R/test_DIFtree.R

In exhaustiveRasch: Item Selection and Exhaustive Search for Rasch Models

test_DIFtree <- function(items=NULL,
                         DIFvars=NULL,
                         dset=NULL,
                         na.rm=TRUE,
                         model=NULL,
                         p.par=NULL,
                         modelType=NULL,
                         alpha=0.1,
                         estimation_param=NULL){
  # This is an internal function that is not intended to be called by users.
  # It is nevertheless exported so that it can be run in the parallelization
  # workers. However, the function is not documented in the manual.


  #' builds a raschtree using the raschtree or rstree function of the
  #'  psychotree Package.
  #' @param items a numeric vector containing the index numbers of the items in
  #'  dset that are used to fit the model
  #' @param DIFvars a vector or a data.frame containing the external variable(s)
  #'  to test for differential item functioning
  #' @param dset a data.frame containing the data
  #' @param na.rm a boolean value. If TRUE, all cases with any NA are removed
  #'  (na.omit). If FALSE, only cases with full NA responses are removed
  #' @param model on object of a fit Rasch model, estimated with the packages
  #' 'eRm' (classes 'RM', 'PCM' or 'RSM'), 'psychotools' (classes raschmodel,
  #' 'pcmodel' or 'rsmodel') or 'pairwise' (class 'pers'), matching the value of
  #'  modelType. If 'model' is provided, this model is used. If NULL, a model is
  #'  fit using 'dset' and 'items'.
  #' @param p.par a person parameter object matching the class of 'model'. If
  #'  NULL, the person parameters will be estimated.
  #' @param modelType a character value defining the rasch model to fit.
  #'  Possible values: "RM", "PCM", "RSM".
  #' @param alpha a numeric value for the alpha level. Will be ignored if
  #'  use.pval is FALSE
  #' @param estimation_param options for parameter estimation using
  #' \link{estimation_control}
  #' @return if none of the p-values is significant, a list containing
  #'  3 elements is returned: the item combination that was tested, a list of
  #'  the class the model was estimated with (depending on modelType and
  #'  estimation_param$est) with the fit model and a list with a person
  #'  parameter object (depending on estimation_param$est). If there is at
  #'  least one item with a significant p-value, NULL is returned.
  #' @export
  #' @keywords internal

  # This function implements one of the tests that are executed via the 'tests'
  # argument of the exhaustive_tests() function. It is an internal function, a
  # call by the user is not indicated. It is nevertheless exported in order to
  # work in parallelization. However, it is not included in the package
  # documentation (roxygen2 keyword 'internal').

  if (inherits(items, "list")){
    model <- items[[2]]
    p.par <- items[[3]]
    items <- items[[1]]
  }

  ds_test <- dset[, items]

  if (is.null(model)){
    if (na.rm==TRUE){ds_test<- stats::na.omit(ds_test)
    } else{ds_test <- ds_test[rowSums(is.na(ds_test)) < ncol(ds_test)-1, ]}
    #try(suppressWarnings({
    #  model <- get(modelType)(ds_test, se=TRUE)
    #}), silent=TRUE)
    model <- fit_rasch(X=ds_test, modelType=modelType,
                       estimation_param=estimation_param)
  }

  if (!is.null(model)){
    ds_DIF <- merge(ds_test, DIFvars, by=0)
    ds_DIF$Row.names <- NULL
    ds_DIF$rasch <- as.matrix(ds_DIF[ , seq_len(length(items))])
    ds_DIF <- ds_DIF[ , -(seq_len(length(items)))]
    if (modelType=="RM"){
      try(suppressWarnings({
        DIF_tree <- psychotree::raschtree(rasch ~., data=ds_DIF)}), silent=TRUE)
      }
    if (modelType=="RSM"){
      try(suppressWarnings({
        DIF_tree <- psychotree::rstree(rasch ~., data=ds_DIF)}), silent=TRUE)
      }
    if (modelType=="PCM"){
      try(suppressWarnings({
        DIF_tree <- psychotree::pctree(rasch ~., data=ds_DIF)}), silent=TRUE)
      }
  }
  if (exists("DIF_tree")==TRUE & !is.null(model)){
    if (length(DIF_tree)==1){
      return(list(items, model, p.par))
    }
  }
}