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Effect.R
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Effect.R
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##---------------------------------------------------------------
## Required libraries
##---------------------------------------------------------------
#----- Parallel computing
library(doParallel)
#----- Make clusters based on the number of CPU cores
cl<-makeCluster(4) # 64 cores
registerDoParallel(cl)
getDoParWorkers()
#----- Support parallel excution
library(foreach)
library(data.table)
library(mnormt)
library(sn)
load("simulated_data.RData")
Y10.T1 <- NULL
Y00.T1 <- NULL
Y11.T1 <- NULL
Y10.T2 <- NULL
Y00.T2 <- NULL
Y11.T2 <- NULL
Y10.T3 <- NULL
Y00.T3 <- NULL
Y11.T3 <- NULL
Y10.T4 <- NULL
Y00.T4 <- NULL
Y11.T4 <- NULL
main <- function(temp){
eval(parse(text=paste0("load('Sim_Cov_",temp-1,".RData')")))
eval(parse(text=paste0("load('Sim_Med_",temp-1,".RData')")))
eval(parse(text=paste0("load('Sim_Out_",temp-1,".RData')")))
W0 <- cbind(Master$TTEMP3, Master$PctUrban, log(Master$Ter+1))
W00 <- cbind(Master$TTEMP1, Master$PctUrban, log(Master$Ter+1))
off <- Master$TB3*3
off0 <- Master$TB1*3
off1 <- Master$TB3*3
off2 <- Master$TB5*3
off3 <- Master$TB7*3
off4 <- Master$TB9*3
MC.i <- dim(W0)[1]
Y10.t1 <- NULL
Y00.t1 <- NULL
Y11.t1 <- NULL
Y10.t2 <- NULL
Y00.t2 <- NULL
Y11.t2 <- NULL
Y10.t3 <- NULL
Y00.t3 <- NULL
Y11.t3 <- NULL
Y10.t4 <- NULL
Y00.t4 <- NULL
Y11.t4 <- NULL
for(I in 1:200){
ind <- I*10
t.ind <- sort(unique(med_time10[[ind]]$Kc))
prob <- (table(factor(med_time10[[ind]]$Kc, levels=1:20))/length(med_time10[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(med_time10[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
m1.t1 <- rnorm(MC.i, rowSums(cbind(1,as.numeric(Master$PM1),W0)*(med_time10[[ind]]$alpha[t.ind,])),sqrt((1/med_time10[[ind]]$sigma[t.ind])) )
t.ind <- sort(unique(med_time11[[ind]]$Kc))
prob <- (table(factor(med_time11[[ind]]$Kc, levels=1:20))/length(med_time11[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(med_time11[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
m1 <- rnorm(MC.i, rowSums(cbind(1,as.numeric(Master$PM1),W0)*(med_time11[[ind]]$alpha[t.ind,])),sqrt((1/med_time11[[ind]]$sigma[t.ind])) )
t.ind <- sort(unique(cov_time10[[ind]]$Kc))
prob <- (table(factor(cov_time10[[ind]]$Kc, levels=1:20))/length(cov_time10[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(cov_time10[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
w1 <- rnorm(MC.i, rowSums(cbind(1,m1.t1,W0)*(cov_time10[[ind]]$alpha[t.ind,])),sqrt((1/cov_time10[[ind]]$sigma[t.ind])) )
W1 <- W0
W1[,1] <- w1
t.ind <- sort(unique(out_time11[[ind]]$Kc))
prob <- (table(factor(out_time11[[ind]]$Kc, levels=1:20))/length(out_time11[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(out_time11[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
y10.t1 <- rpois(length(m1.t1),exp(log(off1)+rowSums(cbind(1,m1.t1,log(Master$TD1+1.5),W0)*(out_time11[[ind]]$alpha[t.ind,]))))
Y10.t1[I] <- mean(y10.t1/off1)
y11.t1 <- rpois(length(m1.t1),exp(log(off1)+rowSums(cbind(1,m1,log(Master$TD1+1.5),W0)*(out_time11[[ind]]$alpha[t.ind,]))))
Y11.t1[I] <- mean(y11.t1/off1)
t.ind <- sort(unique(out_time10[[ind]]$Kc))
prob <- (table(factor(out_time10[[ind]]$Kc, levels=1:20))/length(out_time10[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(out_time10[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
y00.t1 <- rpois(length(m1.t1),exp(log(off1)+rowSums(cbind(1,m1.t1,log(Master$TD1+1.5),W0)*(out_time10[[ind]]$alpha[t.ind,]))))
Y00.t1[I] <- mean(y00.t1/off1)
##### T=2
t.ind <- sort(unique(med_time20[[ind]]$Kc))
prob <- (table(factor(med_time20[[ind]]$Kc, levels=1:20))/length(med_time20[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(med_time20[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
m2.t2 <- rnorm(MC.i, rowSums(cbind(1,m1.t1,W1)*(med_time20[[ind]]$alpha[t.ind,])),sqrt((1/med_time20[[ind]]$sigma[t.ind])) )
t.ind <- sort(unique(med_time21[[ind]]$Kc))
prob <- (table(factor(med_time21[[ind]]$Kc, levels=1:20))/length(med_time21[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(med_time21[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
m2 <- rnorm(MC.i, rowSums(cbind(1,m1.t1,W1)*(med_time21[[ind]]$alpha[t.ind,])),sqrt((1/med_time21[[ind]]$sigma[t.ind])) )
t.ind <- sort(unique(out_time21[[ind]]$Kc))
prob <- (table(factor(out_time21[[ind]]$Kc, levels=1:20))/length(out_time21[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(out_time21[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
y10.t2 <-rpois(length(m1.t1),exp(log(off2)+rowSums(cbind(1,m2.t2,log(y00.t1+1.5),W1)*(out_time21[[ind]]$alpha[t.ind,]))))
Y10.t2[I] <-mean(y10.t2/off2)
y11.t2 <- rpois(length(m1.t1),exp(log(off2)+rowSums(cbind(1,m2,log(y00.t1+1.5),W1)*(out_time21[[ind]]$alpha[t.ind,]))))
Y11.t2[I] <- mean(y11.t2/off2)
t.ind <- sort(unique(out_time20[[ind]]$Kc))
prob <- (table(factor(out_time20[[ind]]$Kc, levels=1:20))/length(out_time20[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(out_time20[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
y00.t2 <- rpois(length(m1.t1),exp(log(off2)+rowSums(cbind(1,m2.t2,log(y00.t1+1.5),W1)*(out_time20[[ind]]$alpha[t.ind,]))))
Y00.t2[I] <- mean(y00.t2/off2)
####### t=3
t.ind <- sort(unique(cov_time20[[ind]]$Kc))
prob <- (table(factor(cov_time20[[ind]]$Kc, levels=1:20))/length(cov_time20[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(cov_time20[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
w2 <- rnorm(MC.i, rowSums(cbind(1,m2.t2,W1)*(cov_time20[[ind]]$alpha[t.ind,])),sqrt((1/cov_time20[[ind]]$sigma[t.ind])) )
W2 <- W1
W2[,1] <- w2
t.ind <- sort(unique(med_time30[[ind]]$Kc))
prob <- (table(factor(med_time30[[ind]]$Kc, levels=1:20))/length(med_time30[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(med_time30[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
m3.t3 <- rnorm(MC.i, rowSums(cbind(1,m2.t2,W2)*(med_time30[[ind]]$alpha[t.ind,])),sqrt((1/med_time30[[ind]]$sigma[t.ind])) )
t.ind <- sort(unique(med_time31[[ind]]$Kc))
prob <- (table(factor(med_time31[[ind]]$Kc, levels=1:20))/length(med_time31[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(med_time31[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
m3 <- rnorm(MC.i, rowSums(cbind(1,m2.t2,W2)*(med_time31[[ind]]$alpha[t.ind,])),sqrt((1/med_time31[[ind]]$sigma[t.ind])) )
t.ind <- sort(unique(out_time31[[ind]]$Kc))
prob <- (table(factor(out_time31[[ind]]$Kc, levels=1:20))/length(out_time31[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(out_time31[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
y10.t3 <- rpois(length(m1.t1),exp(log(off3)+rowSums(cbind(1,m3.t3,log(y00.t2+1.5),W2)*(out_time31[[ind]]$alpha[t.ind,]))))
Y10.t3[I] <- mean(y10.t3/off3)
y11.t3 <- rpois(length(m1.t1),exp(log(off3)+rowSums(cbind(1,m3,log(y00.t2+1.5),W2)*(out_time31[[ind]]$alpha[t.ind,]))))
Y11.t3[I] <- mean(y11.t3/off3)
t.ind <- sort(unique(out_time30[[ind]]$Kc))
prob <- (table(factor(out_time30[[ind]]$Kc, levels=1:20))/length(out_time30[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(out_time30[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
y00.t3 <- rpois(length(m1.t1),exp(log(off3)+rowSums(cbind(1,m3.t3,log(y00.t2+1.5),W2)*(out_time30[[ind]]$alpha[t.ind,]))))
Y00.t3[I] <- mean(y00.t3/off3)
####### t=4
t.ind <- sort(unique(cov_time30[[ind]]$Kc))
prob <- (table(factor(cov_time30[[ind]]$Kc, levels=1:20))/length(cov_time30[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(cov_time30[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
w3 <- rnorm(MC.i, rowSums(cbind(1,m3.t3,W2)*(cov_time30[[ind]]$alpha[t.ind,])),sqrt((1/cov_time30[[ind]]$sigma[t.ind])) )
W3 <- W2
W3[,1] <- w3
t.ind <- sort(unique(med_time40[[ind]]$Kc))
prob <- (table(factor(med_time40[[ind]]$Kc, levels=1:20))/length(med_time40[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(med_time40[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
m4.t4 <- rnorm(MC.i, rowSums(cbind(1,m3.t3,W3)*(med_time40[[ind]]$alpha[t.ind,])),sqrt((1/med_time40[[ind]]$sigma[t.ind])) )
t.ind <- sort(unique(med_time41[[ind]]$Kc))
prob <- (table(factor(med_time41[[ind]]$Kc, levels=1:20))/length(med_time41[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(med_time41[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
m4 <- rnorm(MC.i, rowSums(cbind(1,m3.t3,W3)*(med_time41[[ind]]$alpha[t.ind,])),sqrt((1/med_time41[[ind]]$sigma[t.ind])) )
t.ind <- sort(unique(out_time41[[ind]]$Kc))
prob <- (table(factor(out_time41[[ind]]$Kc, levels=1:20))/length(out_time41[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(out_time41[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
y10.t4 <- rpois(length(m1.t1),exp(log(off4)+rowSums(cbind(1,m4.t4,log(y00.t3+1.5),W3)*(out_time41[[ind]]$alpha[t.ind,]))))
Y10.t4[I] <- mean(y10.t4/off4)
y11.t4 <-rpois(length(m1.t1),exp(log(off4)+rowSums(cbind(1,m4,log(y00.t3+1.5),W3)*(out_time41[[ind]]$alpha[t.ind,]))))
Y11.t4[I] <- mean(y11.t4/off4)
t.ind <- sort(unique(out_time40[[ind]]$Kc))
prob <- (table(factor(out_time40[[ind]]$Kc, levels=1:20))/length(out_time40[[ind]]$Kc))[t.ind]
if(length(prob)==1){
t.ind <- rep(t.ind, MC.i)
}else{
t.ind <- sample(sort(unique(out_time40[[ind]]$Kc)),(MC.i), prob=prob, replace=TRUE)
}
y00.t4 <- rpois(length(m1.t1),exp(log(off4)+rowSums(cbind(1,m4.t4,log(y00.t3+1.5),W3)*(out_time40[[ind]]$alpha[t.ind,]))))
Y00.t4[I] <- mean(y00.t4/off4)
}
Y11.T1 <- mean(Y11.t1)
Y10.T1 <- mean(Y10.t1)
Y00.T1 <- mean(Y00.t1)
Y11.T2 <- mean(Y11.t2)
Y10.T2 <- mean(Y10.t2)
Y00.T2 <- mean(Y00.t2)
Y11.T3 <- mean(Y11.t3)
Y10.T3 <- mean(Y10.t3)
Y00.T3 <- mean(Y00.t3)
Y11.T4 <- mean(Y11.t4)
Y10.T4 <- mean(Y10.t4)
Y00.T4 <- mean(Y00.t4)
nie1 <- Y11.T1-Y10.T1
nie2 <- Y11.T2-Y10.T2
nie3 <- Y11.T3-Y10.T3
nie4 <- Y11.T4-Y10.T4
nde1 <- Y10.T1-Y00.T1
nde2 <- Y10.T2-Y00.T2
nde3 <- Y10.T3-Y00.T3
nde4 <- Y10.T4-Y00.T4
te1 <- nie1 + nde1
te2 <- nie2 + nde2
te3 <- nie3 + nde3
te4 <- nie4 + nde4
return(c(nie1,nie2,nie3,nie4,nde1,nde2,nde3,nde4,te1,te2,te3,te4))
}
result<-foreach(temp = c(1:400), .combine = rbind) %dopar% main(temp)
save(result, file="Sim_Result.RData")
stopCluster(cl)