R/IRT.derivedParameters.R

In CDM: Cognitive Diagnosis Modeling

## File Name: IRT.derivedParameters.R
## File Version: 0.10

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IRT.derivedParameters <- function( jkobject, derived.parameters )
{
    object <- jkobject
    parsM <- object$parsM
    est <- object$jpartable$value
    names(est) <- rownames(parsM)
    parsM <- t(parsM)
    allformulas <- derived.parameters[[1]]
    FF <- length(derived.parameters)
    if (FF>1){
        for (ff in 2:FF){
            t1 <- stats::terms( allformulas)
            t2 <- paste( c( attr( t1, "term.labels" ),
                attr(  stats::terms( derived.parameters[[ff]] ), "term.labels" )  ),
                collapse=" + " )
            allformulas  <- stats::as.formula( paste( " ~ 0 + ", t2 ) )
        }
    }
    # create matrices of derived parameters
    der.pars <- stats::model.matrix( allformulas, as.data.frame( t(est) ) )
    colnames(der.pars) <- names(derived.parameters)
    der.pars.rep <- stats::model.matrix( allformulas, as.data.frame( parsM) )
    colnames(der.pars.rep) <- names(derived.parameters)
    parsM <- t(der.pars.rep)
    res0 <- jkestimates( est=as.vector(der.pars), parsM=parsM, fayfac=object$fayfac )
    jpartable <- data.frame( "parnames"=names(derived.parameters),
                    "value"=as.vector(der.pars) )
    jpartable$jkest <- res0$dfr$jkest
    jpartable$jkse <- res0$dfr$jkse
    res <- list( "parsM"=parsM, "vcov"=res0$vcov_pars,
                    "jpartable"=jpartable, "fayfac"=object$fayfac )
    class(res) <- "IRT.jackknife"
    return(res)
}
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