R/RaschInt.R

In vthorrf/CIRM: Procedures for IRT modeling

RaschInt <- function(x,l){
  require(R2jags)

  ### Assemble data into list for JAGS====
  y = x
  itemID = colnames(y)
  subjID = rownames(y)
  Nitem = ncol(y)
  Nsubj = nrow(y)
  v = ncol(y)
  n = nrow(y)
  L = (l - 1)
  dataList = list( y=y , Nsubj=Nsubj, Nitem=Nitem , L=L)

  # Define the model====
  modelString = "
  model {
  for ( i in 1:Nsubj ) {
    for ( j in 1:Nitem ) {
      y[i,j] ~ dbin( pCorr[i,j] , L)
      pCorr[i,j] <- ilogit( T[i,j] )
      T[i,j] <- (Abil[i] + Easi[j])
    }
  }

  ### Latent estimates
  for ( subjIdx in 1:Nsubj ) {
    Abil[subjIdx] <- 2*log(subjAbil[subjIdx])
    subjAbil[subjIdx] ~ dnorm( muAbil , sigmaAbil )T(0,)
  }
  for ( itemIdx in 1:Nitem ) {
    Easi[itemIdx] <- 2*log(itemDiff[itemIdx])
    itemDiff[itemIdx] ~ dnorm( muDiff , sigmaDiff )T(0,)
  }

  ### Priors for latents
  muAbil    ~ dgamma(.001,.001)
  sigmaAbil ~ dgamma(.001,.001)
  muDiff    ~ dgamma(.001,.001)
  sigmaDiff ~ dgamma(.001,.001)
  }
  " # close quote for modelString
  model = textConnection(modelString)

  # Run the chains====
  # Name the parameters to be monitored
  params <- c("pCorr","subjAbil","itemDiff","Abil","Easi")
  # Random initial values
  inits <- function(){list("subjAbil"=stats::rgamma(Nsubj,shape=1e-3,rate=1e-3),
                           "itemDiff"=stats::rgamma(Nitem,shape=1e-3,rate=1e-3),
                           "sigmaAbil"=stats::rgamma(Nsubj,shape=1e-3,rate=1e-3),
                           "sigmaDiff"=stats::rgamma(Nitem,shape=1e-3,rate=1e-3))}
  # Define some MCMC parameters for JAGS
  nthin    = 1    # How Much Thinning?
  nchains  = 3    # How Many Chains?
  nburnin  = 100  # How Many Burn-in Samples?
  nsamples = 1100 # How Many Recorded Samples?
  ### Calling JAGS to sample
  startTime = proc.time()
  samples <- R2jags::jags(dataList, NULL, params, model.file =model,
                          n.chains=nchains, n.iter=nsamples, n.burnin=nburnin,
                          n.thin=nthin, DIC=T, jags.seed=666)
  stopTime = proc.time(); elapsedTime = stopTime - startTime; methods::show(elapsedTime)

  ### Inspect and diagnose the run
  #gethdv <- function(v) { return(density(v)$x[which.max(density(v)$y)]) }
  REs <- colMeans(samples$BUGSoutput$sims.list$pCorr[,,])
  abil <- colMeans(samples$BUGSoutput$sims.list$subjAbil)
  diff <- colMeans(samples$BUGSoutput$sims.list$itemDiff)
  dic <- samples$BUGSoutput$DIC
  full <- samples
  matrix <- ordering(REs,abil,diff)$matrix
  Result <- list("matrix"=matrix,"abil"=abil,"diff"=diff,"dic"=dic,"full"=full)
  return(Result)
}