R/summary.mcdina.R

In CDM: Cognitive Diagnosis Modeling

## File Name: summary.mcdina.R
## File Version: 0.35


##################################################################
# Summary of the GDINA model
summary.mcdina <- function( object, digits=4, file=NULL, ... )
{
    #-------------------------------------------------------
    # INPUT:
    # object    ... result from GDINA analysis
    # rdigits     ... number of digits for rounding parameter estimates
    #-------------------------------------------------------
    rdigits <- digits

    osink( file=file, suffix=paste0( "__SUMMARY.Rout") )

    # Parameter summary
    cat("----------------------------------------------------------------------------\n")
    d1 <- utils::packageDescription("CDM")
    cat( paste( d1$Package, " ", d1$Version, " (", d1$Date, ")", sep=""), "\n" )
    cat( "Date of Analysis:", paste( object$time$s2 ), "\n" )
    cat("Computation Time:", print(object$time$s2 - object$time$s1), "\n\n")
    cat("Multiple Choice DINA Model (MC-DINA)\n")
    cat( "\nNumber of iterations","=", object$iter )
    if ( ! object$converged ){
        cat("\nMaximum number of iterations was reached.")
    }

    cat( "\n\nDeviance","=", round( object$ic$dev, 2 ) )
    cat( "  | Loglikelihood","=", round( - object$ic$dev / 2, 2 ),    "\n" )
    cat( "Number of persons","=", object$ic$n, "\n" )
    cat( "Number of groups","=", object$G, "\n" )
    cat( "Number of items","=", ncol(object$dat), "\n" )
    cat( "Number of estimated parameters","=", object$ic$np, "\n" )
    cat( "Number of estimated item parameters","=", object$ic$itempars, "\n" )
    cat( "Number of estimated skill class parameters","=", object$ic$traitpars )
    cat( " (", object$ic$Nskillclasses, "latent skill classes)\n")
    cat( "\nAIC ","=", round( object$ic$AIC, 2 ), " ; penalty=",
                round( object$ic$AIC - object$ic$deviance,2 ), "\n" )
    cat( "BIC ","=", round( object$ic$BIC, 2 ), " ; penalty=",
                round( object$ic$BIC - object$ic$deviance,2 ), "\n" )
    cat( "CAIC","=", round( object$ic$CAIC, 2 )," ; penalty=",
                round( object$ic$CAIC - object$ic$deviance,2 ), "\n\n" )

    ###########################################################
    ds <- object$item
    cds <- colnames(ds)
    inds <- grep( "se", cds )
    ds <- ds[, - inds ]
    ind <- grep( "Cat", colnames(ds) )
    ds[,ind] <- round( ds[,ind], rdigits )
    cat("----------------------------------------------------------------------------\n")
    cat("\nItem Parameter Estimates \n\n")
    r1 <- options()
    options(scipen=999)
    print(ds)
    options(scipen=r1$scipen)
    cat("----------------------------------------------------------------------------\n")
    cat("\nSkill Probabilities \n\n")
    print(round(object$skill.patt,rdigits) )
#    cat("not yet implemented!\n")
#    if ( ( object$G==1 ) & (ncol(object$q.matrix ) > 1 ) &
#            max(object$NAttr==1 ) ){
#        cat("----------------------------------------------------------------------------\n")
#        QM <- max(object$q.matrix)
#        if (QM==1){
#            cat("\nTetrachoric Correlations \n\n")
#            gt1 <- skill.cor( object )
#                } else {
#            cat("\nPolychoric Correlations \n\n")
#            gt1 <- skill.polychor( object )
#                }
#        print(round(gt1$cor.skills,3))
#            }
    cat("\n----------------------------------------------------------------------------\n")
    cat("\nSkill Pattern Probabilities \n\n")
    if ( object$G==1 ){
        xt <- round( object$attribute.patt[,1], rdigits )
        names(xt) <- rownames( object$attribute.patt )
    } else {
        xt <- round( object$attribute.patt, rdigits )
        rownames(xt) <- rownames( object$attribute.patt )
    }
    print(xt)

    csink( file=file )
}
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