R/ncomb.R

In dvir: Disaster Victim Identification

#' The number of assignments for DVI problem
#'
#' The number of victims and missing persons of each sex is given. The number of
#' possible assignments, i.e., the number of ways the victims can be identified
#' with the missing persons, is calculated.
#'
#' @param nVfemales Integer. The number of female victims.
#' @param nMPfemales Integer. The number of female missing persons.
#' @param nVmales Integer. The number of male victims.
#' @param nMPmales Integer. The number of male missing persons.
#'
#' @return The total number of possible assignments.
#'
#' @examples
#' 
#' # Example
#' m1 = ncomb(5,5,5,5) #
#' 
#' # Example: 3 male victims; 2 male missing persons.
#' # The number of a priori possible assignments is
#' m1 = ncomb(0,0,3,2) # 13
#'
#'
#' # Compare with the complete list of assignments
#' m2 = expand.grid.nodup(list(V1 = c("*", "M1", "M2"),
#'                             V2 = c("*", "M1", "M2"),
#'                             V3 = c("*", "M1", "M2")))
#' stopifnot(m1 == nrow(m2))
#'
#' @export
ncomb = function(nVfemales, nMPfemales, nVmales, nMPmales){

# The number of assignments for one sex
  fmoves = function(V,M){
    nmoves = 0
    for (k in 0:min(V,M)){
      n1 = choose(V, k)
      n2 = choose(M, k)
      nmoves = nmoves + n1*n2*factorial(k)
    }
    nmoves
  }
  
  if(nVfemales < 0| nMPfemales < 0 | nVmales < 0 | nMPmales < 0)
    stop2("All parameters must be non-negative integers")
  
# Calculate for each sex and multiply  
  nfe = fmoves(nVfemales, nMPfemales) 
  nma = fmoves(nVmales, nMPmales)
  nfe * nma
}