int_generate.R

# Internal function called by gendt
#
# @useDynLib simstudy
# @importFrom Rcpp sourceCpp
#
# @param args One row from data definitions data.table
# @param n The number of observations required in the data set
# @param dt Incomplete simulated data.table
# @return A data.frame with the updated simulated data
# @keywords internal

generate <- function(args, n, dfSim, idname) {

  if (! args$dist %in% c("normal", "poisson", "noZeroPoisson", "binary", 
                         "binomial","uniform", "uniformInt",
                         "categorical", "gamma", "beta", "negBinomial",
                         "nonrandom", "exponential")) {
    stop(paste(args$dist, "not a valid distribution. Please check spelling."), call. = FALSE)
  }

  if (args$dist=="normal") {
    newColumn <- gennorm(n, args$formula, args$variance, args$link, dfSim)
  } else if (args$dist == "poisson") {
    newColumn <- genpois(n, args$formula, args$link, dfSim)
  } else if (args$dist == "noZeroPoisson") {
    newColumn <- genpoisTrunc(n, args$formula, args$link, dfSim)
  } else if (args$dist == "binary") {
    args$variance = 1
    newColumn <- genbinom(n, args$formula, args$variance, args$link, dfSim)
  } else if (args$dist == "binomial") {                     # added 040918
    newColumn <- genbinom(n, args$formula, args$variance, args$link, dfSim)
  } else if (args$dist=="uniform") {
    newColumn <- genunif(n, args$formula, dfSim)
  } else if (args$dist=="uniformInt") {
    newColumn <- genUnifInt(n, args$formula, dfSim)
  } else if (args$dist=="categorical") {
    newColumn <- gencat(n, args$formula, args$link, dfSim)
  } else if (args$dist == "exponential") {
    newColumn <- genexp(n, args$formula, args$link, dfSim)
  } else if (args$dist == "gamma") {
    newColumn <- gengamma(n, args$formula, args$variance, args$link, dfSim)
  } else if (args$dist == "beta") {
    newColumn <- genbeta(n, args$formula, args$variance, args$link, dfSim)
  } else if (args$dist == "negBinomial") {
    newColumn <- gennegbinom(n, args$formula, args$variance, args$link, dfSim)
  } else if (args$dist == "nonrandom") {
    newColumn <- gendeterm(n, args$formula, args$link, dfSim)
  }

  # Create data frame

  if (is.null(dfSim))
    dfNew <- data.frame(newColumn)
  else
    dfNew <- data.frame(dfSim, newColumn)

  setnames(dfNew, "newColumn", as.character(args$varname))

  return(dfNew)
}