##' Prediction with new data and return a saved forest with mean surv prob at each time points
##' @export
rsfes.surv_predict <-
function(rsfesfit,newdata,uniquetimes,trlength=500){
trees=rsfesfit$pectrees
colindexes=rsfesfit$colindexes
newindexes=rsfesfit$newindexes
if (trlength>length(rsfesfit$pectrees))
stop("Number of Trees for prediction should not be more than Number of Trees Fitted")
# Preparing testpre dataframe
testpre <- matrix(0,nrow = dim(newdata)[1], ncol= length(uniquetimes))
colnames(testpre)=paste0(uniquetimes)
for (i in 1:trlength) {
#if (oobacc[i]<=avroobacc)
{
# preparing for testing
if (ncol(newdata)<=100){
testdata=extspace_testdat(newdata,newindexes[[i]])
testdata=testdata[,colindexes[[i]]]
}else{
testdata=newdata[,colindexes[[i]]]
testdata=extspace_testdat(testdata,newindexes[[i]])
}
testdata=as.data.frame(testdata)
predicts=predictSurvProb(trees[[i]],testdata,uniquetimes)
#convert all NA into zero
predicts[is.na(predicts)]=0
colnames(predicts)=paste0(uniquetimes)
#print((predicts[1,100]))
testpre<-testpre+predicts
#print(dim(testpre))
}
}
return(testpre/trlength)
}
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