R/itemfit_rmsea_helper.R

In CDM: Cognitive Diagnosis Modeling

## File Name: itemfit_rmsea_helper.R
## File Version: 0.05


#-- auxiliary function itemfit.rmsea
itemfit_rmsea_helper <- function( n.ik, pi.k, probs ){
    # 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]
        }
    }
    # 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[ is.na( p.ik_observed ) ] <- 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 )
    }
    # calculate statistics
    dist.item <- pi.k_tot * ( p.ik_observed - probs )^2
    h1 <- dist.item[,,1]
    for (kk in 2:K){
        h1 <- h1 + dist.item[,,kk]
    }
    itemfit.rmsea <- sqrt( colSums( abs(h1 + eps ) ) )
    return(itemfit.rmsea)
}