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ZS_MSNBH_no_spatial.R
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ZS_MSNBH_no_spatial.R
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############################################
### ZS-MSNBH without spatial correlation ###
############################################
library(readxl)
CHIKV2015 <- read_excel("/Users/xumingchi/Desktop/chikv-csv/CHIKV2015.xlsx")
CHIKV2016 <- read_excel("/Users/xumingchi/Desktop/chikv-csv/CHIKV2016.xlsx")
CHIKV2017 <- read_excel("/Users/xumingchi/Desktop/chikv-csv/CHIKV2017.xlsx")
CHIKV2018 <- read_excel("/Users/xumingchi/Desktop/chikv-csv/CHIKV2018.xlsx")
CHIKV2019 <- read_excel("/Users/xumingchi/Desktop/chikv-csv/CHIKV2019.xlsx")
CHIKV2020 <- read_excel("/Users/xumingchi/Desktop/chikv-csv/CHIKV2020.xlsx")
CHIKV2021 <- read_excel("/Users/xumingchi/Desktop/chikv-csv/CHIKV2021.xlsx")
CHIKV2022 <- read_excel("/Users/xumingchi/Desktop/chikv-csv/CHIKV2022.xlsx")
# extra covariates data
area.level<-read.csv("/Users/xumingchi/Desktop/chikv-csv/DistrictCovariates.csv")
# then merge these data
datCHIKV<-merge.data.frame(CHIKV2015,CHIKV2016,by.x ="District",by.y="District",
suffixes = c("/15","/16") ,sort = F)
datCHIKV<-merge.data.frame(datCHIKV,CHIKV2017,by.x ="District",by.y="District",sort = F)
datCHIKV<-merge.data.frame(datCHIKV,CHIKV2018,by.x ="District",by.y="District",
suffixes = c("/17","/18") ,sort = F)
datCHIKV<-merge.data.frame(datCHIKV,CHIKV2019,by.x ="District",by.y="District",sort = F)
datCHIKV<-merge.data.frame(datCHIKV,CHIKV2020,by.x ="District",by.y="District",
suffixes = c("/19","/20") ,sort = F)
colnames(datCHIKV)[length(colnames(datCHIKV))]<-"53/20"
datCHIKV<-merge.data.frame(datCHIKV,CHIKV2021,by.x ="District",by.y="District",
suffixes=c("/20","/21"),sort = F)
datCHIKV<-merge.data.frame(datCHIKV,CHIKV2022,by.x ="District",by.y="District",
suffixes = c("/21","/22") ,sort = F)
datCHIKV<-datCHIKV[,-c(386:418)]
colnames(datCHIKV)[c(334:366)]<-c(paste0(seq(from=20,to=52,by=1),"/21"))
############
# we choose to delete "Paqueta", then
dataCHIKV<-datCHIKV[-c(14),]
cases<-matrix(seq(1,159*384,by=1),nrow=159)
cases<-as.data.frame(cases)
for (i in 1:159){
cases[i,]<-as.numeric(dataCHIKV[i,][-1])
}
cases<-as.matrix(cases)
library(nimble)
HDI <- area.level$HDI
#N_i
N <- area.level$Npop
#population in thousands
pop <- N/1000
# green area
greenarea<- area.level$green_area
# time and sin/cos of time
time<-seq(1,384,by=1)
sin.time<-sin(time*2*pi/52)
cos.time<-cos(time*2*pi/52)
# def functions
dhurdleNegBinMarg <- nimbleFunction(
run = function(x = double(0), p = double(0), lambda = double(0), r= double(0),
log = integer(0, default = 0)) {
returnType(double(0))
if(x==0){
logProb <- log(1-p)
}else if(x>0){
new.p<- r/(r+lambda)
logProb <- log(p)+ dnbinom(x, size=r ,prob= new.p, log=TRUE)
- log(1-(new.p)^r)
}else{
logProb <- -Inf
}
if(log) return(logProb)
else return(exp(logProb))
})
#now the rhurdlePoisMarg
rhurdleNegBinMarg <- nimbleFunction(
run = function(n = integer(0), p = double(0), lambda = double(0), r=double(0)) {
returnType(double(0))
if(n != 1) print("rmixunif only allows n = 1; using n = 1.")
xt <- rbinom(n=1,size=1,prob=p)
new.p<- r/(r+lambda)
if(xt==1){
#xsim <- rpois(n=1,lambda=lambda)
xsim <- rnbinom(n=1, size=r, prob= new.p)
while(xsim==0){
#xsim <- rpois(n=1,lambda=lambda)
xsim <- rnbinom(n=1, size=r, prob=new.p)
}
}else if(xt==0){
xsim <- 0
}
return(xsim)
})
assign('dhurdleNegBinMarg', dhurdleNegBinMarg, envir = .GlobalEnv)
assign('rhurdleNegBinMarg', rhurdleNegBinMarg, envir = .GlobalEnv)
dhurdleNegBinMargV <- Vectorize(dhurdleNegBinMarg)
rhurdleNegBinMargV <- Vectorize(rhurdleNegBinMarg)
assign('dhurdleNegBinMargV', dhurdleNegBinMargV, envir = .GlobalEnv)
assign('rhurdleNegBinMargV', rhurdleNegBinMargV, envir = .GlobalEnv)
set.seed(1993)
# define X
X<- matrix(rep(0,159*384),nrow=159,ncol=384)
X[cases>0] <- 1
lpsi <- log(cases+1)
mlpsi <- mean(log(cases+1))
dengeeConsts <- list(N=159,T=384,
pop=pop,HDI=HDI, greenarea=greenarea,
psi =cases,mpsi=mean(cases),mlpsi=mean(log(cases+1)),lpsi=log(cases+1),
sin.time=sin.time,
cos.time=cos.time)
dengeeData <- list(y=cases,X=X)
parallel_nimble <- function(dengeeData, dengeeConsts,
n_iterations,
n_chains,
warm_up,
n_thin,N,T) {
require(nimble)
# first we define 2 functions for NB models
dhurdleNegBinMarg <- nimbleFunction(
run = function(x = double(0), p = double(0), lambda = double(0), r= double(0),
log = integer(0, default = 0)) {
returnType(double(0))
if(x==0){
logProb <- log(1-p)
}else if(x>0){
new.p<- r/(r+lambda)
logProb <- log(p)+ dnbinom(x, size=r ,prob= new.p, log=TRUE)
- log(1-(new.p)^r)
}else{
logProb <- -Inf
}
if(log) return(logProb)
else return(exp(logProb))
})
#now the rhurdlePoisMarg
rhurdleNegBinMarg <- nimbleFunction(
run = function(n = integer(0), p = double(0), lambda = double(0), r=double(0)) {
returnType(double(0))
if(n != 1) print("rmixunif only allows n = 1; using n = 1.")
xt <- rbinom(n=1,size=1,prob=p)
new.p<- r/(r+lambda)
if(xt==1){
#xsim <- rpois(n=1,lambda=lambda)
xsim <- rnbinom(n=1, size=r, prob= new.p)
while(xsim==0){
#xsim <- rpois(n=1,lambda=lambda)
xsim <- rnbinom(n=1, size=r, prob=new.p)
}
}else if(xt==0){
xsim <- 0
}
return(xsim)
})
assign('dhurdleNegBinMarg', dhurdleNegBinMarg, envir = .GlobalEnv)
assign('rhurdleNegBinMarg', rhurdleNegBinMarg, envir = .GlobalEnv)
dhurdleNegBinMargV <- Vectorize(dhurdleNegBinMarg)
rhurdleNegBinMargV <- Vectorize(rhurdleNegBinMarg)
assign('dhurdleNegBinMargV', dhurdleNegBinMargV, envir = .GlobalEnv)
assign('rhurdleNegBinMargV', rhurdleNegBinMargV, envir = .GlobalEnv)
# we have changes in the model defining
dengeeCode <- nimbleCode({
#priors
beta0 ~ dnorm(0,sd=100)
beta1 ~dnorm(0,sd=100)
beta2 ~ dnorm(0, sd=100)
beta3 ~ dnorm(0, sd=100)
beta4 ~ dnorm(0, sd=100)
beta5 ~ dnorm(0, sd=100)
beta6 ~ dnorm(0, sd=100)
alpha1 ~dnorm(0,sd=100)
alpha2 ~ dnorm(0, sd=100)
alpha3 ~ dnorm(0, sd=100)
alpha4 ~dnorm(0,sd=100)
alpha5 ~ dnorm(0, sd=100)
alpha6 ~ dnorm(0, sd=100)
alpha7 ~ dnorm(0, sd=100)
alpha8 ~ dnorm(0, sd=100)
alpha9 ~ dnorm(0, sd=100)
alpha10 ~ dnorm(0, sd=100)
alpha11 ~ dnorm(0, sd=100)
gamma1 ~ dnorm(0, sd=100)# green area
gamma2 ~ dnorm(0, sd=100)
gamma3 ~ dnorm(0, sd=100)
# gen r for NB
#for (i in 1:N){
# lr[i] ~ dnorm(lru[i],sd=lrsd[i])
# r[i]<- exp(lr[i])
#}
#r ~ dunif(0,100)# add this
rho~dnorm(0,sd=100)
for(i in 1:N){
b0[i]~dnorm(beta0+beta3*(pop[i]-mean(pop[1:N]))+beta4*(HDI[i]-mean(HDI[1:N]))+
beta6*(greenarea[i]-mean(greenarea[1:N])),prec_b0)# add betas
b[i]~dnorm(rho+(log(pop[i])-mean(log(pop[1:N])))*beta5,prec_b)
}
prec_b ~ dgamma(.1,.1)
sigma_b <- 1/sqrt(prec_b)
prec_b0 ~ dgamma(.1,.1)
sigma_b0 <- 1/sqrt(prec_b0)
#likelihood
for (i in 1:N){
for (t in 2:T){
# logistics regs: p01, p11 and r
logit(p01[i,t])<- alpha1+alpha2*(HDI[i]-mean(HDI[1:N]))+
alpha3*(pop[i]-mean(pop[1:N]))+gamma1*(greenarea[i]-mean(greenarea[1:N]))
logit(p11[i,t])<- alpha4+alpha5*(HDI[i]-mean(HDI[1:N]))+ alpha6*(pop[i]-mean(pop[1:N]))+
alpha7*(log(psi[i,t-1]+1)-mlpsi)+gamma2*(greenarea[i]-mean(greenarea[1:N]))
log(r[i,t])<- alpha8+ alpha9*(HDI[i]-mean(HDI[1:N]))+
alpha10*(pop[i]-mean(pop[1:N]))+
alpha11*(log(psi[i,t-1]+1)-mlpsi)+gamma3*(greenarea[i]-mean(greenarea[1:N]))
# mu
mup[i,t]<- exp(b0[i])*psi[i,t-1]+exp(b[i]+beta1*(sin.time[t]-mean(sin.time[]))+
beta2*(cos.time[t]-mean(cos.time[])))
# y
y[i,t] ~ dhurdleNegBinMarg(p=p01[i,t]*(1-X[i,t-1])+p11[i,t]*X[i,t-1],
lambda= mup[i,t],
r=r[i,t])
}
}
})
inits <- list(beta0 = rnorm(n=1,mean=0,sd=1),
beta1 = rnorm(n=1,mean=0,sd=.01),
beta2 = rnorm(n=1,mean=0,sd=.01) ,
beta3 = rnorm(n=1,mean=0,sd=.01) ,
beta4 = rnorm(n=1,mean=0,sd=.01) ,
beta5 = rnorm(n=1,mean=0,sd=.01) ,
beta6 = rnorm(n=1,mean=0,sd=.01) ,
alpha1 = rnorm(n=1,mean=0,sd=.01),
alpha2 = rnorm(n=1,mean=0,sd=.01) ,
alpha3 = rnorm(n=1,mean=0,sd=.01) ,
alpha4 = rnorm(n=1,mean=0,sd=.01),
alpha5 = rnorm(n=1,mean=0,sd=.01) ,
alpha6 = rnorm(n=1,mean=0,sd=.01) ,
alpha7 = rnorm(n=1,mean=0,sd=.01) ,
alpha8 = rnorm(n=1,mean=0,sd=.01) ,
alpha9 = rnorm(n=1,mean=0,sd=.01) ,
alpha10 = rnorm(n=1,mean=0,sd=.01) ,
alpha11 = rnorm(n=1,mean=0,sd=.01) ,
gamma1= rnorm(n=1,mean=0,sd=.01) ,
gamma2= rnorm(n=1,mean=0,sd=.01) ,
gamma3= rnorm(n=1,mean=0,sd=.01) ,
prec_b = 1,prec_b0 = 1,
rho=rnorm(n=1,mean=0,sd=1),
b=rnorm(n=159,mean=0,sd=.01),
b0=rnorm(n=159,mean=0,sd=.01))
dengeemodel <- nimbleModel(dengeeCode, dengeeConsts, dengeeData, inits)
Cdengee <- compileNimble(dengeemodel)
dengeeConf <- configureMCMC(dengeemodel, print = TRUE)
#sample sd on log scale
dengeeConf$removeSampler(c("sigma_b"))
dengeeConf$addSampler(target=c("sigma_b"),type="RW",control=list(log=TRUE))
print(dengeeConf)
dengeeConf$addMonitors(c("beta0","beta1","beta2","beta3","beta4","beta5","beta6",
"alpha1","alpha2","alpha3","alpha4","alpha5","alpha6","alpha7",
"alpha8","alpha9","alpha10","alpha11","gamma1","gamma2",
"gamma3","sigma_b0",
"rho","b","sigma_b","b0"))
dengeeMCMC <- buildMCMC(dengeeConf)
CdengeeMCMC <- compileNimble(dengeeMCMC, project = dengeemodel,resetFunctions = TRUE)
initsFunction <- function() list(beta0 = rnorm(n=1,mean=0,sd=1),
beta1 = rnorm(n=1,mean=0,sd=.01),
beta2 = rnorm(n=1,mean=0,sd=.01) ,
beta3 = rnorm(n=1,mean=0,sd=.01) ,
beta4 = rnorm(n=1,mean=0,sd=.01) ,
beta5 = rnorm(n=1,mean=0,sd=.01) ,
beta6 = rnorm(n=1,mean=0,sd=.01) ,
alpha1 = rnorm(n=1,mean=0,sd=.01),
alpha2 = rnorm(n=1,mean=0,sd=.01) ,
alpha3 = rnorm(n=1,mean=0,sd=.01) ,
alpha4 = rnorm(n=1,mean=0,sd=.01),
alpha5 = rnorm(n=1,mean=0,sd=.01) ,
alpha6 = rnorm(n=1,mean=0,sd=.01) ,
alpha7 = rnorm(n=1,mean=0,sd=.01) ,
alpha8 = rnorm(n=1,mean=0,sd=.01) ,
alpha9 = rnorm(n=1,mean=0,sd=.01) ,
alpha10 = rnorm(n=1,mean=0,sd=.01) ,
alpha11 = rnorm(n=1,mean=0,sd=.01) ,
gamma1= rnorm(n=1,mean=0,sd=.01) ,
gamma2= rnorm(n=1,mean=0,sd=.01) ,
gamma3= rnorm(n=1,mean=0,sd=.01) ,
prec_b = 1,prec_b0 = 1,
rho=rnorm(n=1,mean=0,sd=1),
b=rnorm(n=159,mean=0,sd=.01),
b0=rnorm(n=159,mean=0,sd=.01))
samples.ZSMSNBH.no.spatial <- runMCMC(CdengeeMCMC, niter =n_iterations,nchains = n_chains,nburnin=warm_up
,samplesAsCodaMCMC = TRUE,thin=n_thin,inits = initsFunction)
return(samples.ZSMSNBH.no.spatial)
}
library(parallel)
library(doParallel)
parallel::detectCores()
cl <- parallel::makeCluster(3,
outfile="/Users/xumingchi/Desktop/hurdle-codes/ZSMSNBH_parallel_no_spatial.txt")
doParallel::registerDoParallel(cl)
clusterExport(cl, list( "parallel_nimble"),
envir = globalenv())
fit_par <- foreach::foreach(i = 1:3,
.packages = c("nimble", "Rcpp")) %dopar% {
parallel_nimble(dengeeData, dengeeConsts,
n_iterations = 80000,
n_chains = 1,
warm_up =30000,
n_thin = 5,N=159,T=384)
}
parallel::stopCluster(cl)
#end_time <- Sys.time()
#end_time - start_time
#run_time <- end_time-start_time
library(coda)
combine_par_mcmc <- function(fit_par) {
n_par <- length(fit_par)
n_chain <- 1
out_lst <- list()
if (n_chain == lengths(fit_par)[1]) {
index <- 0
for (i in 1:n_par) {
for (j in 1:n_chain) {
index <- index + 1
trace_fit <- as.data.frame(do.call(rbind, fit_par[[i]][j]))
out_lst[[index]] <- coda::mcmc(trace_fit)
} }
val <- coda::mcmc.list(out_lst)
} else {
val <- coda::mcmc.list(fit_par)
}
return(val)
}
ZSMSNBH.no.spatial.samples <- combine_par_mcmc(fit_par)
save(ZSMSNBH.no.spatial.samples, file="/Users/xumingchi/Desktop/hurdle-codes/ZSMSNBH_no_spatial.RData")
load("/Users/xumingchi/Desktop/hurdle-codes/ZSMSNBH_no_spatial.RData")# load the samples
# check convergence
library(coda)
effectiveSize(ZSMSNBH.no.spatial.samples[,c("beta0","beta1","beta2","beta3","beta4","beta5","beta6","sigma_b","sigma_b0","rho",
"alpha1","alpha2","alpha3","alpha4","alpha5","alpha7")])
min(effectiveSize(ZSMSNBH.no.spatial.samples[,c("beta0","beta1","beta2","beta3","beta4","beta5","beta6","sigma_b","sigma_b0","rho")]))
gelman.diag(ZSMSNBH.no.spatial.samples[,c("beta0","beta1","beta2","beta3","beta4","beta5","beta6","sigma_b","sigma_b0","rho",
"alpha1","alpha2","alpha3","alpha4","alpha5","alpha7")],multivariate = FALSE)
#randomly check location specific parameters
gelman.diag(ZSMSNBH.no.spatial.samples[,c("b[1]","b[2]","b[3]","b[4]",
"b[21]","b[39]","b[112]","b[55]",
"b[23]","b[29]","b[15]","b[74]",
"b[41]","b[123]","b[16]","b[59]")],multivariate = FALSE)
gelman.diag(ZSMSNBH.no.spatial.samples[,c("b0[1]","b0[2]","b0[3]","b0[4]",
"b0[21]","b0[39]","b0[112]","b0[55]",
"b0[23]","b0[29]","b0[15]","b0[74]",
"b0[41]","b0[123]","b0[16]","b0[59]")],multivariate = FALSE)
# summary
summary(ZSMSNBH.no.spatial.samples[,c("beta0","beta1","beta2","beta3","beta4","beta5","beta6","sigma_b","sigma_b0","rho",
"alpha1","alpha2","alpha3","alpha4","alpha5","alpha7",
"alpha8","alpha9","alpha10","alpha11","gamma1","gamma2",
"gamma3")])
#---------------------------------
# 2. WAIC for ZS-MSNBH model
#---------------------------------
library(RMKdiscrete)
library(boot)
samps.ZSMSNBH.no.spatial<- data.frame(rbind(ZSMSNBH.no.spatial.samples[[1]],
ZSMSNBH.no.spatial.samples[[2]],
ZSMSNBH.no.spatial.samples[[3]]))
X<- matrix(rep(0,159*384),nrow=159,ncol=384)
X[cases>0] <- 1
lppd.h.no.spatial <- 0
pwaic.h.no.spatial <- 0
for(i in 1:159){
print(i)
for(t in 2:384){
# state the parameters
b0<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("b0.",i,".")]))
b<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("b.",i,".")]))
beta1<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("beta1")]))
beta2<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("beta2")]))
alpha1<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha1")]))
alpha2<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha2")]))
alpha3<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha3")]))
alpha4<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha4")]))
alpha5<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha5")]))
alpha6<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha6")]))
alpha7<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha7")]))
alpha8<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha8")]))
alpha9<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha9")]))
alpha10<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha10")]))
alpha11<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("alpha11")]))
gamma1<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("gamma1")]))
gamma2<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("gamma2")]))
gamma3<- as.numeric(unlist(samps.ZSMSNBH.no.spatial[paste0("gamma3")]))
# mup
mup<- exp(b0)*cases[i,t-1]+exp(b+beta1*(sin.time[t]-mean(sin.time[]))+
beta2*(cos.time[t]-mean(cos.time[])))
# r
r.samp<- exp(alpha8+ alpha9*(HDI[i]-mean(HDI[1:159]))+
alpha10*(pop[i]-mean(pop[1:159]))+
alpha11*(log(cases[i,t-1]+1)-mlpsi)+gamma3*(greenarea[i]-mean(greenarea[1:159])))
# p01 and p11
p01<- inv.logit(alpha1+alpha2*(HDI[i]-mean(HDI[1:159]))+
alpha3*(pop[i]-mean(pop[1:159]))+gamma1*(greenarea[i]-mean(greenarea[1:159])))
p11<- inv.logit(alpha4+alpha5*(HDI[i]-mean(HDI[1:159]))+ alpha6*(pop[i]-mean(pop[1:159]))+
alpha7*(log(cases[i,t-1]+1)-mlpsi)+gamma2*(greenarea[i]-mean(greenarea[1:159])))
# waic
lppd.h.no.spatial <- lppd.h.no.spatial + log(mean(dhurdleNegBinMarg(x = cases[i,t],
p=p01*(1-X[i,t-1])+p11*X[i,t-1],
lambda= mup, r=r.samp )))
pwaic.h.no.spatial<- pwaic.h.no.spatial+ var(log(dhurdleNegBinMarg(x = cases[i,t],
p=p01*(1-X[i,t-1])+p11*X[i,t-1],
lambda= mup, r=r.samp )))
}
}
waic.zsmsnbh.no.spatial <- -2*(lppd.h.no.spatial-pwaic.h.no.spatial)
# this is the end