R/print.summary.din.R

In alexanderrobitzsch/CDM: Cognitive Diagnosis Modeling

## File Name: print.summary.din.R
## File Version: 1.29
################################################################################
# print method for objects of class "summary.din"                              #
################################################################################
print.summary.din <-
function(x, ...){

# Call: generic
# Input: object of class summary.din
# Print: prints the named list, of an object of class summary.din

################################################################################
# console output                                                               #
################################################################################
# if(is.null(x$log.file)){
  d <- utils::packageDescription("CDM")
  packageStartupMessage(paste(d$Package," ", d$Version," (Built ",d$Date,")",sep=""))
#   cat("Call:\n",  x$CALL, "\n")
  cat("Call:\n",  x$call, "\n\n")

    cat( "Date of Analysis:", paste( x$end.analysis ), "\n" )
    cat("Computation Time:", print(
            x$end.analysis - x$start.analysis),"\n\n")


#      "\nItem discrimination index:\n")
#      print(as.table(x$IDI))
#      cat("\nSummary of skill pattern distribution:\n")
 #     print(x$SKILL.CLASS.PROB)
     cat("\nDeviance","=", x$deviance, " |   Log-Likelihood=",
            round( x$din.object$loglike,3), "\n")

    #*** iterations
    cat( "\nNumber of iterations:", x$din.object$iter, "\n")
    if ( ! x$din.object$converged ){ cat("Maximum number of iterations was reached.\n") }
    #***

     cat( "\nNumber of item parameters:", x$Npars[,1], "\n")
     cat( "Number of skill class parameters:", x$Npars[,2], "\n")
  cat("\nInformation criteria:",
      "\n  AIC","=", x$AIC,
      "\n  BIC","=", x$BIC, "\n")
  cat("\nMean of RMSEA item fit:",
    round( x$din.object$mean.rmsea,3 ), "\n")
  cat("\nItem parameters\n")
  obji <- x$item
  rownames(obji) <- NULL
    print( obji, digits=3 )
    cat("\nMarginal skill probabilities:\n")
    print(x$din.object$skill.patt, digits=4)
    # tetrachoric skill correlations
    if( ncol(x$din.object$q.matrix ) > 1 ){
        obji <- skill.cor(x$din.object)$cor.skills
        cat("\nTetrachoric correlations among skill dimensions\n")
        print( obji, digits=4 )
    }
    cat("\nSkill Pattern Probabilities \n\n")
    xt <- round( x$din.object$attribute.patt[,1], digits=5 )
    names(xt) <- rownames( x$din.object$attribute.patt )
    print(xt)
# }else{

################################################################################
# logfile output                                                               #
################################################################################
    # tr <- try({sink(file=x$log.file)})
if (FALSE){
    tr <- try({sink(file=paste0( x$log.file, "__SUMMARY.Rout") )})

    if(is.null(tr)){
      gowidth <- getOption("width")
      options(width=10000)
      cat("#.......................................................\n")
      d <-  utils::packageDescription("CDM")
#      c1 <- citation("CDM")
      cat(paste( "This is CDM package version ", d$Version, " (", d$Date, ")\n",sep=""))
#      print(c1)
      cat("\n")
      cat("#-------------------------\n")
      cat("# SUMMARY OF ANALYSIS\n")
      cat( "Start:", paste(x$start.analysis ))
      cat( "\nEnd  :", paste(x$end.analysis ))
      cat("\n#-------------------------\n\n")
      cat("Model rule:",x$display, "\n")
      cat("Number of observations:",nrow(x$data), "\n")
      cat("Number of items:",nrow(x$q.matrix), "\n")
      cat("Labels of items:", paste(rownames(x$q.matrix), collapse=", "), "\n")
      cat("Number of skills:",ncol(x$q.matrix), "\n")
      cat("Labels of skills:", paste(attributes(x$q.matrix)$skill.labels, collapse=", "), "\n")
      cat("Q-Matrix:\n\n")
      print(data.frame(x$q.matrix))

      cat("\n#-------------------------\n")
      cat("# SUMMARY OF MODEL FIT\n")
      cat("#-------------------------\n\n")
      cat("Loglikelihood:", x$loglike, "\n")
      cat("AIC:", x$AIC, "\n")
      cat("BIC:", x$BIC, "\n\n")
      cat("Item discrimination index:\n\n")
      print(as.table(x$IDI))
      cat("\nSummary of skill pattern distribution:\n\n")
      print(x$SKILL.CLASS.PROB)

      cat("\n#-------------------------\n")
      cat("SUMMARY OF MODEL RESULTS\n")
      cat("#-------------------------\n\n")
      cat("Item parameter estimates:\n\n")
      print(x$coef)
      cat("\nSkill probability:\n\n")
      print(x$skill.patt)
      cat("\nSkill pattern occurrence probability:\n\n")
      print(x$attribute.patt)
      cat("\nSkill class assignment and skill assignment probabilities for respective response pattern:\n\n")
      print(cbind("freq"=as.vector(table(x$pattern[,1])), unique(x$pattern)))

      sink()
      options(width=gowidth)
      cat("\nExtensive summary written to log file:\n", x$log.file,"\n")
    }else cat("\nError while trying to write summary to log file:\n", tr[1])
  }

}