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multiposteriorCLASS.R
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multiposteriorCLASS.R
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multiposteriorchineseAFT = function(c,Y1,Y2,D1,D2,That, K, r, si,sig2.dat,gmmx1, gmmx2, regy1, regy2) {
numclust <- table(factor(c, levels = 1:K))
activeclass<- which(numclust!=0)
gmmx1 <- gmmx1
gmmx2 <- gmmx2
regy1 <- regy1
regy2 <- regy2
ctemp <- c
Ytemp1 <- Y1
Ytemp2 <- Y2
Ytemp1.scaled <- matrix(NA, nrow = N, ncol = D1)
Ytemp2.scaled <- matrix(NA, nrow = N, ncol = D2)
for ( i in 1:length(activeclass)) {
clust <- which(c == activeclass[i])
if (length(clust)==1){
Ytemp1.scaled[clust,1:D1] <- matrix(0, nrow =1, ncol =D1)
Ytemp2.scaled[clust,1:D2] <- matrix(0, nrow =1, ncol =D2)
} else {
Ytemp1.scaled[clust,1:D1] <- scale(Ytemp1[clust,1:D1], center = TRUE, scale = TRUE)
Ytemp2.scaled[clust,1:D2] <- scale(Ytemp2[clust,1:D2], center = TRUE, scale = TRUE)
}
}
weights.final <- c(0)
## This can't be parallelized !!!!!
for(l in 1:N) {
temp <- ctemp[l]
cminus <- ctemp
cminus[l] <- NA
## The table function helps converting the data point specific indicator variables to class specific indicator variables
g <- table(factor(cminus, levels = 1:K))
active <- which(g!=0)
### Length of the number of active clusters
kminus <- length(active)
## 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
active <- append(active, max(active)+1)
active <- append(active, max(active)+1)
## If the observation was singelton (i.e no other point was associated with it then we assign to kminus +1 parameter)
if(length(which(cminus==temp))==0)
{
## The kminus+1 parameter gets the value of the temporary variable
ctemp[l] <- active[kminus+1]
gmmx1$mu[active[kminus+1],1:D1] <- gmmx1$mu[temp,1:D1]
gmmx1$S[active[kminus]+1,1:D1,1:D1] <- gmmx1$S[temp,1:D1,1:D1]
regy1$beta0[active[kminus+1]] <- regy1$beta0[temp]
regy1$betahat[active[kminus+1], 1:D1] <- regy1$betahat[temp, 1:D1]
regy1$sigma2[active[kminus+1]] <- regy1$sigma2[temp]
regy1$lambda2[active[kminus+1]] <- regy1$lambda2[temp]
regy1$tau2[active[kminus+1], 1:D1] <- regy1$tau2[temp, 1:D1]
gmmx2$mu[active[kminus+1],1:D2] <- gmmx2$mu[temp,1:D2]
gmmx2$S2[active[kminus]+1,1:D2,1:D2] <- gmmx2$S2[temp,1:D2,1:D2]
regy2$beta0[active[kminus+1]] <- regy2$beta0[temp]
regy2$betahat[active[kminus+1], 1:D2] <- regy2$betahat[temp, 1:D2]
regy2$sigma2[active[kminus+1]] <- regy2$sigma2[temp]
regy2$lambda2[active[kminus+1]] <- regy2$lambda2[temp]
regy2$tau2[active[kminus+1], 1:D2] <- regy2$tau2[temp, 1:D2]
## Also the second auxilary variable should be drawn from the prior distribution
source('priordraw.R')
priorone1 <- NA
priorone2 <- NA
##### priorone1 <- priordraw(beta, gmmx1$W, gmmx1$epsilon, ro, r, si,N,D1, sig2.dat)
repeat
{
priorone1 <- priordraw(beta, gmmx1$W, gmmx1$epsilon, gmmx1$ro, r, si,N,D1, sig2.dat)
res <- try(chol(priorone1$Sigma), silent = TRUE)
if (class(res) != "try-error"){
break
}
}
gmmx1$mu[active[kminus+2],1:D1] <- priorone1$mu
gmmx1$S[active[kminus+2],1:D1,1:D1] <- priorone1$Sigma
regy1$beta0[active[kminus+2]] <- priorone1$beta0
regy1$sigma2[active[kminus+2]] <- priorone1$sigma2
regy1$betahat[active[kminus+2],1:D1] <- priorone1$betahat
regy1$lambda2[active[kminus+2]] <- priorone1$lambda2
regy1$tau2[active[kminus+2], 1:D1] <- priorone1$tau2
# priorone2 <- priordraw(beta, gmmx2$W, gmmx2$epsilon, ro, r, si,N,D2, sig2.dat)
repeat
{
priorone2 <- priordraw(beta, gmmx2$W, gmmx2$epsilon, gmmx2$ro, r, si,N,D2, sig2.dat)
res <- try(chol(priorone2$Sigma), silent = TRUE)
if (class(res) != "try-error"){
break
}
}
gmmx2$mu[active[kminus+2],1:D2] <- priorone2$mu
gmmx2$S[active[kminus+2],1:D2,1:D2] <- priorone2$Sigma
regy2$beta0[active[kminus+2]] <- priorone2$beta0
regy2$sigma2[active[kminus+2]] <- priorone2$sigma2
regy2$betahat[active[kminus+2],1:D2] <- priorone2$betahat
regy2$lambda2[active[kminus+2]] <- priorone2$lambda2
regy2$tau2[active[kminus+2], 1:D2] <- priorone2$tau2
## The number of points in the cluster
} else {
priorone1 <- NA
priorone2 <- NA
### Draw the values of two auxilary parameters from Prior Distribution
source('priorPARAMETERS.R')
#priorone1 <- priordraw(beta, gmmx1$W, gmmx1$epsilon, ro, r, si,N,D1, sig2.dat)
repeat {
priorone1 <- priordraw(beta, gmmx1$W, gmmx1$epsilon, gmmx1$ro, r, si,N,D1, sig2.dat)
res <- try(chol(priorone1$Sigma), silent = TRUE)
if (class(res) != "try-error"){
break
}
}
gmmx1$mu[active[kminus+1],1:D1] <- priorone1$mu
gmmx1$S[active[kminus+1],1:D1,1:D1] <- priorone1$Sigma
regy1$beta0[active[kminus+1]] <- priorone1$beta0
regy1$sigma2[active[kminus+1]] <- priorone1$sigma2
regy1$betahat[active[kminus+1],1:D1] <- priorone1$betahat
regy1$lambda2[active[kminus+1]] <- priorone1$lambda2
regy1$tau2[active[kminus+1], 1:D1] <- priorone1$tau2
# priorone2 <- priordraw(beta, gmmx2$W, gmmx2$epsilon, ro, r, si,N,D2, sig2.dat)
repeat {
priorone2 <- priordraw(beta, gmmx2$W, gmmx2$epsilon, gmmx2$ro, r, si,N, D2, sig2.dat)
res <- try(chol(priorone2$Sigma), silent = TRUE)
if (class(res) != "try-error"){
break
}
}
gmmx2$mu[active[kminus+1],1:D2] <- priorone2$mu
gmmx2$S[active[kminus+1],1:D2,1:D2] <- priorone2$Sigma
regy2$beta0[active[kminus+1]] <- priorone2$beta0
regy2$sigma2[active[kminus+1]] <- priorone2$sigma2
regy2$betahat[active[kminus+1],1:D2] <- priorone2$betahat
regy2$lambda2[active[kminus+1]] <- priorone2$lambda2
regy2$tau2[active[kminus+1], 1:D2] <- priorone2$tau2
source('priorPARAMETERS.R')
#priorone1 <- priordraw(beta, gmmx1$W, gmmx1$epsilon, ro, r, si,N,D1, sig2.dat)
repeat {
priorone1 <- priordraw(beta, gmmx1$W, gmmx1$epsilon, gmmx1$ro, r, si,N,D1, sig2.dat)
res <- try(chol(priorone1$Sigma),silent = TRUE)
if (class(res) != "try-error"){
break
}
}
gmmx1$mu[active[kminus+2],1:D1] <- priorone1$mu
gmmx1$S[active[kminus+2],1:D1,1:D1] <- priorone1$Sigma
regy1$beta0[active[kminus+2]] <- priorone1$beta0
regy1$sigma2[active[kminus+2]] <- priorone1$sigma2
regy1$betahat[active[kminus+2],1:D1] <- priorone1$betahat
regy1$lambda2[active[kminus+2]] <- priorone1$lambda2
regy1$tau2[active[kminus+2], 1:D1] <- priorone1$tau2
##priorone2 <- priordraw(beta, gmmx2$W, gmmx2$epsilon, ro, r, si,N,D2, sig2.dat)
repeat {
priorone2 <- priordraw(beta, gmmx2$W, gmmx2$epsilon, gmmx2$ro, r, si,N,D2, sig2.dat)
res <- try(chol(priorone2$Sigma), silent = TRUE)
if (class(res) != "try-error"){
break
}
}
gmmx2$mu[active[kminus+2],1:D2] <- priorone2$mu
gmmx2$S[active[kminus+2],1:D2,1:D2] <- priorone2$Sigma
regy2$beta0[active[kminus+2]] <- priorone2$beta0
regy2$sigma2[active[kminus+2]] <- priorone2$sigma2
regy2$betahat[active[kminus+2],1:D2] <- priorone2$betahat
regy2$lambda2[active[kminus+2]] <- priorone2$lambda2
regy2$tau2[active[kminus+2], 1:D2] <- priorone2$tau2
}
#######################################################
posterior <- matrix(NA, nrow = length(active), ncol = 1)
## Calculating the probabalities for drawing the value of c_i from the active classes
for (j in 1:kminus) {
posterior[j] <- log(g[active[j]] /(N-1+alpha)) + dMVN(x = as.vector(t(Ytemp1[l,])), mean = gmmx1$mu[active[j],1:D1], Q = gmmx1$S[active[j],1:D1,1:D1], log = TRUE) + dMVN(x = as.vector(t(Ytemp2[l,])), mean = gmmx2$mu[active[j],1:D2], Q = gmmx2$S[active[j],1:D2,1:D2], log =TRUE) + dnorm(x = That[l], mean = regy1$beta0[active[j]] + regy1$betahat[active[j],] %*% as.vector(t(Ytemp1.scaled[l,])), sd = sqrt(regy1$sigma2[active[j]]),log = TRUE ) + dnorm(x = That[l], mean = regy2$beta0[active[j]] + regy2$betahat[active[j],] %*% as.vector(t(Ytemp2.scaled[l,])), sd = sqrt(regy2$sigma2[active[j]]), log = TRUE )
}
posterior[kminus+1] <- log((0.5 * alpha) /(N-1+alpha)) + dMVN(x = as.vector(t(Ytemp1[l,])), mean = gmmx1$mu[active[kminus+1],1:D1], Q = gmmx1$S[active[kminus+1],1:D1,1:D1], log = TRUE) + dMVN(x = as.vector(t(Ytemp2[l,])), mean = gmmx2$mu[active[kminus+1],1:D2], Q = gmmx2$S[active[kminus+1],1:D2,1:D2], log = TRUE) + dnorm(x = That[l], mean = regy1$beta0[active[kminus+1]] + regy1$betahat[active[kminus+1],] %*% as.vector(t(Ytemp1.scaled[l,])), sd = sqrt(regy1$sigma2[active[kminus+1]]), log =TRUE ) + dnorm(x = That[l], mean = regy2$beta0[active[kminus+1]] + regy2$betahat[active[kminus+1],] %*% as.vector(t(Ytemp2.scaled[l,])), sd = sqrt(regy2$sigma2[active[kminus+1]]), log =TRUE)
posterior[kminus+2] <- log((0.5 * alpha) /(N-1+alpha)) + dMVN(x = as.vector(t(Ytemp1[l,])), mean = gmmx1$mu[active[kminus+2],1:D1], Q = gmmx1$S[active[kminus+2],1:D1,1:D1], log = TRUE) + dMVN(x = as.vector(t(Ytemp2[l,])), mean = gmmx2$mu[active[kminus+2],1:D2], Q = gmmx2$S[active[kminus+2],1:D2,1:D2], log = TRUE) + dnorm(x = That[l], mean = regy1$beta0[active[kminus+2]] + regy1$betahat[active[kminus+2],] %*% as.vector(t(Ytemp1.scaled[l,])), sd = sqrt(regy1$sigma2[active[kminus+2]]), log =TRUE ) + dnorm(x = That[l], mean = regy2$beta0[active[kminus+2]] + regy2$betahat[active[kminus+2],] %*% as.vector(t(Ytemp2.scaled[l,])), sd = sqrt(regy2$sigma2[active[kminus+2]]), log =TRUE)
# posterior[kminus+1] <- (0.5 * alpha) /(N-1+alpha) * dMVN(as.vector(t(Y[l,1:D])), mean = mu[active[kminus+1],1:D], Q= S[active[kminus+1],1:D,1:D]) * dnorm(x = That[l], mean = beta0[active[kminus+1]] + betahat[active[kminus+1],1:D] %*% as.vector(t(Ytemp[l,1:D])), sd = sqrt(sigma2[active[kminus+1]]) )
#
# posterior[kminus+2] <- (0.5 * alpha) /(N-1+alpha) * dMVN(as.vector(t(Y[l,1:D])), mean = mu[active[kminus+2],1:D], Q = S[active[kminus+2],1:D,1:D]) * dnorm(x = That[l], mean = beta0[active[kminus+2]] + betahat[active[kminus+2],1:D] %*% as.vector(t(Ytemp[l,1:D])), sd = sqrt(sigma2[active[kminus+2]]) )
#
## Calculating the normalization constant for probabilities
post <- exp(posterior)
ctemp[l] <- sample(active, 1, prob= post, replace = TRUE)
}
c <- ctemp
######## Delete those observations that are not associcated with no data point #################3
g <- table(factor(c, levels = 1:K))
inactive <- which(g==0)
for ( i in 1:length(inactive)){
gmmx1$mu[inactive[i],] <- NA
gmmx1$S[inactive[i],1:D1,1:D1] <- NA
gmmx2$mu[inactive[i],] <- NA
gmmx2$S[inactive[i],1:D2,1:D2] <- NA
regy1$beta0[inactive[i]] <- NA
regy2$beta0[inactive[i]] <- NA
regy2$sigma2[inactive[i]] <- NA
regy1$sigma2[inactive[i]] <- NA
regy1$betahat[inactive[i],] <- NA
regy2$betahat[inactive[i],] <- NA
regy1$lambda2[inactive[i]] <- NA
regy2$lambda2[inactive[i]] <- NA
regy1$tau2[inactive[i],] <- NA
regy2$tau2[inactive[i],] <- NA
}
list('c'=c,'gmmx1'=gmmx1,'gmmx2'= gmmx2,'regy1'= regy1,'regy2'= regy2)
}