R/calcstats.R

In redist: Simulation Methods for Legislative Redistricting

#' Segregation index calculation for MCMC redistricting.
#'
#' \code{redist.segcalc} calculates the dissimilarity index of segregation (see
#' Massey & Denton 1987 for more details) for a specified subgroup under any
#' redistricting plan.
#'
#' @param plans A matrix of congressional district assignments or a
#' redist object.
#' @param group_pop A vector of populations for some subgroup of interest.
#' @param total_pop A vector containing the populations of each geographic unit.
#'
#' @return \code{redist.segcalc} returns a vector where each entry is the
#' dissimilarity index of segregation (Massey & Denton 1987) for each
#' redistricting plan in \code{algout}.
#'
#' @references Fifield, Benjamin, Michael Higgins, Kosuke Imai and Alexander
#' Tarr. (2016) "A New Automated Redistricting Simulator Using Markov Chain
#' Monte Carlo." Working Paper. Available at
#' \url{http://imai.princeton.edu/research/files/redist.pdf}.
#'
#' Massey, Douglas and Nancy Denton. (1987) "The Dimensions of Social
#' Segregation". Social Forces.
#'
#' @examples
#' \donttest{
#' data(fl25)
#' data(fl25_enum)
#' data(fl25_adj)
#'
#' ## Get an initial partition
#' init_plan <- fl25_enum$plans[, 5118]
#'
#' ## 25 precinct, three districts - no pop constraint ##
#' alg_253 <- redist.flip(
#'     adj = fl25_adj, total_pop = fl25$pop,
#'     init_plan = init_plan, nsims = 10000
#' )
#'
#' ## Get Republican Dissimilarity Index from simulations
#' rep_dmi_253 <- redist.segcalc(alg_253, fl25$mccain, fl25$pop)
#' }
#' @concept analyze
#' @export
redist.segcalc <- function(plans, group_pop, total_pop) {

    ## If redist object, get the partitions entry
    if (all(class(plans) == "redist")) {
        plans <- plans$plans
    }

    if (!((nrow(plans) == length(group_pop)) &
        (length(group_pop) == length(total_pop)) &
        (length(total_pop) == nrow(plans)))) {
        cli_abort("Please make sure there is a population entry for each geographic unit")
    }

    nd <- dplyr::n_distinct((plans[, 1]))
    out <- redistmetrics::seg_dissim(plans,
        shp = data.frame(), group_pop = group_pop,
        total_pop = total_pop
    )
    out[seq(1, length(out), by = nd)]
}