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predictTIME.R
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predictTIME.R
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### This function takes the posterior parameters AND predicts the time for the new points
#### The fundamental assumption is that EACH NEW TEST POINT IS CONDITIONALLY INDEPENDENT on the OTHER POINTS
#### We predict value of one point at a time
### The final output is Time for the new samples, ONE AT A TIME
#### when making a final comparison it's a good idea to weigh the points according to the model likelihood
predictchineseAFTtime = function(Y.input){
source('priorPARAMETERS.R')
Y.new <- Y.input
N.new <- nrow(Y.new)
c.new.list <- list(0)
## The number of posterior samples
#That.new <- time.new
post.time = matrix(NA,nrow = nrow(Y.new), ncol = Nps)
print("GOING THROUGH MCMC Samples")
pb <- txtProgressBar(min = 1, max = Nps , style = 3)
cind <- c(0)
# modelweights <- c(0)
predCIndex.sbc <- c(0)
for (count in 1:Nps){
## Assign the parameters to the posterior sample
ctemp <- c.list[[count]]
mu <- mu.list[[count]]
S <- S.list[[count]]
beta0 <- beta0.list[[count]]
betahat <- betahat.list[[count]]
sigma2 <- sigma2.list[[count]]
g <- table(factor(ctemp, levels = 1:K))
activeclass <- which(g!=0)
## The table function helps converting the data point specific indicator variables to class specific indicator variables
kminus <- length(activeclass)
# active <- activeclass
#Two Auxilary Variables
#The name of the auxilary variables are taken to be one and two more than the maximum value in the already active cluster set
activeclass <- append(activeclass, max(activeclass)+1)
activeclass <- append(activeclass, max(activeclass)+1)
active <- activeclass
### Assigning values to parameters
priortwo <- NA
priortwo <- priordraw(beta, W, epsilon, ro, r, si,N,D, sig2.dat)
mu[active[kminus+1],1:D] <- priortwo$mu
S[active[kminus+1],1:D,1:D] <- priortwo$Sigma[1:D,1:D]
beta0[active[kminus+1]] <- priortwo$beta0
sigma2[active[kminus+1]] <- priortwo$sigma2
betahat[active[kminus+1],1:D] <- priortwo$betahat
priorthree <- NA
priorthree <- priordraw(beta, W, epsilon, ro, r, si,N,D, sig2.dat)
mu[active[kminus+2],1:D] <- priorthree$mu
S[active[kminus+2],1:D,1:D] <- priorthree$Sigma[1:D,1:D]
beta0[active[kminus+2]] <- priorthree$beta0
sigma2[active[kminus+2]] <- priorthree$sigma2
betahat[active[kminus+2],1:D] <- priorthree$betahat
#######################################################
########### Scaling the data ########################
Y.new.scaled.list <- list(0)
for (j in 1:kminus) {
clust <- which(ctemp == active[j])
if(length(clust) > 1){
obj.t <- scale(Y[clust,1:D], center = TRUE, scale = TRUE)
Y.new.scaled.list[[j]] <- scale(Y.new, center = attr(obj.t,"scaled:center"), scale = (attr(obj.t,"scaled:scale")))
} else {
obj.t <- scale(Y[,1:D], center = TRUE, scale = TRUE)
Y.new.scaled.list[[j]] <- scale(Y.new, center = attr(obj.t,"scaled:center"), scale = (attr(obj.t,"scaled:scale")))
}
}
for (j in (kminus+1):(kminus+2)) {
obj.t <- scale(Y[,1:D], center = TRUE, scale = TRUE)
Y.new.scaled.list[[j]] <- scale(Y.new, center = attr(obj.t,"scaled:center"), scale = (attr(obj.t,"scaled:scale")))
}
###### Some quantities used to store probabilities
posteriortime <- matrix(0, nrow = length(active), ncol = N.new)
posteriortimeweight <- matrix(0, nrow = length(active), ncol = N.new)
weights <- matrix(0, nrow = length(active), ncol = N.new)
## This can't be parallelized !!!!!
for(l in 1:N.new) {
## Calculating the Expectations and also the normalization constant for the Expectation
for (j in 1:kminus) {
posteriortime[j,l] <- beta0[active[j]] + betahat[active[j],1:D] %*% as.vector(t(Y.new.scaled.list[[j]][l,]))
posteriortimeweight[j,l] <- log(g[active[j]]/ (N-1+alpha)) + dMVN(as.vector(t(Y.new[l,1:D])), mean = mu[active[j],1:D], Q = S[active[j],1:D,1:D], log =TRUE)
}
res <- try(dMVN(as.vector(t(Y.new[l,1:D])), mean = mu[active[kminus+1],1:D], Q= S[active[kminus+1],1:D,1:D]), silent=TRUE)
if (class(res) == "try-error"){
posteriortime[kminus+1,l] <- 0
posteriortimeweight[kminus+1,l] <- -Inf
} else{
posteriortime[kminus+1,l] <- beta0[active[kminus+1]] + betahat[active[kminus+1],1:D] %*% as.vector(t(Y.new.scaled.list[[j]][l,]))
posteriortimeweight[kminus+1,l] <- log(alpha/ (N-1+alpha)) + dMVN(as.vector(t(Y.new[l,1:D])), mean = mu[active[kminus+1],1:D], Q= S[active[kminus+1],1:D,1:D], log = TRUE)
}
res2 <- try(dMVN(as.vector(t(Y.new[l,1:D])), mean = mu[active[kminus+2],1:D], Q= S[active[kminus+2],1:D,1:D]), silent=TRUE)
if (class(res) == "try-error"){
posteriortime[kminus+2,l] <- 0
posteriortimeweight[kminus+2,l] <- -Inf
} else{
posteriortime[kminus+2,l] <- beta0[active[kminus+2]] + betahat[active[kminus+2],1:D] %*% as.vector(t(Y.new.scaled.list[[j]][l,]))
posteriortimeweight[kminus+2,l] <- log(alpha/ (N-1+alpha)) + dMVN(as.vector(t(Y.new[l,1:D])), mean = mu[active[kminus+2],1:D], Q= S[active[kminus+2],1:D,1:D], log = TRUE)
}
weights[,l] <- exp(posteriortimeweight[,l])/sum(exp(posteriortimeweight[,l]))
}
for ( l in 1:N.new){
post.time[l,count] <- as.numeric(t(posteriortime[,l]) %*% weights[,l])
}
# modelweights[count] <- sum(exp((1/N.new) *apply(posteriortimeweight,1,sum)))
predCIndex.sbc[count] <- as.numeric(survConcordance(smod.new ~ exp(-post.time[,count]))[1])
#
# print(cind[count])
## Calculting the Model Weight
Sys.sleep(0.1)
setTxtProgressBar(pb, count)
}
# #### To calculate average values over MCMC samples
# modelweight.norm <- modelweights/(sum(modelweights))
#
# post.time.corrected <- post.time
# for ( i in 1:Nps){
# post.time.corrected[,i] <- post.time[,i] *modelweight.norm[i]
# }
# post.time.avg <<- apply(post.time.corrected[,1:Nps],1,sum)
predCIndex.sbc <<- predCIndex.sbc
post.time <<- post.time
}