R/mdmb_regression_summary.R

In alexanderrobitzsch/mdmb: Model Based Treatment of Missing Data

## File Name: mdmb_regression_summary.R
## File Version: 0.481


#*******************************************************
# Summary for logistic_regression object
mdmb_regression_summary <- function( object, digits=4, file=NULL, ...)
{

    type <- object$type

    # open sink
    CDM::osink( file=file, suffix=paste0( '__SUMMARY.Rout') )

    cat('-----------------------------------------------------------------\n')
    # package and R session
    mdmb_summary_print_model_description(object=object, pack='mdmb')

    cat( object$description, '\n\n')

    cat('-----------------------------------------------------------------\n')
    cat( 'Optimizer','=', object$optimizer, '\n' )
    cat( 'Converged','=', object$converged, '\n' )
    cat( 'Convergence code','=', object$convergence_code, '\n' )
    cat('\n')
    cat( 'Number of observations','=', object$ic$n, '\n' )
    cat( 'Number of iterations','=', object$iter, '\n\n' )
    cat( 'Deviance','=', round( object$ic$deviance, 2 ), '\n' )
    cat( 'Log likelihood','=', round( object$loglike, 2 ), '\n' )
    cat( 'Log prior','=', round( object$logprior, 2 ), '\n' )
    cat( 'Log posterior','=', round( object$logpost, 2 ), '\n' )

    cat('\n')
    cat( 'Number of estimated parameters','=', object$ic$np, '\n' )

    cat( '  Number of estimated beta parameters','=', object$ic$np.beta, '\n' )
    cat( '  Number of estimated sigma parameters','=', object$ic$np.sigma, '\n' )
    cat( '  Number of estimated lambda parameters','=', object$ic$np.lambda, '\n' )
    cat( '  Number of estimated df parameters','=', object$ic$np.df, '\n' )
    cat( '  Number of estimated threshold parameters','=', object$ic$np.thresh, '\n' )
    cat('\n')

    cat( 'AIC','=', round( object$ic$AIC, 2 ), ' | penalty','=',
                round( object$ic$AIC - object$ic$deviance,2 ),
                '   | AIC=-2*LL + 2*p  \n' )
    cat( 'AICc','=', round( object$ic$AICc, 2 ),' | penalty','=',
                round( object$ic$AICc - object$ic$deviance,2 ) )
    cat('    | AICc=-2*LL + 2*p + 2*p*(p+1)/(n-p-1)  (bias corrected AIC)\n' )
    cat( 'BIC','=', round( object$ic$BIC, 2 ), ' | penalty','=',
                round( object$ic$BIC - object$ic$deviance,2 ),
            '   | BIC=-2*LL + log(n)*p  \n' )
    cat( 'CAIC','=', round( object$ic$CAIC, 2 ),' | penalty','=',
                round( object$ic$CAIC - object$ic$deviance,2 ) )
    cat('   | CAIC=-2*LL + [log(n)+1]*p  (consistent AIC)\n\n' )

    cat('-----------------------------------------------------------------\n')
    cat('Estimated Parameters\n')
    obji <- object$partable
    CDM::cdm_print_summary_data_frame(obji, digits=digits, from=2)
    cat('\n')

    #*** print thresholds
    if (type %in% c('oprobit') ){
        cat('-----------------------------------------------------------------\n')
        cat('Threshold Parameters\n')
        print( round( object$thresh, digits ) )
        cat('\n')
    }

    #-----------------------------------------
    # Explained Variance

    #*** logistic regression
    if (type %in% c('logistic', 'oprobit') ){
        cat('Pseudo R-Square (McKelvey & Zavoina)','=',
            round( object$R2, digits ), '\n' )
    }
    #*** yjt and bct regression
    if (type %in% c('yjt','bct') ){
        cat('R2','=',     round( object$R2, digits ), '\n' )
    }

    # close sink
    CDM::csink( file=file )
}
#*******************************************************