R/gdina_mstep_item_ml_rrum2.R

In CDM: Cognitive Diagnosis Modeling

## File Name: gdina_mstep_item_ml_rrum2.R
## File Version: 1.11

#####################################################
# GDINA M-step item parameters
gdina_mstep_item_ml_rrum2 <- function(
            pjjj, Ilj.ast, Rlj.ast, eps, avoid.zeroprobs, Mjjj, invM.list, linkfct,
            rule, method, iter, delta.new, max.increment, fac.oldxsi, jj, delta,
            rrum.model, delta.fixed, mstep_iter, mstep_conv, devchange)
{
    eps2 <- eps

    delta_jj <- delta[[jj]]
    delta_jj <- stats::qlogis( logpars2rrumpars(delta_jj) )

    converged <- FALSE
    ii <- 0
    max_increment <- max.increment

    eps <- 1E-3
    Rlj.ast <- Rlj.ast + .005
    Ilj.ast <- Ilj.ast + .05

    #*** define function
    ll_FUN <- function(par){
                delta_jj <- par
                delta_jj <- rrumpars2logpars( stats::plogis(delta_jj) )
                irf1 <- ( Mjjj %*% delta_jj )[,1]
                irf1 <- exp(irf1)
                irf1 <- cdm_squeeze( irf1, c(eps,1-eps) )
                ll <- - sum( Rlj.ast * log(abs(irf1)) +
                            ( Ilj.ast - Rlj.ast ) * log( abs(1 - irf1 ) ) )
                return(ll)
        }
    #*** optimzation
    mstep_conv <- 1000
    res0 <- stats::optim( par=delta_jj, fn=ll_FUN, method="BFGS",
                    control=list( maxit=mstep_conv) )
    delta_jj <- res0$par

    #*** retransform
    delta_jj <- stats::plogis( delta_jj )
    delta_jj <- rrumpars2logpars( delta_jj )
    delta.new[[jj]] <- delta_jj
    if ( (fac.oldxsi > 0 ) & (iter>3)){
        fac.oldxsi1 <- fac.oldxsi * ( devchange >=0 )
        delta.new[[jj]] <- fac.oldxsi1*delta[[jj]] + ( 1 - fac.oldxsi1 ) * delta.new[[jj]]
    }

    # fix delta parameter here!!
    if ( ! is.null( delta.fixed ) ){
        delta.fixed.jj <- delta.fixed[[jj]]
        if ( ! is.na( delta.fixed.jj)[1] ){
            delta.new[[jj]] <- delta.fixed.jj
        }
    }
    #*** output
    res <- list( delta.new=delta.new )
    return(res)
}
######################################################