R/edmst.zones.R

Defines functions edmst.zones

Documented in edmst.zones

#' Determine zones for the early stopping dynamic Minimum
#' Spanning Tree scan test
#'
#' \code{edmst.zones} determines the zones for the early
#' stopping Dynamic Minimum Spanning Tree scan test
#' (\code{\link{edmst.test}}).  The function returns the
#' zones, as well as the associated test statistic, cases in
#' each zone, the expected number of cases in each zone, and
#' the population in each zone.
#'
#' Every zone considered must have a total population less
#' than \code{ubpop * sum(pop)}.  Additionally, the maximum
#' intercentroid distance for the regions within a zone must
#' be no more than \code{ubd * the maximum intercentroid
#' distance across all regions}.
#' @inheritParams dmst.test
#' @inheritParams mst.all
#' @inheritParams flex.zones
#' @inherit dc.zones return
#' @author Joshua French
#' @references Costa, M.A. and Assuncao, R.M. and Kulldorff,
#'   M. (2012) Constrained spanning tree algorithms for
#'   irregularly-shaped spatial clustering, Computational
#'   Statistics & Data Analysis, 56(6), 1771-1783.
#'   <doi:10.1016/j.csda.2011.11.001>
#' @export
#' @examples
#' data(nydf)
#' data(nyw)
#' coords = as.matrix(nydf[,c("longitude", "latitude")])
#' # find zone with max statistic starting from each individual region
#' all_zones = edmst.zones(coords, cases = floor(nydf$cases),
#'                         nydf$pop, w = nyw, ubpop = 0.25,
#'                         ubd = .25, longlat = TRUE)
edmst.zones = function(coords, cases, pop, w,
                       ex = sum(cases) / sum(pop) * pop,
                       ubpop = 0.5, ubd = 1, longlat = FALSE,
                       cl = NULL, progress = TRUE) {
  # sanity checking
  arg_check_dmst_zones(coords = coords, cases = cases,
                       pop = pop, w = w, ex = ex,
                       ubpop = ubpop, ubd = ubd,
                       longlat = longlat, type = "all",
                       progress = progress)
  # setup various arguments and such
  ty = sum(cases)   # total number of cases
  # intercentroid distances
  d = sp::spDists(as.matrix(coords), longlat = longlat)
  # upperbound for population in zone
  max_pop = ubpop * sum(pop)
  # find all neighbors from each starting zone within distance upperbound
  nn = nndist(d, ubd)
  # get zones and relevant information
  out = mst.all(neighbors = nn, cases = cases, pop = pop,
                w = w, ex = ex, ty = ty, max_pop = max_pop,
                type = "all", nlinks = "one", early = TRUE,
                cl = cl, progress = progress)
  # return results of mst.all in a nicely formatted list
  prep.mst(out)
}

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smerc documentation built on Nov. 23, 2021, 5:07 p.m.