R/compose_distinct.R

In nikitagusarov/sdcv2: Discrete Choice Experiment Simulator

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## This file enlists functions used for data generation procedures ##
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# nikita.gusarov@univ-grenoble-alpes.fr - February 2022

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# 1. Definition of coupling strategyes #
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# Compose random

#' @title Compose random choice sets
#' @description This is an internal function which creates distinct choice sets configurations for give experimental design.
#' The function simplifies the size adjustment of Z depending on the population size of X.
#' Each individuals faces a personalised Z version.
#'
#' @param experimental_design An `experimental_design` object.
#' @param n Number of different choice situations per individual.
#' Is NULL by default, ensuring that the function will retreive the n value from the `experimental_design`.
#' @param size Population size for Z to be adjusted to.
#' @return data.frame Z dataset
#'
#' @examples
#' # Create alternatives
#' alt1 <- alternative$new()
#' alt1$add_attributes(Quality = runif(min = 0, max = 1), Price = rnorm(mean = 5))
#' alt2 <- alternative$new()
#' alt2$add_attributes(Size = runif(min = 0, max = 1), Price = rnorm(mean = 6))
#'
#' # Regroup alternatives into design
#' edesign <- experimental_design$new(alternatives = list(alt1, alt2), n = 4)
#'
#' # Compose
#' Z <- compose_distinct(edesign, size = 2)
#' @export
#' @import foreach

compose_distinct <- function(experimental_design, n = NULL, size) {
  if (is.null(n)) {
    n <- experimental_design$n
    if (is.null(n)) stop("No valid 'n' passed to compose_distinct")
  }

  # Regenerate Z for every individual
  Z <- foreach(i = seq(size), .combine = "rbind") %do% {
    # Generate Z for all individuals
    Z <- alternatives_gen(
      experimental_design,
      n = n
    )
  }

  # Output
  return(Z)
}