R/smerc_cluster-summary.R

In smerc: Statistical Methods for Regional Counts

#' Summary of \code{smerc_cluster} object
#'
#' Summary of object of class \code{smerc_cluster}.
#'
#' @param object An object of class \code{smerc_cluster}.
#' @inheritDotParams base::summary
#' @param idx An index vector indicating the elements of
#' \code{object$clusters} to print information for. The default
#' is all clusters.
#' @param digits Integer indicating the number of decimal places.
#' @method summary smerc_cluster
#' @return A \code{data.frame} with columns:
#' \item{nregions}{The number of regions in the cluster.}
#' \item{max_dist}{The maximum intercentroid distance between all the regions
#' in the cluster.}
#' \item{cases}{The number of cases in the cluster.}
#' \item{ex}{The expected number of cases in the cluster.}
#' \item{rr}{Relative risk in the cluster window. This is
#' \code{(cases/pop)/((total_cases - cases)/
#' (total_population - population))}.}
#' \item{stat}{The test statistic for the cluster.}
#' \item{p}{The p-value of the test statistic associated with the cluster.}
#' @export
#' @examples
#' data(nydf)
#' coords <- with(nydf, cbind(x, y))
#' out <- scan.test(
#'   coords = coords, cases = floor(nydf$cases),
#'   pop = nydf$pop, nsim = 49,
#'   longlat = TRUE, alpha = 0.2
#' )
#' # summarize all clusters
#' summary(out)
#' # summarize clusters 1 and 3
#' summary(out, idx = c(1, 3))
summary.smerc_cluster <- function(object, ...,
                                  idx = seq_along(object$clusters),
                                  digits = 1) {
  if (min(idx) < 1 | max(idx) > length(object$clusters)) {
    stop("invalid idx values")
  }
  nregions <- sapply(object$clusters[idx], function(i) length(i$locids))
  max_dist <- base::round(sget(object$clusters[idx], "max_dist"),
    digits = digits
  )
  cases <- sget(object$clusters[idx], "cases")
  ex <- base::round(sget(object$clusters[idx], "expected"),
    digits = digits
  )
  rr <- base::round(sget(object$clusters[idx], "rr"),
    digits = digits
  )
  stat <- base::round(sget(object$clusters[idx], "test_statistic"),
    digits = 1
  )
  p <- sget(object$clusters[idx], "pvalue")
  data.frame(
    nregions,
    max_dist,
    cases,
    ex,
    rr,
    stat,
    p
  )
}