R/printLCT.R

In MattisvdBergh/LCT: Estimate a Latent Class Tree model with Latent GOLD 5.1

#' Print tree results to the terminal
#'
#' These function display the results of a Latent Class Tree analysis
#'
#' @param dataDir directory of the data or a dataframe with the data
#' @param LG directory of the Latent GOLD executable
#' @param LGS directory of Latent GOLD syntax for a model with 1- and 2-class splits
#' @param decreasing Whether the ordering of classes should be decreasing or not. Defaults to TRUE.
#' @param maxClassSplit1 Maximum size of the first split of the tree. Will be assessed with the criterion given in stopCriterium. Defaults to two.
#' @param maxClassSplit2 Maximum size of each split after the first split of the tree. Defaults to two.
#' @param stopCriterium Criterium to decide on a split. Can be "LL" (logLikelihood), "AIC" or "BIC".
#' @param resultsName Name of a folder which will be created in the working directory and contains all results by Latent GOLD.
#' @param minSampleSize Minimum sample size of a class. If this is below 1, a probability of the total sample size is used.
#' @param itemNames The names of the indicators. If this is not given, the rownames of the datafile will be used.
#' @param nKeepVariables Number of variables to be kept if one wants to explore the results with external variables.
#' @param weight Name of the variable with the weights. When all records are unique observations, this should be one for every observation.
#' @param measurementLevels A character vector being either ordinal or continuous to indicate the measurement level of each variable. It is required when LGS is specified.
#'
#' @return None
#' @export
print.LCT <- function(x, ...){
  cat("tree object","\n\n")
  for(idxLevels in 1:(length(x$treeSetup$cleanNames) - 1)){
    cat("level",idxLevels,": ", x$treeSetup$cleanNames[[idxLevels]], "\n")
  }
  cat("Class Sizes final level:\n")
  print(x$splitInfo$CppG[x$treeSetup$finalClasses])
}