R/CV.control.R

In wuziyueemory/twostageSL: Two-Stage Super Learner Prediction Function

#' Control parameters for the cross validation steps in \code{twostageSL}
#'
#' Control parameters for the cross validation steps in \code{twostageSL}
#'
#' @param V Integer. Number of splits for the V-fold cross-validation step. The default is 10. In most cases, between 10 and 20 splits works well.
#' @param stratifyCV Logical. Should the data splits be stratified by a binary response? Attempts to maintain the same ratio in each training and validation sample.
#' @param shuffle Logical. Should the rows of \code{X} be shuffled before creating the splits.
#' @param validRows A List. Use this to pass pre-specified rows for the sample splits. The length of the list should be \code{V} and each entry in the list should contain a vector with the row numbers of the corresponding validation sample.
#'
#' @return A list containing the control parameters
#'

twostageSL.CV.control <- function(V = 10L, stratifyCV = FALSE, shuffle = TRUE, validRows = NULL){
  # make sure V is an integer
  V <- as.integer(V)

  # Checks for user supplied validRows is present:
  if(!is.null(validRows)) {
    if(!is.list(validRows)) {
      stop('validRows must be a list of length V containing the row numbers for the corresponding validation set')
    }
    if(!identical(V, length(validRows))) {
      stop('V and length(validRows) must be identical')
    }
  }
  list(V = V, stratifyCV = stratifyCV, shuffle = shuffle, validRows = validRows)
}