R/rmixnorm.R

In KScorrect: Lilliefors-Corrected Kolmogorov-Smirnov Goodness-of-Fit Tests

#' @rdname dmixnorm
#' @export
#' @importFrom stats rnorm
rmixnorm <- function (n, mean, sd, pro) {
  if (mode(n) != "numeric" | n <= 1L)
    stop("'n' must be a positive, non-empty numeric vector.")
  if (any(missing(mean), missing(sd)))
    stop("'mean' and 'sd' not provided, without default.")
  mean <- as.vector(mean, mode = "numeric")
  G <- length(mean)
  sd <- as.vector(sd, mode = "numeric")
  if (missing(pro)) {
    pro <- rep(1 / G, G)
    warning("mixing proportion 'pro' not provided. Assigned equal proportions by default.")
  }
  if (any(pro < 0L, sd < 0L))
    stop("'pro' and 'sd' must not be negative.")
  lpro <- length(pro)
  modelName = "V"
  lsd <- length(sd)
  if (lsd == 1L & G > 1L) {
    modelName <- "E"
    sd[seq(G)] <- sd[1]
    lsd <- length(sd)
    warning("'equal variance model' implemented. If want 'variable-variance model', specify remaining 'sd's.")
  }
  if (G < lsd | G < lpro | (lsd > 1L & G != lsd) | (!missing(pro) & G != lpro))
    stop("the lengths of supplied parameters do not make sense.")
  pro <- as.vector(pro, mode = "numeric")
  pro <- pro / sum(pro)
  clabels <- sample(1:G, size = n, replace = TRUE, prob = pro)
  ctable <- tabulate(clabels, nbins = G)
  x <- rep(0, n)
  for (k in 1:G) {
    x[clabels == k] <- mean[k] + rnorm(ctable[k], sd = sd[k])
  }
  structure(as.vector(x), modelName = modelName)
}