R/dmst.sim.R

In jfrench/smerc: Statistical Methods for Regional Counts

#' Perform \code{dmst.test} on simulated data
#'
#' \code{dmst.sim} efficiently performs
#' \code{\link{dmst.test}} on a simulated data set.  The
#' function is meant to be used internally by the
#' \code{\link{dmst.test}} function, but is informative for
#' better understanding the implementation of the test.
#' @param nn A list of distance-based nearest neighbors,
#' preferably from the \code{\link{nndist}} function.
#' @inheritParams scan.sim
#' @inheritParams dc.test
#' @inheritParams mst.all
#' @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
#' d <- gedist(coords, longlat = TRUE)
#' nn <- nndist(d, ubd = 0.05)
#' max_pop <- sum(pop) * 0.25
#' tsim <- dmst.sim(1, nn, ty, ex, nyw,
#'   pop = pop,
#'   max_pop = max_pop
#' )
dmst.sim <- function(nsim = 1, nn, ty, ex, w, pop, max_pop,
                     cl = NULL) {
  arg_check_sim(
    nsim = nsim, ty = ty, ex = ex,
    type = "poisson", tpop = NULL, w = w,
    ubpop = 0.1, static = FALSE
  )

  # compute max test stat for nsim simulated data sets
  tsim <- pbapply::pblapply(seq_len(nsim), function(i) {
    # simulate new data
    ysim <- stats::rmultinom(1, size = ty, prob = ex)
    tall <- mst.all(nn,
      cases = ysim, pop = pop, w = w,
      ex = ex, ty = ty,
      max_pop = max_pop, type = "maxonly",
      early = FALSE, nlinks = "one", progress = FALSE
    )
    max(tall)
  }, cl = cl)
  unlist(tsim, use.names = FALSE)
}