R/gdina_probitem.R

In CDM: Cognitive Diagnosis Modeling

## File Name: gdina_probitem.R
## File Version: 0.22

#################################################################################
# calculate model implied probabilities in GDINA models
gdina_probitem <- function( Mj, Aj, delta, rule, linkfct, delta.summary,
        necc.attr=NULL)
{
    I <- length(delta)
    pjj <- as.list( 1:I )
    ljjj <- rep(0,I)
    for (ii in 1:I){
        pjjt <- ( Mj[[ii]][[1]] %*% delta[[ii]] )[,1]
        names(pjjt) <- paste0("A",apply( Aj[[ii]], 1, FUN=function(ll){ paste(ll, collapse="") } ) )
        if (linkfct=="logit"){ pjjt <- stats::plogis( pjjt ) }
        if (linkfct=="log"){ pjjt <- exp( pjjt ) }
        pjj[[ii]] <- pjjt
        ljjj[ii] <- length(pjjt)
    }

    pjj <- unlist( pjj )
    res <- data.frame( "itemno"=rep(1:I, ljjj), "skillcomb"=names(pjj), "prob"=pjj )
    dres <- NULL
    for (ii in 1:I){
        dii <- delta.summary[ delta.summary$itemno==ii, ]
        dii <- dii[ nrow(dii), c("item", "rule", "partype.attr" ) ]
        necc_ii <- necc.attr[[ii]]
        dii$partype.attr <- paste0( names(necc_ii), collapse="-")
        colnames(dii)[3] <- "nessskill"
        dres <- rbind( dres, dii )
    }
    res <- cbind( dres[ res$itemno, ], res )

    rownames(res) <- NULL
    return(res)
}
#################################################################################

gdina.probitem <- gdina_probitem