]]
}
npm <- length(best)
nbzitr <- rep(2,ny)
nbzitr[which(x$linktype==2)] <- x$nbnodes
Ymarg <- matrix(0,maxmes*ny,x$ng)
#if(verbose==TRUE) print(head(newdata1))
if(!draws)
{
#if(verbose==TRUE) cat("ny=",ny,"nvc=",nvc,"ncontr=",x$N[2],"nv=",nv,"\n",x$Xnames,"\n",head(newdata1),"\n","idg",x$idg0)
out <- .Fortran(C_predictmult,
as.double(X0),
as.integer(x$idprob0),
as.integer(x$idea0),
as.integer(x$idg0),
as.integer(x$idcor0),
as.integer(x$idcontr0),
as.integer(x$ng),
as.integer(ncor),
as.integer(nalea),
as.integer(nv),
as.integer(ny),
as.integer(maxmes),
as.integer(x$idiag),
as.integer(x$N[5]),
as.integer(npm),
as.double(best),
as.double(x$epsY),
as.integer(x$linktype),
as.integer(nbzitr),
as.double(x$linknodes),
as.integer(unlist(x$modalites)),
as.integer(x$nbmod),
as.integer(nsim),
as.integer(methInteg),
Ymarg=as.double(Ymarg))
out$Ymarg[out$Ymarg==9999] <- NA
#Ypred <- matrix(out$Ymarg,ncol=x$ng,byrow=FALSE)
Ypred <- data.frame(rep(x$Ynames,each=maxmes),matrix(out$Ymarg,ncol=x$ng,byrow=FALSE))
if (x$ng==1) colnames(Ypred) <- c("Yname","Ypred")
if (x$ng>1) colnames(Ypred) <- c("Yname",paste("Ypred_class",1:x$ng,sep=""))
}
else #draws
{
ndraws <- as.integer(ndraws)
ydraws <- NULL
posfix <- eval(x$call$posfix)
if(ndraws>0)
{
Mat <- matrix(0,ncol=npm,nrow=npm)
Mat[upper.tri(Mat,diag=TRUE)]<- x$V
if(length(posfix))
{
Mat2 <- Mat[-posfix,-posfix]
Chol2 <- chol(Mat2)
Chol <- matrix(0,npm,npm)
Chol[setdiff(1:npm,posfix),setdiff(1:npm,posfix)] <- Chol2
Chol <- t(Chol)
}
else
{
Chol <- chol(Mat)
Chol <- t(Chol)
}
}
for (j in 1:ndraws)
{ #cat("boucle sur ndraws j=",j,"\n")
bdraw <- rnorm(npm)
bdraw <- best + Chol %*% bdraw
out <- .Fortran(C_predictmult,
as.double(X0),
as.integer(x$idprob0),
as.integer(x$idea0),
as.integer(x$idg0),
as.integer(x$idcor0),
as.integer(x$idcontr0),
as.integer(x$ng),
as.integer(ncor),
as.integer(nalea),
as.integer(nv),
as.integer(ny),
as.integer(maxmes),
as.integer(x$idiag),
as.integer(x$N[5]),
as.integer(npm),
as.double(bdraw),
as.double(x$epsY),
as.integer(x$linktype),
as.integer(nbzitr),
as.double(x$linknodes),
as.integer(unlist(x$modalites)),
as.integer(x$nbmod),
as.integer(nsim),
as.integer(methInteg),
Ymarg=as.double(Ymarg))
out$Ymarg[out$Ymarg==9999] <- NA
ydraws <- cbind(ydraws,out$Ymarg)
}
f <- function(x) {
quantile(x[!is.na(x)],probs=c(0.025,0.5,0.975))
}
ydistr <- apply(ydraws,1,FUN=f)
Ypred_50 <- matrix(ydistr[2,],ncol=x$ng,byrow=FALSE)
Ypred_2.5 <- matrix(ydistr[1,],ncol=x$ng,byrow=FALSE)
Ypred_97.5 <- matrix(ydistr[3,],ncol=x$ng,byrow=FALSE)
#Ypred <- cbind(Ypred_50,Ypred_2.5,Ypred_97.5)
Ypred <- data.frame(rep(x$Ynames,each=maxmes),Ypred_50,Ypred_2.5,Ypred_97.5)
if (x$ng==1) colnames(Ypred) <- c("Yname","Ypred_50","Ypred_2.5","Ypred_97.5")
if (x$ng>1) colnames(Ypred) <- c("Yname",c(paste("Ypred_50_class",1:x$ng,sep=""),paste("Ypred_2.5_class",1:x$ng,sep=""),paste("Ypred_97.5_class",1:x$ng,sep="")))
}
res.list <- NULL
res.list$pred <- Ypred
res.list$times <- times
}
else
{
cat(" The program stopped abnormally. No prediction can be computed.\n")
res.list <- list(pred=NA,times=NA)
}
class(res.list) <- "predictY"
return(res.list)
}
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