redaccion

R/GLNF.R

@@ -1,55 +1,55 @@
 #'Global and Local Searches for Net Flows to Sort
 #'
 #'This function applies the GLNF Sorting (Global Local Net Flow Sorting)
-#'algorithm to sort the alternatives into groups ordered according to the
-#'decision-maker's preferences for multiple criteria. GLNF sorting is based
-#'on PROMETHEE net flows and a set of limiting profiles. This algorithm starts
-#'from a global classification (global search) that is enhanced by two
+#'algorithm to classify the alternatives into ordered groups according to the
+#'decision-maker's preferences in multiple criteria context. GLNF sorting is
+#'based on PROMETHEE net flows and a set of limiting profiles. This algorithm
+#'starts from a global classification (global search) that is enhanced by two
 #'local searches, intra-categorical and inter-categorical.
 #' @usage
 #' GLNF(matrix_evaluation, data_criteria)
-#' @param matrix_evaluation A matrix that encompasses the values of the
-#' alternatives across various criteria, with the rows representing the
-#' alternatives and limiting profiles, while the columns correspond to the
-#' evaluation criteria.
-#' @param data_criteria A matrix with the parameter information (rows) for each
-#' criterion (columns). The parameters rows are in the following order: Function
-#' Type, Indifference Threshold, Preference Threshold, Objective and Weight.
+#' @param matrix_evaluation Matrix with the values for all the alternatives and
+#' limiting profiles are row and columns correspond to the evaluation criteria.
+#' @param data_criteria Matrix with the parameter information (rows) for each
+#' criterion (columns). The rows of parameters are in the following order:
+#' Function Type, Indifference Threshold, Preference Threshold, Objective and
+#' Weight.
 #' @references
 #' Barrera, F., Segura, M., & Maroto, C. (2023). Multicriteria sorting method
 #' based on global and local search for supplier segmentation. International
 #' Transactions in Operational Research. Online https://doi.org/10.1111/itor.13288
 #' @return
-#' -`Global` A matrix with the results of the global search where the positive
-#' flow, the negative flow, the net flow and its preclassification are defined
-#' for each alternative.
+#' -`Global` Matrix with the results of the global search where positive,
+#' negative and net flow, and its preclassification are defined for each
+#' alternative.
 #'
-#' -`Local1` Matrices with the results of the first local search, where the
-#' alternatives are separated according to their sign from the net flows
-#' obtained after applying PROMETHEE to each group formed in the global search.
+#' -`Local1` Matrices with the results of the first local search. PROMETHEE is
+#' applied to each group obtained in the global search. The alternatives are
+#' divided according to their positive or negative sign from the net flows
+#' obtained from PROMETHEE.
 #'
-#' -`Local2` Matrices with the results of the second local search, where where
-#' the alternatives are separated according to their sign from net flows are
-#' obtained after applying PROMETHEE between each pair of categories.
+#' -`Local2` Matrices with the results of the second local search, where the
+#' alternatives are divided according to their sign from net flows are obtained
+#' after applying PROMETHEE between each pair of neighbour categories.
 #'
-#' -`Class` Final classification results.
+#' -`Class` Final classification of the alternatives results.
 #' @export
 #' @seealso \code{\link{PROMETHEEII}}
 #' @details
 #' - The Limiting Profiles should be presented as rows in the matrix_evaluation.
 #' The name must start with the letter "r" followed by the profile number
 #' (e.g., "r1", "r2").
-#' - For a total of k categories, there should be k + 1 limiting profiles.
-#'  Where to create k groups the set of limit profiles "r1 preferred to r2,...,
-#'  preferred to r(k+1)" is defined.
-#' - The preference function types are as follows: "linear", "v-shape", "usual",
-#' "u-shape", "level" and "gaussian".
+#' - For k categories, there should be k + 1 limiting profiles. To create k
+#' groups the set of limit profiles are defined, where r1 is preferred to
+#' r2,...,preferred to r(k+1).
+#' - The types of preference function are as follows: "linear", "v-shape",
+#' "usual", "u-shape", "level" and "gaussian".
 #' - The preference and indifference thresholds depend on the type of function
 #' selected. The preference threshold requires definition (is non-zero) for all
 #' functions except for "usual" and "u-shaped". The indifference threshold is
 #' non-zero for "linear", "level" and "u-shaped" functions.
-#' - In the objective criterion write "max" to maximize or "min" to minimize.
-#' - The sum of the weights of all the criteria must be equal to 1.
+#' - In the objective write "max" to maximize or "min" to minimize.
+#' - The sum of the weights of all criteria must be equal to 1.
 #' @examples
 #'matrix_evaluation <- data.frame (
 #'
@@ -104,7 +104,7 @@ GLNF <- function(matrix_evaluation, data_criteria) {
   phi_prof <- lim_prof$Phi
   data <- data[!grepl("^r\\d+", data$Alternative), ]
   data$Category <- NA
-  # Assign categories according to Phi
+  # Assigning categories according to Phi
   for (i in 1:(num_categories)) {
     lim_sup <- phi_prof[i]
     lim_inf <- phi_prof[i + 1]
@@ -116,7 +116,7 @@ GLNF <- function(matrix_evaluation, data_criteria) {
   Global_Search <- data
   lim_prof$Category <- NA
   Global_Search <- rbind(Global_Search, lim_prof)
-  #create subarrays for each group according to the global search
+  # Creating subarrays for each group according to the global search
   Global_Search_categories <- list()
   for (i in 1:(num_categories)){
     Alt_category <- data[data$Category == i, ]
@@ -126,13 +126,13 @@ GLNF <- function(matrix_evaluation, data_criteria) {
     category_matrix <- matrix_evaluation[matrix_evaluation$Alternative %in% ids_category, ]
     Global_Search_categories[[paste0("C", i)]] <- category_matrix
   }
-  #Step 3. Local search number 1
+  # Step 3. Local search 1
   Local1_categories <- list()
   for (i in seq_along(Global_Search_categories)) {
     matrix_name <- names(Global_Search_categories[i])
     ME <- matrix_evaluation
     matrix_evaluation <- Global_Search_categories[[i]]
-    #PROMETHEE
+    # PROMETHEE
     RS <- PROMETHEEII(matrix_evaluation, data_criteria)
     data <- as.data.frame(RS[[1]])
     data$Phi <- as.numeric(data$Phi)
@@ -143,7 +143,7 @@ GLNF <- function(matrix_evaluation, data_criteria) {
     Local1_categories[[paste0("LocalSearch1 ", matrix_name, "(-)")]] <- local_neg
     matrix_evaluation <- ME
   }
-  #Step 4. Local search number 2
+  # Step 4. Local search 2
   Local2_categories <- list()
   for (i in 1:(num_categories - 1)) {
     category_higher <- paste0("LocalSearch1 C", i, "(-)")
@@ -165,7 +165,7 @@ GLNF <- function(matrix_evaluation, data_criteria) {
     Local2_categories[[paste0("LocalSearch2 C", i, "- & C", i + 1, "+ (-)")]] <- local_neg
     matrix_evaluation <- ME
   }
-  #Step 5. Final classification
+  # Step 5. Final classification
   Classifica <- data.frame(Alternative = matrix_evaluation$Alternative, Category = NA)
   # Classification: C1, Ck, and other groups.
   for (i in seq_along(Global_Search_categories)) {

R/PROMETHEEII.R

@@ -1,10 +1,10 @@
 #' PROMETHEE Outranking Method
 #'
 #'PROMETHEE is a multicriteria method that quantifies preference relationships
-#'and obtains net flows, generating a ranking that reflects the decision-maker's
-#'preferences. This function applies PROMETHEE I (partial ranking) and
-#'PROMETHEE II (full ranking). This function can be used with a large number of
-#'alternatives.
+#''and obtains the positive, negative and net flows of the alternatives,
+#'generating a rankings that reflect the decision-maker's preferences. This
+#'function applies PROMETHEE I (partial ranking) and PROMETHEE II (full ranking).
+#'This function can be used with a large number of alternatives.
 #'
 #' @param matrix_evaluation A matrix that encompasses the values of the
 #' alternatives across various criteria, with the rows representing the

README.md

@@ -30,11 +30,11 @@ devtools::install_github("ifelipebj/PrometheeTools")
 
 #### PROMETHEE Outranking Method
 
-PROMETHEE is a multicriteria method that quantifies preference relationships
-and obtains net flows, generating a ranking that reflects the decision-maker's
-preferences. This function applies PROMETHEE I (partial ranking) and
-PROMETHEE II (full ranking). This function can be used with a large number of
-alternatives.
+PROMETHEE is a multicriteria method that quantifies preference relationships and
+obtains the positive, negative and net flows of the alternatives, generating a
+rankings that reflect the decision-maker's preferences. This function applies
+PROMETHEE I (partial ranking) and PROMETHEE II (full ranking). This function can 
+be used with a large number of alternatives.
 
 Usage:
 
@@ -44,10 +44,10 @@ PROMETHEEII(matrix_evaluation, data_criteria)
 #### Global and Local Searches for Net Flows to Sort
 
 This function applies the GLNF Sorting (Global Local Net Flow Sorting)
-algorithm to sort the alternatives into groups ordered according to the
-decision-maker's preferences for multiple criteria. GLNF sorting is based
-on PROMETHEE net flows and a set of limiting profiles. This algorithm starts
-from a global classification (global search) that is enhanced by two
+algorithm to classify the alternatives into ordered groups according to the
+decision-maker's preferences in multiple criteria context. GLNF sorting is
+based on PROMETHEE net flows and a set of limiting profiles. This algorithm
+starts from a global classification (global search) that is enhanced by two
 local searches, intra-categorical and inter-categorical.
 
 Usage:
@@ -120,7 +120,7 @@ your criteria.
 - `Preference Threshold` is the preference threshold for the criterion.
 - `Objective` specifies whether the criterion's objective is to maximize ("max") 
 or minimize ("min").
-- `Weight` represents the weight of the criterion in the analysis.The sum of the weights of all the criteria must be equal to 1.
+- `Weight` represents the weight of the criterion in the analysis.The sum of the weights of all criteria must be equal to 1.
 
 The preference and indifference thresholds depend on the type of function
 selected. The preference threshold is non-zero for alL functions except for