MCDA (version 0.1.0)

plotMRSortSortingProblem: Plot the categories and assignments of an Electre TRI-like sorting problem (via separation profiles).

Description

The profiles shown are the separation profiles between the classes. They are stored as the lower profiles of the categories.

Usage

plotMRSortSortingProblem(
  performanceTable,
  categoriesLowerProfiles,
  categoriesRanks,
  assignments,
  criteriaMinMax,
  criteriaUBs,
  criteriaLBs,
  categoriesDictators = NULL,
  categoriesVetoes = NULL,
  majorityRule = NULL,
  criteriaWeights = NULL,
  majorityThreshold = NULL,
  alternativesIDs = NULL,
  criteriaIDs = NULL,
  legendRatio = 0.2
)

Arguments

performanceTable

Matrix or data frame containing the performance table. Each row corresponds to an alternative, and each column to a criterion. Rows (resp. columns) must be named according to the IDs of the alternatives (resp. criteria).

categoriesLowerProfiles

Matrix containing, in each row, the lower profiles of the categories (the separation profiles in fact). The columns are named according to the criteria, and the rows are named according to the categories. The index of the row in the matrix corresponds to the rank of the category.

categoriesRanks

A vector containing the ranks of the categories (1 for the best, with higher values for increasingly less preferred categories). The vector needs to be named with the categories names, whereas the ranks need to be a range of values from 1 to the number of categories.

assignments

Vector containing the assignments (IDs of the categories) of the alternatives to the categories. The elements are named according to the alternatives.

criteriaMinMax

Vector containing the preference direction on each of the criteria. "min" (resp. "max") indicates that the criterion has to be minimized (maximized). The elements are named according to the IDs of the criteria.

criteriaUBs

Vector containing the upper bounds of the criteria to be considered for the plotting. The elements are named according to the IDs of the criteria.

criteriaLBs

Vector containing the lower bounds of the criteria to be considered for the plotting. The elements are named according to the IDs of the criteria.

categoriesDictators

Matrix containing, in each row, the lower dictator profiles of the categories. The columns are named according to the criteria, and the rows are named according to the categories. The index of the row in the matrix corresponds to the rank of the category.

categoriesVetoes

Matrix containing, in each row, the lower veto profiles of the categories. The columns are named according to the criteria, and the rows are named according to the categories. The index of the row in the matrix corresponds to the rank of the category.

majorityRule

A string containing one of the following values: 'V' , 'D', 'v', 'd', 'dV', 'Dv', 'dv'. This indicates the type of majority rule that will be used by the MRSort model. 'V' stands for MRSort with vetoes, 'D' stands for MRSort with dictators, 'v' stands for MRSort with vetoes weakened by dictators, 'd' stands for MRSort with dictators weakened by vetoes, 'dV' stands for MRSort with vetoes dominating dictators, 'Dv' stands for MRSort with dictators dominating vetoes, while 'dv' stands for MRSort with conflicting vetoes and dictators.

criteriaWeights

Vector containing the criteria weights. The elements are named according to the IDs of the criteria.

majorityThreshold

A value corresponding to the majority threshold. Along with the criteria weights, this value is used to determine when a coalition of criteria is sufficient in order to assert that an alternative is at least as good as a category profile.

alternativesIDs

Vector containing IDs of alternatives, according to which the datashould be filtered.

criteriaIDs

Vector containing IDs of criteria, according to which the data should be filtered.

legendRatio

The ratio between the legend and plot heights. By defaut 0.2.

Examples

# the performance table

performanceTable <- rbind(
  c(1,10,1),
  c(4,20,2),
  c(2,20,0),
  c(6,40,0),
  c(30,30,3))

rownames(performanceTable) <- c("RER","METRO1","METRO2","BUS","TAXI")

colnames(performanceTable) <- c("Price","Time","Comfort")

# lower profiles of the categories 
# (best category in the first position of the list)

categoriesLowerProfiles <- rbind(c(3, 11, 3),c(7, 25, 2),c(30,30,0))

colnames(categoriesLowerProfiles) <- colnames(performanceTable)

rownames(categoriesLowerProfiles)<-c("Good","Medium","Bad")

categoriesRanks <-c(1,2,3)

names(categoriesRanks) <- c("Good","Medium","Bad")

# criteria to minimize or maximize

criteriaMinMax <- c("min","min","max")

names(criteriaMinMax) <- colnames(performanceTable)

# lower bounds of the criteria for the determination of value functions

criteriaLBs=c(0,5,0)

names(criteriaLBs) <- colnames(performanceTable)

# upper bounds of the criteria for the determination of value functions

criteriaUBs=c(50,50,4)

names(criteriaUBs) <- colnames(performanceTable)

# weights

criteriaWeights <- c(1,3,2)

names(criteriaWeights) <- colnames(performanceTable)

assignments <- assignments<-MRSort(performanceTable, 
                                   categoriesLowerProfiles, 
                                   categoriesRanks,
                                   criteriaWeights, 
                                   criteriaMinMax, 3)

names(assignments) <- rownames(performanceTable)

plotMRSortSortingProblem(performanceTable, categoriesLowerProfiles, 
                         categoriesRanks, assignments, criteriaMinMax, 
                         criteriaUBs, criteriaLBs)