R/WSA.R

In jy-r/Rmcdm: Multicriteria decision making methods for R

#' WSA
#' 
#' wsa mcdm method
#' @param matrix data.frame (n \times m) of n alternatives and m crierias
#' @param weights data.frame of weights size 1 \times n 
#' @param which_is_min logical vector for which criterias should be minimalized
#' @import dplyr
#' @export WSA
#' @examples 
#' matrix <- structure(list(edu = c(4, 2, 3, 4, 4, 3), eng = c(1, 2, 2, 3, 2, 3), 
#'                          pc = c(5, 4, 5, 2, 2, 3), prax = c(1, 0, 3, 2, 1, 3), 
#'                          int = c(85, 80, 85, 70, 65, 90)), .Names = c("edu", "eng","pc", "prax", "int"),
#'                           row.names = c(NA, -6L), class = "data.frame")
#' weights <- structure(list(X1 = c(0.2, 0.333333333333333, 0.133333333333333, 
#'                                 0.0666666666666667, 0.266666666666667)),
#'                                  .Names = "X1", row.names = c(NA, -5L), class = "data.frame")
#' which_is_min = rep(0,5)
#' WSA(matrix, weights, which_is_min)


WSA <- function(matrix, weights, which_is_min) {
  wsa <- matrix
  n = nrow(matrix)
    
    
  vmax <- wsa %>% summarise_all(max)
  vmax <- matrix(unlist(rep(vmax, n)), ncol = ncol(matrix), byrow = TRUE)
  
  vmin <- wsa %>% summarise_all(min)
  vmin <- matrix(unlist(rep(vmin, n)), ncol = ncol(matrix), byrow = TRUE)
  
  weights.wsa <-
    matrix(unlist(rep(weights, n)), ncol = ncol(matrix), byrow = TRUE)
  
  
  wsa <- as.matrix(wsa)
  
  wsa[, which_is_min] <-
    (vmax[, which_is_min] - wsa[, which_is_min]) / (vmax[, which_is_min] - vmin[, which_is_min])
  wsa[, !which_is_min] <-
    (wsa[, !which_is_min] - vmin[, !which_is_min]) / (vmax[, !which_is_min] - vmin[, !which_is_min])
  
  wsa <- wsa * weights.wsa
  
  wsa.sum <- rowSums(wsa)
  
  wsa <- as.data.frame(wsa)
  

  
  result <-
    data.frame(wsa = wsa.sum,
               rank = rank(-wsa.sum))
  return(result)
}