########################################
## LIBRARIES
########################################

library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)

#######################################
## ANALYSIS FUNCTIONS
#######################################

pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
  nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
  resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
  if (method=='MML') {
    tam1 <-  tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
  }
  if (method=='JML') {
    tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
  }
  if (method!='MML' & method!='JML') {
    stop('Invalid method. Please choose among MML or JML')
  }
  return(tam1)
}

replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
  nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
  resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
  if (method=='MML') {
    n <- max(df[,sequence])
    print(n)
    tam1 <- pbmclapply(seq(1,n),
                   function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
                   )
  }
  listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
  returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
  colnames(returndat) <- listitems
  for (s in seq(1,max(df[,sequence]))) {
  for (k in seq(1,nbitems)) {
    returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
  }
  }
  returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
  returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
  returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
  returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
  returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
  returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
  if (truebeta==0) {
    returndat$beta.same.sign.truebeta <- NA
  } else {
    returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
  }
  returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
                           M=1+max(df$item1),
                           N=nrow(df[df$replication==1,])/2,
                           eff.size=eff.size,
                           dif.size= difsize,
                           nb.dif= nbdif
                           )
  returndat <- cbind(returndat2,returndat)
  return(returndat)
}

#######################################
## SCENARIO ANALYSIS
#######################################

registerDoMC(4)

######### Scenario 1: J=4 / M=2 

#### A: H0 TRUE

dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_1A_200.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_1A_300.csv')

res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis(get(x)))

write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_1A_100.csv')
write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_1A_200.csv')
write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_1A_300.csv')





######### Scenario 3: J=7 / M=2 

#### A: H0 TRUE

dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_3A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_3A_200.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_3A_300.csv')

res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis(get(x)))

write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_3A_100.csv')
write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_3A_200.csv')
write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_3A_300.csv')