R/din.partable.R

In CDM: Cognitive Diagnosis Modeling

## File Name: din.partable.R
## File Version: 0.24

##############################################
# parameter table for the din model
din.partable <- function( guess,  slip, attribute.patt, data, rule,
        guess.equal, slip.equal, constraint.guess, constraint.slip,
        zeroprob.skillclasses, attribute.patt.splitted )
{
    # parameters
    J <- nrow(guess)
    L <- nrow(attribute.patt)
    items <- colnames(data)
    K <- ncol(attribute.patt.splitted)

    #**************
    # create parameter table
    partable <- data.frame( "partype"=c( rep( c("guess","slip"), J),
                        rep("probs", L), rep("margprobs", K ) )
                            )
    partable$parindex <- c( 1:J, J + 1:J, 2*J + 1:(L-1), 0, rep( 0, K ))
    partable$item <- c( rep(1:J, each=2 ), rep(0,L+K) )
    partable$item.name <- c( rep(colnames(data),each=2), rep("",L+K) )
    partable$skillclass <- c( rep(0,2*J), 1:(L), rep(0,K))
    partable$varyindex <- c( rep(1:J, each=2 ), 1:(L-1), 0, rep(0,K))
    m1 <- paste0( rep( items, each=2 ), "_", c( "guess", "slip" ) )
    partable$parnames <- c( m1, paste0( "prob_class", 1:L ),
                paste0( "prob_skill", 1:K ))
    dfr <- cbind( guess$est, slip$est )
    dfr <- matrix( t(dfr), nrow=1, byrow=TRUE )

    # marginal skill probabilities
    margskills <- colSums( attribute.patt.splitted * attribute.patt[,1] )

    partable$value <- c( dfr[1,], attribute.patt[,1], margskills )
    partable$fixed <- FALSE
    partable$free <- partable$parindex > 0
    partable$rule <- c( rep(rule,2), rep("",L+K) )
    partable$totindex <- 1:(nrow(partable))

    #**************************
    # include item parameter constraints
    if (guess.equal){
        p1 <- partable[ partable$partype=="guess", ]
        partable[ p1$totindex, "parindex" ] <- p1$parindex[1]
        partable[ p1$totindex, "parnames" ] <- "all_guess"
    }
    if (slip.equal){
        p1 <- partable[ partable$partype=="slip", ]
        partable[ p1$totindex, "parindex" ] <- p1$parindex[1]
        partable[ p1$totindex, "parnames" ] <- "all_slip"
    }
    if ( ! is.null(constraint.slip) ){
        p1 <- partable[ ( partable$partype=="slip" ) &
                        ( partable$item %in% constraint.slip[,1] ), ]
        partable[ p1$totindex, "fixed" ] <- TRUE
        partable[ p1$totindex, "free" ] <- FALSE
        partable[ p1$totindex, "parindex" ] <- 0
    }
    if ( ! is.null(constraint.guess) ){
        p1 <- partable[ ( partable$partype=="guess" ) &
                        ( partable$item %in% constraint.guess[,1] ), ]
        partable[ p1$totindex, "fixed" ] <- TRUE
        partable[ p1$totindex, "free" ] <- FALSE
        partable[ p1$totindex, "parindex" ] <- 0
    }
    if ( ! is.null(zeroprob.skillclasses) ){
        p1 <- partable[ ( partable$partype=="probs" ) &
                        ( partable$skillclass %in% zeroprob.skillclasses ), ]
        partable[ p1$totindex, "fixed" ] <- TRUE
        partable[ p1$totindex, "free" ] <- FALSE
        partable[ p1$totindex, "parindex" ] <- 0
    }
    #*********************************
    # include parameter transformation matrix

    estpars <- unique( partable[ partable$parindex > 0, "parnames" ] )
    allpars <- unique( partable$parnames )
    MP <- length(allpars)
    FP <- length(estpars)
    A <- matrix( 0, nrow=MP, ncol=FP)
    rownames(A) <- allpars
    colnames(A) <- estpars

    # free parameters
    a1 <- match( estpars, allpars )
    A[ cbind( a1, 1:FP ) ] <- 1
    # probabilities of last class
    probs_names <- partable[ partable$partype=="probs", "parnames" ]
    v1 <- probs_names[ length(probs_names ) ]
    v2 <- intersect( setdiff( probs_names, v1 ), estpars )
    A[ v1, v2 ] <- - 1
    # marginal skill probabilities
    rownames(attribute.patt.splitted) <- probs_names
    colnames(attribute.patt.splitted) <-
            partable[ partable$partype=="margprobs", "parnames" ]
    attribute.patt.splitted <- ( attribute.patt.splitted - 1 )
    a1 <- t(attribute.patt.splitted)
    a1 <- a1[, intersect( estpars, colnames(a1) ) ]
    A[ rownames(a1), colnames(a1) ] <- a1
    #*********************************
    # introduce new parameter index
    partable$parindex <- match( partable$parindex, sort(unique(partable$parindex) ) ) - 1
    res <- list( "partable"=partable, "vcov.derived"=list("A"=A) )
    return(res)
}
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