R/reglca_fit_probabilities.R

In alexanderrobitzsch/CDM: Cognitive Diagnosis Modeling

## File Name: reglca_fit_probabilities.R
## File Version: 0.4416

reglca_fit_probabilities <- function( freq, pi_class, lambda, parm_init=NULL, regular_type="scad",
    h=1E-4, maxit=100, conv=1E-5, cd_steps=5, max_increment=1, verbose=TRUE, prob_min=0,
    ii=NULL, iter=NULL, xsi=NULL, est_type="CD" )
{
    #--- order frequencies
    NP <- length(freq)
    freq_index <- data.frame(index=1:NP, freq=freq, pi_class=pi_class )
    freq_index <- freq_index[ order(freq_index$freq), ]
    freq <- freq_index$freq
    pi_class <- freq_index$pi_class
    C <- pi_class*freq
    W <- pi_class*(1-freq)
    #--- init parameters
    if ( is.null(parm_init) ){
        parm_init <- c( freq[1], diff(freq) )
    }
    parm <- parm_init
    iter <- 0
    iterate <- TRUE


    #---- begin iterations
    while(iterate){
        res <- reglca_fit_probabilities_fit_function( parm=parm, lambda=lambda,
                        C=C, W=W, regular_type=regular_type )
        ll <- res$ll
        pen <- res$pen
        fit_fct <- res$fit_fct
        parm_old <- parm
        for (pp in 1:NP){
            parm <- reglca_update_parameter( parm=parm, pp=pp, C=C, W=W, h=h, lambda=lambda,
                        regular_type=regular_type, cd_steps=cd_steps, conv=conv,
                        max_increment=max_increment, vt=NULL, prob_min=prob_min, ii=ii,
                        iter=iter)
        }
        #-- normalize probabilities
        parm <- reglca_normalize_probabilities(parm=parm)
        parchange <- max( abs( parm - parm_old ))
        if (iter > maxit){ iterate <- FALSE }
        if (parchange < conv){ iterate <- FALSE }
        iter <- iter + 1
        if (verbose){
            cat( paste0("Iteration ", iter, " | Max. parm. change=",
                    round( parchange, 6), " | Fit function=", round( fit_fct, 6), "\n") )
            utils::flush.console()
        }
    }

    freq_index$fitted <- cumsum(parm)
    probs <- rep(0,NP)
    probs[ freq_index$index ] <- freq_index$fitted
    n_par <- sum( abs(parm) > 1E-10 )

    #--- output
    res <- list( parm=parm, n_par=n_par, probs=probs, ll=ll, pen=pen, fit_fct=fit_fct)
    return(res)
}