R/spCIRM.R

In vthorrf/CIRM: Procedures for IRT modeling

spCIRM <- function(x,l,p=.5){
  require(R2jags)

  ### Assemble data into list for JAGS====
  y = x
  p = p
  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, p=p, 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)
      Pr[i,j] <- pCorr[pi[i,j],j]
      pCorr[i,j] <- (p * subjAbil[i]) /
                    ( (p * subjAbil[i]) + ((1-p) * itemDiff[j]) )
      pi[i,j] ~ dcat(pp[1:Nsubj])
    }
  }

  ### Dirichlet distribution prior
  for(i in 1:Nsubj) {
    c[i]      ~ dexp( delta )
    xi_raw[i] ~ dgamma(c[i] + 1, 1)
    pp[i]     <- xi_raw[i]/sum(xi_raw[1:Nsubj])
  }

  ### Abil estimates
  for ( subjIdx in 1:Nsubj ) {
    subjAbil[subjIdx] ~ dbeta( (mu*(sigma-2)) + 1 , ((1 - mu)*(sigma-2)) + 1  )
  }

  ### Diff estimates
  for ( itemIdx in 1:Nitem ) {
    itemDiff[itemIdx] ~ dbeta( (omega*(kappa-2)) + 1 , ((1 - omega)*(kappa-2)) + 1 )
  }

  ### Priors
  mu            ~ dbeta(1,1)
  sigma         <- sigmaMinusTwo + 2
  sigmaMinusTwo ~ dgamma(.01,.01)
  omega         ~ dbeta(1,1)
  kappa         <- kappaMinusTwo + 2
  kappaMinusTwo ~ dgamma(.01,.01)
  delta         ~ dunif( 1e-2, 1e2 )
  }
  " # close quote for modelString
  model = textConnection(modelString)

  # Run the chains====
  # Name the parameters to be monitored
  params <- c("pCorr","subjAbil","itemDiff","c")
  # Random initial values
  inits <- function(){list("c"=truncdist::rtrunc(1,"exp",a=1),
                           "mu"=stats::rbeta(Nsubj,1,1),
                           "omega"=stats::rnorm(Nitem,0,1))}
  # 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

  ### Inspect and diagnose the run====
  REs <- colMeans(samples$BUGSoutput$sims.list$pCorr[,,])
  alpha <- colMeans(samples$BUGSoutput$sims.list$alpha)
  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,"alpha"=alpha,"abil"=abil,"diff"=diff,
                 "dic"=dic,"full"=full)
  methods::show(elapsedTime)
  return(Result)
}