## File Name: summary.tam.pv.mcmc.R
## File Version: 0.28
#*******************************************************
# summary
summary.tam.pv.mcmc <- function( object, file=NULL, ...)
{
tam_osink( file=file )
calc_ic <- object$calc_ic
cat("------------------------------------------------------------\n")
#- package and R session
tam_print_package_rsession(pack="TAM")
#- computation time
tam_print_computation_time(object=object)
cat("Bayesian Estimation of Plausible Values")
#- print call
tam_print_call(object$CALL)
cat("------------------------------------------------------------\n")
cat( "Number of iterations", "=", object$n.iter, "\n" )
cat( "Number of burnin iterations", "=", object$n.burnin, "\n" )
cat("------------------------------------------------------------\n")
if (calc_ic){
cat("\nCriteria based on Marginal Likelihood\n")
cat( "\nDeviance", "=", round( object$ic$deviance, 2 ), " | " )
cat( "Log Likelihood", "=", round( -object$ic$deviance/2, 2 ), "\n" )
}
cat( "Number of persons", "=", object$ic$n, "\n" )
cat( "Number of estimated parameters", "=", object$ic$Npars, "\n\n" )
#--- print information criteria
tam_summary_print_ic( object=object, bayes_crit=TRUE )
#----- properties of plausible values
cat("------------------------------------------------------------\n")
cat("Plausible Values\n\n")
cat( "Number of plausible values", "=", attr(object$pv, "nplausible"), "\n" )
cat( "Iterations between PVs", "=", attr(object$pv, "pv_lag"), "\n" )
cat( "Average acceptance rate", "=",
round( attr(object$theta_acceptance_MH, "M_accrate"),3), "\n" )
cat( "Average MH adjustment factor", "=",
round( attr(object$theta_acceptance_MH, "M_adj_MH"),3), "\n" )
cat("\nAutocorrelation of Drawn Plausible Values\n")
tam_round_data_frame_print(obji=object$theta_acf, digits=3)
cat("\nEAP Reliability\n")
tam_round_data_frame_print(obji=object$EAP_rel, digits=3)
cat("------------------------------------------------------------\n")
cat("Regression Parameters\n")
obji <- object$parameter_summary
tam_round_data_frame_print(obji=obji, from=2, digits=3)
#*** print covariance matrix
cov_digits <- 3
tam_pv_summary_covariance( obji=object$variance,
label="Residual Covariance Matrix", digits=cov_digits)
#*** print correlation matrix
tam_pv_summary_covariance( obji=object$correlation,
label="Residual Correlation Matrix", digits=cov_digits)
#******
tam_csink(file=file)
}
#*******************************************************
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