# NOT RUN {
set.seed(150)
library(MASS)
var2=abs(rnorm(800,0,1));treatment=c(rep(0,400),rep(1,400));
var1=(1/0.85)*var2+2*treatment;
geneid=rep(c(1:50),16);sid=c(rep(c(1:25),16),rep(c(26:50),16));
cov1=rWishart(1,df=50,Sigma=diag(rep(1,50)))
u=rnorm(50,0,1);mu=mvrnorm(n=1,mu=u,cov1[,,1])
sdd=rgamma(1,shape=1,scale=1/10);
for (i in 1:800) {var1[i]=var1[i]+rnorm(1,mu[geneid[i]],sdd)}
miss_logit=var2*(-0.9)+var1*(0.001);
miss=rbinom(800,1,exp(miss_logit)/(exp(miss_logit)+1));
censor=rep(0,800)
for (i in 1:800) {if (var1[i]<0.002) censor[i]=1}
pdata=data.frame(var1,var2,treatment,miss,censor,geneid,sid);
for ( i in 1:800) {if ((pdata$miss[i]==1) & (pdata$censor[i]==1)) pdata$miss[i]=0};
for ( i in 1:800) {if (pdata$miss[i]==1) pdata$var1[i]=NA;
if (pdata$censor[i]==1) pdata$var1[i]=0.002};
pidname="geneid";sidname="sid";
#copy and paste the following formulas to the mmlm() function respectively
formula_completed=var1~var2+treatment;
formula_missing=miss~var2;
formula_censor=censor~1;
formula_subject=~treatment;
response_censorlim=0.002;
pathdir=getwd()
fp=system.file("demo",package="mlmm")
mcfit=readRDS(paste0(fp,"/demo/mlmc.rds"))
model1=mlmc(formula_completed=var1~var2+treatment,formula_missing=miss~var2,
formula_censor=censor~1,formula_subject=~treatment,pdata=pdata,response_censorlim=0.002,
respond_dep_missing=TRUE,pidname="geneid",sidname="sid",pathname=pathdir,
iterno=5,chains=1,savefile=FALSE,usefit=T,stanfit=mcfit)
# }
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