R/uls.sim.R

In smerc: Statistical Methods for Regional Counts

#' Perform \code{uls.test} on simulated data
#'
#' \code{uls.sim} efficiently performs
#' \code{\link{uls.test}} on a simulated data set.  The
#' function is meant to be used internally by the
#' \code{\link{uls.test}} function, but is informative for
#' better understanding the implementation of the test.
#'
#' @inheritParams scan.sim
#' @inheritParams uls.test
#' @inherit scan.sim return
#' @export
#'
#' @examples
#' data(nydf)
#' data(nyw)
#' coords <- with(nydf, cbind(longitude, latitude))
#' cases <- floor(nydf$cases)
#' pop <- nydf$pop
#' ty <- sum(cases)
#' ex <- ty / sum(pop) * pop
#' tsim <- uls.sim(1, ty, ex, nyw, pop = pop, ubpop = 0.5)
uls.sim <- function(nsim = 1, ty, ex, w, pop, ubpop,
                    type = "poisson", check.unique = FALSE,
                    cl = NULL) {
  tpop <- sum(pop)
  arg_check_sim(
    nsim = nsim, ty = ty, ex = ex, type = type,
    tpop = tpop, w = w, ubpop = ubpop,
    static = FALSE
  )

  # compute max test stat for nsim simulated data sets
  tsim <- pbapply::pblapply(seq_len(nsim), function(i) {
    # simulate new data
    ysim <- c(stats::rmultinom(1, size = ty, prob = ex))
    zones <- uls.zones(
      cases = ysim, pop = pop, w = w,
      ubpop = ubpop,
      check.unique = check.unique
    )
    # compute test statistics for each zone
    yin <- zones.sum(zones, ysim)
    if (type == "poisson") {
      ein <- zones.sum(zones, ex)
      tall <- stat.poisson(yin, ty - yin, ein, ty - ein)
    } else if (type == "binomial") {
      popin <- zones.sum(zones, pop)
      tall <- stat.binom(
        yin, ty - yin, ty,
        popin, tpop - popin, tpop
      )
    }
    max(tall)
  }, cl = cl)
  unlist(tsim, use.names = FALSE)
}