R/IRT_RMSD_calc_distributions.R

In alexanderrobitzsch/CDM: Cognitive Diagnosis Modeling

## File Name: IRT_RMSD_calc_distributions.R
## File Version: 0.07

IRT_RMSD_calc_distributions <- function( n.ik, pi.k, eps=1E-30 )
{
    # probs ... [ classes, items, categories ]
    # n.ik ... [ classes, items, categories, groups ]
    # N.ik ... [ classes, items, categories]
    N.ik <- n.ik[,,,1]
    G <- dim(n.ik)[4]
    pitot <- pi.k[,1]
    eps <- 1E-10
    if (G>1){
        for (gg in 2:G ){
            N.ik <- N.ik + n.ik[,,,gg]
            pitot <- pitot + pi.k[,gg]
        }
    }
    #*** extract maximum number of categories
    maxK <- apply( N.ik, c(2,3), sum, na.rm=TRUE )
    maxK <- rowSums( maxK > eps )
    # calculate summed counts
    N.ik_tot <- array( 0, dim=dim(N.ik) )
    N.ik_tot[,,1] <- N.ik[,,1,drop=FALSE]
    K <- dim(N.ik)[3]
    for (kk in 2:K){
        N.ik_tot[,,1] <- N.ik_tot[,,1,drop=FALSE] + N.ik[,,kk,drop=FALSE]
    }
    for (kk in 2:K){
        N.ik_tot[,,kk] <- N.ik_tot[,,1]
    }

    # calculate itemwise statistics
    p.ik_observed <- N.ik / ( N.ik_tot + eps )
    p.ik_observed <- replace_NA( p.ik_observed, value=0 )
    # define class weights
    pi.k_tot <- array( 0, dim=dim(p.ik_observed) )
    for (kk in 1:K){
        pi.k_tot[,,kk] <- matrix( pitot, nrow=dim(pi.k_tot)[1],
                ncol=dim(pi.k_tot)[2], byrow=FALSE )
    }
    #--- output
    res <- list( N.ik=N.ik, N.ik_tot=N.ik_tot, p.ik_observed=p.ik_observed,
                    pi.k_tot=pi.k_tot, maxK=maxK, K=K )
    return(res)
}