R/Noptim.r

In prevR: Estimating Regional Trends of a Prevalence from a DHS and Similar Surveys

#' Suggested optimal value for N
#'
#' Based on previous simulation work, the function suggests an optimal
#' value for the N parameter based on national prevalence, the total number
#' of observations and the number of clusters. See Larmarange et al. 2011
#' for more details.
#'
#' @param object object of class [prevR-class].
#'
#' @return an integer.
#'
#' @references
#' Larmarange Joseph, Vallo Roselyne, Yaro Seydou, Msellati Philippe and Meda
#' Nicolas (2011) "Methods for mapping regional trends of HIV prevalence from
#' Demographic and Health Surveys (DHS)",
#' \emph{Cybergeo: European Journal of Geography}, no 558,
#' \url{https://journals.openedition.org/cybergeo/24606},
#' DOI: 10.4000/cybergeo.24606.
#'
#' @examples
#' Noptim(fdhs)
#'
#' @export
#' @keywords stat

Noptim <- function(object) {
  clusters <- slot(object, "clusters")
  clustersNumber <- nrow(clusters)
  ObservationNumber <- sum(clusters$n)
  isWeightedData <- !any(is.na(match(c("wn", "wpos"), names(clusters))))
  if (isWeightedData) {
    nationalPrev <- 100 * sum(clusters$wpos, na.rm = TRUE) /
      sum(clusters$wn, na.rm = TRUE)
  } else {
    nationalPrev <- 100 * sum(clusters$pos, na.rm = TRUE) /
      sum(clusters$n, na.rm = TRUE)
  }
  if (nationalPrev > 50) {
    nationalPrev <- (100 - nationalPrev)
  }
  round(
    14.172 *
      ObservationNumber^0.419 *
      nationalPrev^-0.361 *
      clustersNumber^0.037 - 91.011
  )
}