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Sim_Out.R
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Sim_Out.R
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## retrieve arguments passed from the command line. (must run in parallel)
process <- as.integer(as.character(commandArgs(trailingOnly = TRUE)))
##---------------------------------------------------------------
## Required libraries
##---------------------------------------------------------------
library(pscl)
library(mnormt)
library(DPpackage)
library(data.table)
library(MCMCpack)
library(sn)
source("Cond_P.R")
Cond_Alpha_O <- function(Yc=NULL, Mp=NULL, Wp=NULL,Yp=NULL, Kp=NULL,K=NULL, a=NULL,tau=NULL, alpha=NULL,OFF=NULL, COV.a=NULL){
K_other <- setdiff(1:K, unique(Kp))
alpha_prop <- alpha
alpha_acc <- NULL
for(ind in K_other){
alpha_prop[ind,] <- rmnorm(1, a, diag(1/tau,dim.cov+3)/10)
alpha[ind,] <- alpha_prop[ind,]
}
# Check SD of proposal distribution (!Adaptive sampler!)
for(ind in unique(Kp)){
COV.alpha <- 2.38^2/((dim.cov+3))*(COV.a[(6*ind-5):(6*ind),1:6]+diag(0.1^10,(dim.cov+3)))
alpha_prop[ind,] <- rmnorm(1, alpha[ind,], COV.alpha)
rat1 <- dmnorm(alpha_prop[ind,], a, diag(1/tau,dim.cov+3), log=TRUE)+dmnorm(alpha[ind,], alpha_prop[ind,], COV.alpha, log=TRUE)+sum(dpois(Yc[which(Kp==ind)], exp(log(OFF[which(Kp==ind)])+cbind(1,Mp,Yp,Wp)[which(Kp==ind),]%*%(alpha_prop[ind,])),log=TRUE))
rat2 <- dmnorm(alpha[ind,], a, diag(1/tau,dim.cov+3), log=TRUE)+dmnorm(alpha_prop[ind,], alpha[ind,], COV.alpha, log=TRUE)+sum(dpois(Yc[which(Kp==ind)], exp(log(OFF[which(Kp==ind)])+cbind(1,Mp,Yp,Wp)[which(Kp==ind),]%*%(alpha[ind,])),log=TRUE))
rat <- rat1 - rat2
if(is.na(rat) | log(runif(1))>rat){
alpha_prop[ind,] <- alpha[ind,]
}else{
alpha[ind,] <- alpha_prop[ind,]
}
}
return(list(alpha=alpha_prop, acc=alpha_acc))
}
Cond_K_O <- function(Yc=NULL, Mp=NULL, Wp=NULL,Yp=NULL,K=NULL, Pp=NULL, alpha=NULL, sigma=NULL, OFF=NULL){
Pc.pre <- sapply(1:K, function(k) Pp[k]*dpois(Yc, exp(log(OFF)+cbind(1,Mp,Yp,Wp)%*%(alpha[k,]))))
zero_index <- which(rowSums(Pc.pre)==0 | is.na(rowSums(Pc.pre)))
Pc <- Pc.pre
Pc[zero_index,] <- 1
Kc <- apply(Pc,1, function(x) sample(1:20,1, prob=x))
return(list(Kc=Kc))
}
Cond_Main_O <- function(Yc=NULL, Mp=NULL, Yp=NULL, Wp=NULL, K=NULL,Kp=Kp, a=NULL, a_prior=NULL,tau=NULL,tau_prior=NULL, alpha=NULL, lambda=NULL,Pp=NULL,dim.cov=NULL,COV.med=NULL, OFF=NULL, COV.a=NULL, COV.tau=NULL){
#---- updating Alpha
update0 <- Cond_Alpha_O(Yc,Mp,Wp,Yp,Kp,K,a,tau,alpha,OFF,COV.a) #<-- alpha
#---- updating assignment vectors
update1 <- Cond_K_O(Yc,Mp,Wp,Yp,K,Pp,update0$alpha,sigma,OFF) #<-- Kc
#---- updating weights and lambda
update2 <- Cond_P(update1$Kc,K,lambda) #<-- Pc and Lambda
#---- Sampling Base Measure
# Check SD of the propsal distribution (!Adaptive sampler!)
a_prop <- NULL
a_acc <- NULL
COV.cov <- 2.38^2/((dim.cov+3))*(COV.med+diag(0.1^10,(dim.cov+3))) #<-- dim.cov = the dimension of the covariates.
a_prop <- c(rmnorm(1, a, COV.cov))
rat1 <- log(sum(a_prior$P*dmnorm(a_prop, a_prior$mean, a_prior$sigma))) + sum(sapply(1:K, function(x) sum(dnorm(update0$alpha[x,], a_prop, sqrt(1/tau), log=TRUE)))) + dmnorm(a, a_prop, COV.cov, log=TRUE)
rat2 <- log(sum(a_prior$P*dmnorm(a, a_prior$mean, a_prior$sigma))) + sum(sapply(1:K, function(x) sum(dnorm(update0$alpha[x,], a, sqrt(1/tau), log=TRUE)))) + dmnorm(a_prop, a, COV.cov, log=TRUE)
rat <- rat1 - rat2
if(is.na(rat) | log(runif(1))>rat){
a_prop <- a
}else{
a <- a_prop
}
tau_prop <- tau
tau_acc <- NULL
for(i in 1:(dim.cov+3)){
tau_prop[i] <- rgamma(1, tau[i]*100, 100)
rat1 <- sum(dgamma(tau_prop[i], 2, 1, log=TRUE))+ sum(sapply(1:K, function(x) sum(dnorm(update0$alpha[x,], a, sqrt(1/tau_prop), log=TRUE)))) + dgamma(tau[i], tau_prop[i]*100, 100, log=TRUE)
rat2 <- sum(dgamma(tau[i], 2, 1, log=TRUE)) + sum(sapply(1:K, function(x) sum(dnorm(update0$alpha[x,], a, sqrt(1/tau), log=TRUE)))) + dgamma(tau_prop[i], tau[i]*100, 100, log=TRUE)
rat <- rat1 - rat2
if(is.na(rat) | log(runif(1))>rat){
tau_prop[i] <- tau[i]
tau_acc <- 0
}else{
tau[i] <- tau_prop[i]
tau_acc <- 1
}
}
return(list(alpha=update0$alpha, tau=tau, tau_acc=tau_acc, a=a, a_acc=a_acc, Pc=update2$Pc, lambda=update2$lambda, Kc=update1$Kc))
}
#----- Load Data
load("simulated_data.RData")
load(file=paste0("data_",process+1,".RData"))
##### data at time 1 : TRT=(0) #####
Yc <- Y1[Z1==0]
Mp <- as.numeric(M1[Z1==0])
Yp <- log(Master$TD1[Z1==0]+1.5)
Wp <- cbind(X1, Master$PctUrban, log(Master$Ter+1))[Z1==0,]
OFF <- Master$TB3[Z1==0]*3
K <- 20
Kp <- sample(1:K,length(Yc),replace=TRUE)
#### Priors ###############
dim.cov <- dim(Wp)[2]
a_prior <- list()
a_prior$P <- 1
a_prior$mean <- rep(0,6)
a_prior$sigma <- diag(1000, dim.cov+3)
a <- a_prior$mean
alpha <- matrix(coef(glm(Yc~offset(log(OFF))+Mp+Yp+Wp, family=poisson)), nrow=K,ncol=dim.cov+3, byrow=TRUE)
#### Starting PMs ############
lambda <- 2
Pp <- rep(1/K,K)
COV.med <- vcov(glm(Yc~offset(log(OFF))+Mp+Yp+Wp, family=poisson))/dim.cov
# Setting the number of MCMC interations
MCMC<-6000
tau <- rep(0.5, dim.cov+3)
COV.a <- rbind(COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med)/100
update3 <- list()
update3[[1]] <- list(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kc=Kp,
a=a,
a_prior=a_prior,
tau=tau,
alpha=alpha,
lambda=lambda,
Pc=Pp,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
a_storage <- NULL
alpha1_storage <- NULL
alpha2_storage <- NULL
alpha3_storage <- NULL
alpha4_storage <- NULL
alpha5_storage <- NULL
alpha6_storage <- NULL
alpha7_storage <- NULL
alpha8_storage <- NULL
alpha9_storage <- NULL
alpha10_storage <- NULL
alpha11_storage <- NULL
alpha12_storage <- NULL
alpha13_storage <- NULL
alpha14_storage <- NULL
alpha15_storage <- NULL
alpha16_storage <- NULL
alpha17_storage <- NULL
alpha18_storage <- NULL
alpha19_storage <- NULL
alpha20_storage <- NULL
for(c in 2:MCMC){
a_storage <- rbind(a_storage, update3[[(c-1)]]$a)
if(c > 500){
COV.med <- cov(a_storage)
}
alpha1_storage <- rbind(alpha1_storage, update3[[(c-1)]]$alpha[1,])
alpha2_storage <- rbind(alpha2_storage, update3[[(c-1)]]$alpha[2,])
alpha3_storage <- rbind(alpha3_storage, update3[[(c-1)]]$alpha[3,])
alpha4_storage <- rbind(alpha4_storage, update3[[(c-1)]]$alpha[4,])
alpha5_storage <- rbind(alpha5_storage, update3[[(c-1)]]$alpha[5,])
alpha6_storage <- rbind(alpha6_storage, update3[[(c-1)]]$alpha[6,])
alpha7_storage <- rbind(alpha7_storage, update3[[(c-1)]]$alpha[7,])
alpha8_storage <- rbind(alpha8_storage, update3[[(c-1)]]$alpha[8,])
alpha9_storage <- rbind(alpha9_storage, update3[[(c-1)]]$alpha[9,])
alpha10_storage <- rbind(alpha10_storage, update3[[(c-1)]]$alpha[10,])
alpha11_storage <- rbind(alpha11_storage, update3[[(c-1)]]$alpha[11,])
alpha12_storage <- rbind(alpha12_storage, update3[[(c-1)]]$alpha[12,])
alpha13_storage <- rbind(alpha13_storage, update3[[(c-1)]]$alpha[13,])
alpha14_storage <- rbind(alpha14_storage, update3[[(c-1)]]$alpha[14,])
alpha15_storage <- rbind(alpha15_storage, update3[[(c-1)]]$alpha[15,])
alpha16_storage <- rbind(alpha16_storage, update3[[(c-1)]]$alpha[16,])
alpha17_storage <- rbind(alpha17_storage, update3[[(c-1)]]$alpha[17,])
alpha18_storage <- rbind(alpha18_storage, update3[[(c-1)]]$alpha[18,])
alpha19_storage <- rbind(alpha19_storage, update3[[(c-1)]]$alpha[19,])
alpha20_storage <- rbind(alpha20_storage, update3[[(c-1)]]$alpha[20,])
if(c > 500){
for(i in 1:20){
eval(parse(text=paste("COV.a[",(6*i-5),":",(6*i),",1:6] <- cov(alpha",i,"_storage)", sep="")))
}
}
update3[[c]] <- Cond_Main_O(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kp=update3[[c-1]]$Kc,
a=update3[[c-1]]$a,
a_prior=a_prior,
tau=update3[[c-1]]$tau,
alpha=update3[[c-1]]$alpha,
lambda=update3[[c-1]]$lambda,
Pp=update3[[c-1]]$Pc,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
}
out_time10 <- list()
for(ind in 1:2000){
IND <- ind+4000
out_time10[[ind]] <- list(a=update3[[IND]]$a,
tau=update3[[IND]]$tau,
alpha=update3[[IND]]$alpha,
Kc=update3[[IND]]$Kc)
}
rm(update3)
##### data at time 1 : TRT=(1) #####
Yc <- Y1[Z1==1]
Mp <- as.numeric(M1[Z1==1])
Yp <- log(Master$TD1[Z1==1]+1.5)
Wp <- cbind(X1, Master$PctUrban, log(Master$Ter+1))[Z1==1,]
OFF <- Master$TB3[Z1==1]*3
K <- 20
Kp <- sample(1:K,length(Yc),replace=TRUE)
#### Priors ###############
dim.cov <- dim(Wp)[2]
a_prior <- list()
a_prior$P <- 1
a_prior$mean <- rep(0,6)
a_prior$sigma <- diag(1000, dim.cov+3)
a <- a_prior$mean
alpha <- matrix(coef(glm(Yc~offset(log(OFF))+Mp+Yp+Wp, family=poisson)), nrow=K,ncol=dim.cov+3, byrow=TRUE)
#### Starting PMs ############
lambda <- 2
Pp <- rep(1/K,K)
COV.med <- vcov(glm(Yc~offset(log(OFF))+Mp+Yp+Wp, family=poisson))/dim.cov
# Setting the number of MCMC interations
MCMC<-6000
tau <- rep(0.5, dim.cov+3)
COV.a <- rbind(COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med)/100
update3 <- list()
update3[[1]] <- list(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kc=Kp,
a=a,
a_prior=a_prior,
tau=tau,
alpha=alpha,
lambda=lambda,
Pc=Pp,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
a_storage <- NULL
alpha1_storage <- NULL
alpha2_storage <- NULL
alpha3_storage <- NULL
alpha4_storage <- NULL
alpha5_storage <- NULL
alpha6_storage <- NULL
alpha7_storage <- NULL
alpha8_storage <- NULL
alpha9_storage <- NULL
alpha10_storage <- NULL
alpha11_storage <- NULL
alpha12_storage <- NULL
alpha13_storage <- NULL
alpha14_storage <- NULL
alpha15_storage <- NULL
alpha16_storage <- NULL
alpha17_storage <- NULL
alpha18_storage <- NULL
alpha19_storage <- NULL
alpha20_storage <- NULL
for(c in 2:MCMC){
a_storage <- rbind(a_storage, update3[[(c-1)]]$a)
if(c > 500){
COV.med <- cov(a_storage)
}
alpha1_storage <- rbind(alpha1_storage, update3[[(c-1)]]$alpha[1,])
alpha2_storage <- rbind(alpha2_storage, update3[[(c-1)]]$alpha[2,])
alpha3_storage <- rbind(alpha3_storage, update3[[(c-1)]]$alpha[3,])
alpha4_storage <- rbind(alpha4_storage, update3[[(c-1)]]$alpha[4,])
alpha5_storage <- rbind(alpha5_storage, update3[[(c-1)]]$alpha[5,])
alpha6_storage <- rbind(alpha6_storage, update3[[(c-1)]]$alpha[6,])
alpha7_storage <- rbind(alpha7_storage, update3[[(c-1)]]$alpha[7,])
alpha8_storage <- rbind(alpha8_storage, update3[[(c-1)]]$alpha[8,])
alpha9_storage <- rbind(alpha9_storage, update3[[(c-1)]]$alpha[9,])
alpha10_storage <- rbind(alpha10_storage, update3[[(c-1)]]$alpha[10,])
alpha11_storage <- rbind(alpha11_storage, update3[[(c-1)]]$alpha[11,])
alpha12_storage <- rbind(alpha12_storage, update3[[(c-1)]]$alpha[12,])
alpha13_storage <- rbind(alpha13_storage, update3[[(c-1)]]$alpha[13,])
alpha14_storage <- rbind(alpha14_storage, update3[[(c-1)]]$alpha[14,])
alpha15_storage <- rbind(alpha15_storage, update3[[(c-1)]]$alpha[15,])
alpha16_storage <- rbind(alpha16_storage, update3[[(c-1)]]$alpha[16,])
alpha17_storage <- rbind(alpha17_storage, update3[[(c-1)]]$alpha[17,])
alpha18_storage <- rbind(alpha18_storage, update3[[(c-1)]]$alpha[18,])
alpha19_storage <- rbind(alpha19_storage, update3[[(c-1)]]$alpha[19,])
alpha20_storage <- rbind(alpha20_storage, update3[[(c-1)]]$alpha[20,])
if(c > 500){
for(i in 1:20){
eval(parse(text=paste("COV.a[",(6*i-5),":",(6*i),",1:6] <- cov(alpha",i,"_storage)", sep="")))
}
}
update3[[c]] <- Cond_Main_O(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kp=update3[[c-1]]$Kc,
a=update3[[c-1]]$a,
a_prior=a_prior,
tau=update3[[c-1]]$tau,
alpha=update3[[c-1]]$alpha,
lambda=update3[[c-1]]$lambda,
Pp=update3[[c-1]]$Pc,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
}
out_time11 <- list()
for(ind in 1:2000){
IND <- ind+4000
out_time11[[ind]] <- list(a=update3[[IND]]$a,
tau=update3[[IND]]$tau,
alpha=update3[[IND]]$alpha,
Kc=update3[[IND]]$Kc)
}
rm(update3)
##### data at time 2 : TRT=(0,0) #####
Yc <- Y2[Z2==0]
Mp <- as.numeric(M2[Z2==0])
Yp <- log(Y1[Z2==0]+1.5)
Wp <- cbind(X2, Master$PctUrban, log(Master$Ter+1))[Z2==0,]
OFF <- Master$TB5[Z2==0]*3
K <- 20
Kp <- sample(1:K,length(Yc),replace=TRUE)
#### Priors ###############
dim.cov <- dim(Wp)[2]
a_past.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
IND <- ind+0
a_past.sample[ind,]<-out_time10[[IND]]$a
}
a_new.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
a_new.sample[ind,] <- a_past.sample[ind,]
}
a_prior$P <- 1
a_prior$mean <- apply(a_new.sample, 2, mean)
a_prior$sigma <- cov(a_new.sample)
a <- a_prior$mean
alpha <- matrix(0, nrow=K,ncol=dim.cov+3, byrow=TRUE)
lambda <- 2
Pp <- rep(1/K,K)
COV.med <- vcov(glm(Yc~offset(log(OFF))+Mp+Yp+Wp, family=poisson))/dim.cov
COV.a <- rbind(COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med)/100
tau <- rep(0.5, dim.cov+3)
# Setting the number of MCMC interations
MCMC<-6000
update3 <- list()
update3[[1]] <- list(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kc=Kp,
a=a,
a_prior=a_prior,
tau=tau,
alpha=alpha,
lambda=lambda,
Pc=Pp,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
a_storage <- NULL
alpha1_storage <- NULL
alpha2_storage <- NULL
alpha3_storage <- NULL
alpha4_storage <- NULL
alpha5_storage <- NULL
alpha6_storage <- NULL
alpha7_storage <- NULL
alpha8_storage <- NULL
alpha9_storage <- NULL
alpha10_storage <- NULL
alpha11_storage <- NULL
alpha12_storage <- NULL
alpha13_storage <- NULL
alpha14_storage <- NULL
alpha15_storage <- NULL
alpha16_storage <- NULL
alpha17_storage <- NULL
alpha18_storage <- NULL
alpha19_storage <- NULL
alpha20_storage <- NULL
for(c in 2:MCMC){
a_storage <- rbind(a_storage, update3[[(c-1)]]$a)
if(c > 500){
COV.med <- cov(a_storage)
}
alpha1_storage <- rbind(alpha1_storage, update3[[(c-1)]]$alpha[1,])
alpha2_storage <- rbind(alpha2_storage, update3[[(c-1)]]$alpha[2,])
alpha3_storage <- rbind(alpha3_storage, update3[[(c-1)]]$alpha[3,])
alpha4_storage <- rbind(alpha4_storage, update3[[(c-1)]]$alpha[4,])
alpha5_storage <- rbind(alpha5_storage, update3[[(c-1)]]$alpha[5,])
alpha6_storage <- rbind(alpha6_storage, update3[[(c-1)]]$alpha[6,])
alpha7_storage <- rbind(alpha7_storage, update3[[(c-1)]]$alpha[7,])
alpha8_storage <- rbind(alpha8_storage, update3[[(c-1)]]$alpha[8,])
alpha9_storage <- rbind(alpha9_storage, update3[[(c-1)]]$alpha[9,])
alpha10_storage <- rbind(alpha10_storage, update3[[(c-1)]]$alpha[10,])
alpha11_storage <- rbind(alpha11_storage, update3[[(c-1)]]$alpha[11,])
alpha12_storage <- rbind(alpha12_storage, update3[[(c-1)]]$alpha[12,])
alpha13_storage <- rbind(alpha13_storage, update3[[(c-1)]]$alpha[13,])
alpha14_storage <- rbind(alpha14_storage, update3[[(c-1)]]$alpha[14,])
alpha15_storage <- rbind(alpha15_storage, update3[[(c-1)]]$alpha[15,])
alpha16_storage <- rbind(alpha16_storage, update3[[(c-1)]]$alpha[16,])
alpha17_storage <- rbind(alpha17_storage, update3[[(c-1)]]$alpha[17,])
alpha18_storage <- rbind(alpha18_storage, update3[[(c-1)]]$alpha[18,])
alpha19_storage <- rbind(alpha19_storage, update3[[(c-1)]]$alpha[19,])
alpha20_storage <- rbind(alpha20_storage, update3[[(c-1)]]$alpha[20,])
if(c > 500){
for(i in 1:20){
eval(parse(text=paste("COV.a[",(6*i-5),":",(6*i),",1:6] <- cov(alpha",i,"_storage)", sep="")))
}
}
update3[[c]] <- Cond_Main_O(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kp=update3[[c-1]]$Kc,
a=update3[[c-1]]$a,
a_prior=a_prior,
tau=update3[[c-1]]$tau,
alpha=update3[[c-1]]$alpha,
lambda=update3[[c-1]]$lambda,
Pp=update3[[c-1]]$Pc,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
}
out_time20 <- list()
for(ind in 1:2000){
IND <- ind+4000
out_time20[[ind]] <- list(a=update3[[IND]]$a,
tau=update3[[IND]]$tau,
alpha=update3[[IND]]$alpha,
Kc=update3[[IND]]$Kc)
}
rm(update3)
##### data at time 2 : TRT=(0,1) #####
Yc <- Y2[Z2==1]
Mp <- as.numeric(M2[Z2==1])
Yp <- log(Y1[Z2==1]+1.5)
Wp <- cbind(X2, Master$PctUrban, log(Master$Ter+1))[Z2==1,]
OFF <- Master$TB5[Z2==1]*3
K <- 20
Kp <- sample(1:K,length(Yc),replace=TRUE)
#### Priors ###############
dim.cov <- dim(Wp)[2]
a_past.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
IND <- ind+0
a_past.sample[ind,]<-out_time10[[IND]]$a
}
a_new.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
a_new.sample[ind,] <- a_past.sample[ind,]
}
a_prior$P <- 1
a_prior$mean <- apply(a_new.sample, 2, mean)
a_prior$sigma <- cov(a_new.sample)
a <- a_prior$mean
alpha <- matrix(0, nrow=K,ncol=dim.cov+3, byrow=TRUE)
lambda <- 2
Pp <- rep(1/K,K)
COV.med <- vcov(glm(Yc~offset(log(OFF))+Mp+Yp+Wp, family=poisson))/dim.cov
COV.a <- rbind(COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med)/100
tau <- rep(0.5, dim.cov+3)
# Setting the number of MCMC interations
MCMC<-6000
update3 <- list()
update3[[1]] <- list(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kc=Kp,
a=a,
a_prior=a_prior,
tau=tau,
alpha=alpha,
lambda=lambda,
Pc=Pp,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
a_storage <- NULL
alpha1_storage <- NULL
alpha2_storage <- NULL
alpha3_storage <- NULL
alpha4_storage <- NULL
alpha5_storage <- NULL
alpha6_storage <- NULL
alpha7_storage <- NULL
alpha8_storage <- NULL
alpha9_storage <- NULL
alpha10_storage <- NULL
alpha11_storage <- NULL
alpha12_storage <- NULL
alpha13_storage <- NULL
alpha14_storage <- NULL
alpha15_storage <- NULL
alpha16_storage <- NULL
alpha17_storage <- NULL
alpha18_storage <- NULL
alpha19_storage <- NULL
alpha20_storage <- NULL
for(c in 2:MCMC){
a_storage <- rbind(a_storage, update3[[(c-1)]]$a)
if(c > 500){
COV.med <- cov(a_storage)
}
alpha1_storage <- rbind(alpha1_storage, update3[[(c-1)]]$alpha[1,])
alpha2_storage <- rbind(alpha2_storage, update3[[(c-1)]]$alpha[2,])
alpha3_storage <- rbind(alpha3_storage, update3[[(c-1)]]$alpha[3,])
alpha4_storage <- rbind(alpha4_storage, update3[[(c-1)]]$alpha[4,])
alpha5_storage <- rbind(alpha5_storage, update3[[(c-1)]]$alpha[5,])
alpha6_storage <- rbind(alpha6_storage, update3[[(c-1)]]$alpha[6,])
alpha7_storage <- rbind(alpha7_storage, update3[[(c-1)]]$alpha[7,])
alpha8_storage <- rbind(alpha8_storage, update3[[(c-1)]]$alpha[8,])
alpha9_storage <- rbind(alpha9_storage, update3[[(c-1)]]$alpha[9,])
alpha10_storage <- rbind(alpha10_storage, update3[[(c-1)]]$alpha[10,])
alpha11_storage <- rbind(alpha11_storage, update3[[(c-1)]]$alpha[11,])
alpha12_storage <- rbind(alpha12_storage, update3[[(c-1)]]$alpha[12,])
alpha13_storage <- rbind(alpha13_storage, update3[[(c-1)]]$alpha[13,])
alpha14_storage <- rbind(alpha14_storage, update3[[(c-1)]]$alpha[14,])
alpha15_storage <- rbind(alpha15_storage, update3[[(c-1)]]$alpha[15,])
alpha16_storage <- rbind(alpha16_storage, update3[[(c-1)]]$alpha[16,])
alpha17_storage <- rbind(alpha17_storage, update3[[(c-1)]]$alpha[17,])
alpha18_storage <- rbind(alpha18_storage, update3[[(c-1)]]$alpha[18,])
alpha19_storage <- rbind(alpha19_storage, update3[[(c-1)]]$alpha[19,])
alpha20_storage <- rbind(alpha20_storage, update3[[(c-1)]]$alpha[20,])
if(c > 500){
for(i in 1:20){
eval(parse(text=paste("COV.a[",(6*i-5),":",(6*i),",1:6] <- cov(alpha",i,"_storage)", sep="")))
}
}
update3[[c]] <- Cond_Main_O(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kp=update3[[c-1]]$Kc,
a=update3[[c-1]]$a,
a_prior=a_prior,
tau=update3[[c-1]]$tau,
alpha=update3[[c-1]]$alpha,
lambda=update3[[c-1]]$lambda,
Pp=update3[[c-1]]$Pc,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
print(c);print(update3[[c-1]]$alpha);print(table(update3[[c-1]]$Kc))
}
out_time21 <- list()
for(ind in 1:2000){
IND <- ind+4000
out_time21[[ind]] <- list(a=update3[[IND]]$a,
tau=update3[[IND]]$tau,
alpha=update3[[IND]]$alpha,
Kc=update3[[IND]]$Kc)
}
rm(update3)
##### data at time 3 : TRT=(0,0,0) #####
Yc <- Y3[Z3==0]
Mp <- as.numeric(M3[Z3==0])
Yp <- log(Y2[Z3==0]+1.5)
Wp <- cbind(X3, Master$PctUrban, log(Master$Ter+1))[Z3==0,]
OFF <- Master$TB7[Z3==0]*3
K <- 20
Kp <- sample(1:K,length(Yc),replace=TRUE)
#### Priors ###############
dim.cov <- dim(Wp)[2]
a_past.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
IND <- ind+0
a_past.sample[ind,]<-out_time20[[IND]]$a
}
a_new.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
a_new.sample[ind,] <- a_past.sample[ind,]
}
a_prior$P <- 1
a_prior$mean <- apply(a_new.sample, 2, mean)
a_prior$sigma <- cov(a_new.sample)
a <- a_prior$mean
alpha <- matrix(0, nrow=K,ncol=dim.cov+3, byrow=TRUE)
lambda <- 2
Pp <- rep(1/K,K)
COV.med <- vcov(glm(Yc~offset(log(OFF))+Mp+Yp+Wp, family=poisson))/dim.cov
COV.a <- rbind(COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med)/100
tau <- rep(0.5, dim.cov+3)
# Setting the number of MCMC interations
MCMC<-6000
update3 <- list()
update3[[1]] <- list(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kc=Kp,
a=a,
a_prior=a_prior,
tau=tau,
alpha=alpha,
lambda=lambda,
Pc=Pp,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
a_storage <- NULL
alpha1_storage <- NULL
alpha2_storage <- NULL
alpha3_storage <- NULL
alpha4_storage <- NULL
alpha5_storage <- NULL
alpha6_storage <- NULL
alpha7_storage <- NULL
alpha8_storage <- NULL
alpha9_storage <- NULL
alpha10_storage <- NULL
alpha11_storage <- NULL
alpha12_storage <- NULL
alpha13_storage <- NULL
alpha14_storage <- NULL
alpha15_storage <- NULL
alpha16_storage <- NULL
alpha17_storage <- NULL
alpha18_storage <- NULL
alpha19_storage <- NULL
alpha20_storage <- NULL
for(c in 2:MCMC){
a_storage <- rbind(a_storage, update3[[(c-1)]]$a)
if(c > 500){
COV.med <- cov(a_storage)
}
alpha1_storage <- rbind(alpha1_storage, update3[[(c-1)]]$alpha[1,])
alpha2_storage <- rbind(alpha2_storage, update3[[(c-1)]]$alpha[2,])
alpha3_storage <- rbind(alpha3_storage, update3[[(c-1)]]$alpha[3,])
alpha4_storage <- rbind(alpha4_storage, update3[[(c-1)]]$alpha[4,])
alpha5_storage <- rbind(alpha5_storage, update3[[(c-1)]]$alpha[5,])
alpha6_storage <- rbind(alpha6_storage, update3[[(c-1)]]$alpha[6,])
alpha7_storage <- rbind(alpha7_storage, update3[[(c-1)]]$alpha[7,])
alpha8_storage <- rbind(alpha8_storage, update3[[(c-1)]]$alpha[8,])
alpha9_storage <- rbind(alpha9_storage, update3[[(c-1)]]$alpha[9,])
alpha10_storage <- rbind(alpha10_storage, update3[[(c-1)]]$alpha[10,])
alpha11_storage <- rbind(alpha11_storage, update3[[(c-1)]]$alpha[11,])
alpha12_storage <- rbind(alpha12_storage, update3[[(c-1)]]$alpha[12,])
alpha13_storage <- rbind(alpha13_storage, update3[[(c-1)]]$alpha[13,])
alpha14_storage <- rbind(alpha14_storage, update3[[(c-1)]]$alpha[14,])
alpha15_storage <- rbind(alpha15_storage, update3[[(c-1)]]$alpha[15,])
alpha16_storage <- rbind(alpha16_storage, update3[[(c-1)]]$alpha[16,])
alpha17_storage <- rbind(alpha17_storage, update3[[(c-1)]]$alpha[17,])
alpha18_storage <- rbind(alpha18_storage, update3[[(c-1)]]$alpha[18,])
alpha19_storage <- rbind(alpha19_storage, update3[[(c-1)]]$alpha[19,])
alpha20_storage <- rbind(alpha20_storage, update3[[(c-1)]]$alpha[20,])
if(c > 500){
for(i in 1:20){
eval(parse(text=paste("COV.a[",(6*i-5),":",(6*i),",1:6] <- cov(alpha",i,"_storage)", sep="")))
}
}
update3[[c]] <- Cond_Main_O(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kp=update3[[c-1]]$Kc,
a=update3[[c-1]]$a,
a_prior=a_prior,
tau=update3[[c-1]]$tau,
alpha=update3[[c-1]]$alpha,
lambda=update3[[c-1]]$lambda,
Pp=update3[[c-1]]$Pc,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
}
out_time30 <- list()
for(ind in 1:2000){
IND <- ind+4000
out_time30[[ind]] <- list(a=update3[[IND]]$a,
tau=update3[[IND]]$tau,
alpha=update3[[IND]]$alpha,
Kc=update3[[IND]]$Kc)
}
rm(update3)
##### data at time 3 : TRT=(0,0,1) #####
Yc <- Y3[Z3==1]
Mp <- as.numeric(M3[Z3==1])
Yp <- log(Y2[Z3==1]+1.5)
Wp <- cbind(X3, Master$PctUrban, log(Master$Ter+1))[Z3==1,]
OFF <- Master$TB7[Z3==1]*3
K <- 20
Kp <- sample(1:K,length(Yc),replace=TRUE)
#### Priors ###############
dim.cov <- dim(Wp)[2]
a_past.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
IND <- ind+0
a_past.sample[ind,]<-out_time20[[IND]]$a
}
a_new.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
a_new.sample[ind,] <- a_past.sample[ind,]
}
a_prior$P <- 1
a_prior$mean <- apply(a_new.sample, 2, mean)
a_prior$sigma <- cov(a_new.sample)
a <- a_prior$mean
alpha <- matrix(0, nrow=K,ncol=dim.cov+3, byrow=TRUE)
lambda <- 2
Pp <- rep(1/K,K)
COV.med <- vcov(glm(Yc~offset(log(OFF))+Mp+Yp+Wp, family=poisson))/dim.cov
COV.a <- rbind(COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med)/100
tau <- rep(0.5, dim.cov+3)
# Setting the number of MCMC interations
MCMC<-6000
update3 <- list()
update3[[1]] <- list(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kc=Kp,
a=a,
a_prior=a_prior,
tau=tau,
alpha=alpha,
lambda=lambda,
Pc=Pp,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
a_storage <- NULL
alpha1_storage <- NULL
alpha2_storage <- NULL
alpha3_storage <- NULL
alpha4_storage <- NULL
alpha5_storage <- NULL
alpha6_storage <- NULL
alpha7_storage <- NULL
alpha8_storage <- NULL
alpha9_storage <- NULL
alpha10_storage <- NULL
alpha11_storage <- NULL
alpha12_storage <- NULL
alpha13_storage <- NULL
alpha14_storage <- NULL
alpha15_storage <- NULL
alpha16_storage <- NULL
alpha17_storage <- NULL
alpha18_storage <- NULL
alpha19_storage <- NULL
alpha20_storage <- NULL
for(c in 2:MCMC){
a_storage <- rbind(a_storage, update3[[(c-1)]]$a)
if(c > 500){
COV.med <- cov(a_storage)
}
alpha1_storage <- rbind(alpha1_storage, update3[[(c-1)]]$alpha[1,])
alpha2_storage <- rbind(alpha2_storage, update3[[(c-1)]]$alpha[2,])
alpha3_storage <- rbind(alpha3_storage, update3[[(c-1)]]$alpha[3,])
alpha4_storage <- rbind(alpha4_storage, update3[[(c-1)]]$alpha[4,])
alpha5_storage <- rbind(alpha5_storage, update3[[(c-1)]]$alpha[5,])
alpha6_storage <- rbind(alpha6_storage, update3[[(c-1)]]$alpha[6,])
alpha7_storage <- rbind(alpha7_storage, update3[[(c-1)]]$alpha[7,])
alpha8_storage <- rbind(alpha8_storage, update3[[(c-1)]]$alpha[8,])
alpha9_storage <- rbind(alpha9_storage, update3[[(c-1)]]$alpha[9,])
alpha10_storage <- rbind(alpha10_storage, update3[[(c-1)]]$alpha[10,])
alpha11_storage <- rbind(alpha11_storage, update3[[(c-1)]]$alpha[11,])
alpha12_storage <- rbind(alpha12_storage, update3[[(c-1)]]$alpha[12,])
alpha13_storage <- rbind(alpha13_storage, update3[[(c-1)]]$alpha[13,])
alpha14_storage <- rbind(alpha14_storage, update3[[(c-1)]]$alpha[14,])
alpha15_storage <- rbind(alpha15_storage, update3[[(c-1)]]$alpha[15,])
alpha16_storage <- rbind(alpha16_storage, update3[[(c-1)]]$alpha[16,])
alpha17_storage <- rbind(alpha17_storage, update3[[(c-1)]]$alpha[17,])
alpha18_storage <- rbind(alpha18_storage, update3[[(c-1)]]$alpha[18,])
alpha19_storage <- rbind(alpha19_storage, update3[[(c-1)]]$alpha[19,])
alpha20_storage <- rbind(alpha20_storage, update3[[(c-1)]]$alpha[20,])
if(c > 500){
for(i in 1:20){
eval(parse(text=paste("COV.a[",(6*i-5),":",(6*i),",1:6] <- cov(alpha",i,"_storage)", sep="")))
}
}
update3[[c]] <- Cond_Main_O(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kp=update3[[c-1]]$Kc,
a=update3[[c-1]]$a,
a_prior=a_prior,
tau=update3[[c-1]]$tau,
alpha=update3[[c-1]]$alpha,
lambda=update3[[c-1]]$lambda,
Pp=update3[[c-1]]$Pc,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,
OFF=OFF)
}
out_time31 <- list()
for(ind in 1:2000){
IND <- ind+4000
out_time31[[ind]] <- list(a=update3[[IND]]$a,
tau=update3[[IND]]$tau,
alpha=update3[[IND]]$alpha,
Kc=update3[[IND]]$Kc)
}
rm(update3)
##### data at time 4 : TRT=(0,0,0,0) #####
Yc <- Y4[Z4==0]
Mp <- as.numeric(M4[Z4==0])
Yp <- log(Y3[Z4==0]+1.5)
Wp <- cbind(X4, Master$PctUrban, log(Master$Ter+1))[Z4==0,]
OFF <- Master$TB9[Z4==0]*3
K <- 20
Kp <- sample(1:K,length(Yc),replace=TRUE)
#### Priors ###############
dim.cov <- dim(Wp)[2]
a_past.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
IND <- ind+0
a_past.sample[ind,]<-out_time30[[IND]]$a
}
a_new.sample <- matrix(nrow=2000, ncol=6)
for(ind in 1:2000){
a_new.sample[ind,] <- a_past.sample[ind,]
}
a_prior$P <- 1
a_prior$mean <- apply(a_new.sample, 2, mean)
a_prior$sigma <- cov(a_new.sample)
a <- a_prior$mean
alpha <- matrix(0, nrow=K,ncol=dim.cov+3, byrow=TRUE)
lambda <- 2
Pp <- rep(1/K,K)
COV.med <- vcov(glm(Yc~offset(log(OFF))+Mp+Yp+Wp, family=poisson))/dim.cov
COV.a <- rbind(COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med,COV.med)/100
tau <- rep(0.5, dim.cov+3)
# Setting the number of MCMC interations
MCMC<-6000
COV.tau <- diag(0.05, dim.cov+3)
update3 <- list()
update3[[1]] <- list(Yc=Yc,
Mp=Mp,
Yp=Yp,
Wp=Wp,
K=K,
Kc=Kp,
a=a,
a_prior=a_prior,
tau=tau,
alpha=alpha,
lambda=lambda,
Pc=Pp,
dim.cov=dim.cov,
COV.med=COV.med,
COV.a=COV.a,