R/tam_latreg_ic.R

In TAM: Test Analysis Modules

## File Name: tam_latreg_ic.R
## File Version: 9.13

########################################
# latent regression information criteria
tam_latreg_ic <- function( nstud, deviance,
    beta, beta.fixed, ndim, variance.fixed, G,
    est.variance, variance.Npars=NULL, group )
{
    #***Model parameters
    ic <- data.frame("n"=nstud, "deviance"=deviance )
    loglike <- - deviance / 2
    logprior <- 0
    logpost <- loglike
    ic$loglike <- loglike
    ic$logprior <- logprior
    ic$logpost <- logpost

    dev <- deviance
    # xsi parameters
    ic$Nparsxsi <- 0
    # B slopes
    ic$NparsB <- 0
    # beta regression parameters
    ic$Nparsbeta <- dim(beta)[1] * dim(beta)[2]
    if ( ! is.null( beta.fixed) ){
            ic$Nparsbeta <- ic$Nparsbeta - nrow(beta.fixed ) }
    # variance/covariance matrix
    ic$Nparscov <- ndim + ndim*(ndim-1)/2
    if ( ! est.variance ){ ic$Nparscov <- ic$Nparscov - ndim }
    if ( ! is.null( variance.fixed) ){
            ic$Nparscov <- max(0, ic$Nparscov - nrow(variance.fixed ) )
    }
    if ( ! is.null(variance.Npars) ){
        ic$Nparscov <- variance.Npars
    }
    if ( ! is.null(group) ){
        ic$Nparscov <- ic$Nparscov + length( unique(group) ) - 1
    }
    # total number of parameters
    ic$Npars <- ic$np <- ic$Nparsxsi + ic$NparsB + ic$Nparsbeta + ic$Nparscov
    # AIC
    ic$AIC <- dev + 2*ic$np
    # AIC3
    ic$AIC <- dev + 3*ic$np
    # BIC
    ic$BIC <- dev + ( log(ic$n) )*ic$np
    # adjusted BIC
    ic$aBIC <- dev + ( log( ( ic$n -2 ) / 24 ) )*ic$np
    # CAIC (consistent AIC)
    ic$CAIC <- dev + ( log(ic$n) + 1 )*ic$np
    # corrected AIC
    ic$AICc <- ic$AIC + 2*ic$np * ( ic$np + 1 ) / ( ic$n - ic$np - 1 )
    return(ic)
}


latreg_TAM.ic <- tam_latreg_ic