Plot decision tree

Description

Plot decision tree

Usage

AHP.decision.tree.plot(
  A,
  comparing.competitors,
  results,
  vertex_font = 1.2,
  edge_font = 1,
  asp = 0.8,
  max_width = 5,
  vertex_size = 50
)

Arguments

Value

the decision tree plot

Apply AHP on the matrices

Description

Apply AHP on the matrices

Usage

apply.AHP(A, comparing.competitors)

Arguments

Value

a list containing I. The weight of each criteria II. The criteria alternative unweighted matrix III. The weighted scores matrix IV. Competitor final scores

Examples

data <- read.csv(system.file("extdata", "AHP_input_file.csv", package = "RMCDA"), header=FALSE)
mat.lst <- read.csv.AHP.matrices(data)
mat.lst[[1]]->A
mat.lst[[2]]->comparing.competitors
results<- apply.AHP(A, comparing.competitors)

Apply Analytical Network Process (ANP) on data

Description

Apply Analytical Network Process (ANP) on data

Usage

apply.ANP(A, comparing.competitors, power)

Arguments

Value

the limiting super matrix

Examples

data <- read.csv(system.file("extdata", "AHP_input_file.csv", package = "RMCDA"), header=FALSE)
mat.lst <- read.csv.AHP.matrices(data)
mat.lst[[1]]->A
mat.lst[[2]]->comparing.competitors
apply.ANP(A, comparing.competitors, 2)

Apply Additive Ratio Assessment (ARAS)

Description

Apply Additive Ratio Assessment (ARAS)

Usage

apply.ARAS(mat, weights, beneficial.vector)

Arguments

Value

a vector containing the utility degree related to each alternative, higher utility indicates better ranking.

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)
colnames(mat)<-c("Toughness Index",	"Yield Strength",	"Young's Modulus",
                 "Density",	"Thermal Expansion",	"Thermal Conductivity",	"Specific Heat")
rownames(mat)<-c("AI 2024-T6", "AI 5052-O","SS 301 FH",
"SS 310-3AH",
"Ti-6AI-4V",
"Inconel 718",
"70Cu-30Zn")
weights <- c(0.28, 0.14, 0.05, 0.24, 0.19, 0.05, 0.05)
beneficial.vector<-c(1,2,3)
apply.ARAS(mat, weights, beneficial.vector)

Function to apply BORDA method to data

Description

This function implements a simple Borda count approach for a decision matrix. It computes a rank for each criterion and then sums these ranks for each alternative. By specifying which columns are beneficial (i.e., higher values preferred), it automatically treats the remaining columns as non-beneficial (i.e., lower values preferred).

Usage

apply.BORDA(mat, beneficial.vector)

Arguments

Value

A numeric vector of total Borda scores for each alternative, in the original row order.

Examples

# Create a small decision matrix (4 alternatives x 3 criteria)
mat <- matrix(c(
  5, 9, 2,
  7, 3, 8,
  6, 5, 4,
  4, 7, 9
), nrow = 4, byrow = TRUE)

beneficial.vector <- c(1, 3)


borda_scores <- apply.BORDA(mat, beneficial.vector)
borda_scores

Function for applying the Best-Worst Method

Description

Function for applying the Best-Worst Method

Usage

apply.BWM(
  criteria.lst,
  worst.criteria,
  best.criteria,
  best.criteria.preference,
  worst.criteria.preference
)

Arguments

Value

the result of BWM

Examples

c <- c("C1", "C2", "C3")
w <- "C1"
b <- "C3"
bcp <- c(8, 2, 1)
wcp <- c(1, 5, 8)
apply.BWM(c, w, b, bcp, wcp)

Apply CILOS Weighting Method

Description

Apply CILOS Weighting Method

Usage

apply.CILOS(mat, beneficial.vector)

Arguments

Value

A numeric vector of calculated weights.

Examples

mat <- matrix(
  c(75.5, 95, 770, 187, 179, 239, 237,
    420, 91, 1365, 1120, 875, 1190, 200,
    74.2, 70, 189, 210, 112, 217, 112,
    2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
    21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
    0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
    0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06),
  nrow = 7, byrow = TRUE
)
beneficial.vector <- c(1, 2, 3, 6, 7)
apply.CILOS(mat, beneficial.vector)

Apply COmbined COmpromise SOlution (COCOSO)

Description

Apply COmbined COmpromise SOlution (COCOSO)

Usage

apply.COCOSO(mat, weights, beneficial.vector)

Arguments

Value

a vector containing the aggregated appraisal scores

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)
colnames(mat)<-c("Toughness Index",	"Yield Strength",	"Young's Modulus",
                 "Density",	"Thermal Expansion",	"Thermal Conductivity",	"Specific Heat")
rownames(mat)<-c("AI 2024-T6", "AI 5052-O","SS 301 FH",
"SS 310-3AH",
"Ti-6AI-4V",
"Inconel 718",
"70Cu-30Zn")
weights <- c(0.28, 0.14, 0.05, 0.24, 0.19, 0.05, 0.05)
beneficial.vector<-c(1,2,3)
apply.COCOSO(mat, weights, beneficial.vector)

Apply Combinative Distance-based Assessment (CODAS)

Description

Apply Combinative Distance-based Assessment (CODAS)

Usage

apply.CODAS(mat, weights, beneficial.vector, psi)

Arguments

Value

a vector containing the calculated quantitative utility

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)
colnames(mat)<-c("Toughness Index",	"Yield Strength",	"Young's Modulus",
                 "Density",	"Thermal Expansion",	"Thermal Conductivity",	"Specific Heat")
rownames(mat)<-c("AI 2024-T6", "AI 5052-O","SS 301 FH",
"SS 310-3AH",
"Ti-6AI-4V",
"Inconel 718",
"70Cu-30Zn")
weights <- c(0.28, 0.14, 0.05, 0.24, 0.19, 0.05, 0.05)
beneficial.vector<-c(1,2,3)
psi <- 0.02
apply.CODAS(mat, weights, beneficial.vector, psi)

Apply Copeland Method

Description

Apply Copeland Method

Usage

apply.COPELAND(mat, beneficial.vector)

Arguments

Value

A numeric vector containing the calculated Copeland scores for each alternative.

Examples

mat <- matrix(c(80, 60, 90,
                75, 85, 95,
                70, 65, 85,
                60, 75, 80),
              nrow = 4, byrow = TRUE)
colnames(mat) <- c("Criterion 1", "Criterion 2", "Criterion 3")
beneficial.vector <- c(1, 2, 3)
apply.COPELAND(mat, beneficial.vector)

Apply COmplex PRoportional ASsessment (COPRAS) method

Description

Apply COmplex PRoportional ASsessment (COPRAS) method

Usage

apply.COPRAS(mat, weights, beneficial.vector)

Arguments

Value

a vector containing the calculated quantitative utility

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)
colnames(mat)<-c("Toughness Index",	"Yield Strength",	"Young's Modulus",
"Density",	"Thermal Expansion",	"Thermal Conductivity",	"Specific Heat")
rownames(mat)<-c("AI 2024-T6",
"AI 5052-O",
"SS 301 FH",
"SS 310-3AH",
"Ti-6AI-4V",
"Inconel 718",
"70Cu-30Zn")
weights <- c(0.28, 0.14, 0.05, 0.24, 0.19, 0.05, 0.05)
beneficial.vector<-c(1,2,3)
apply.COPRAS(mat, weights, beneficial.vector)

Function to apply CRiteria Aggregation for Decision Information Synthesis (CRADIS)

Description

Function to apply CRiteria Aggregation for Decision Information Synthesis (CRADIS)

Usage

apply.CRADIS(mat, weights, beneficial.vector)

Arguments

Value

a vector containing the preference values for each alternative

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)
colnames(mat) <- c("Toughness Index", "Yield Strength", "Young's Modulus",
                  "Density", "Thermal Expansion", "Thermal Conductivity", "Specific Heat")
rownames(mat) <- c("AI 2024-T6", "AI 5052-O", "SS 301 FH",
                   "SS 310-3AH", "Ti-6AI-4V", "Inconel 718", "70Cu-30Zn")
weights <- c(0.28, 0.14, 0.05, 0.24, 0.19, 0.05, 0.05)
beneficial.vector <- c(1, 2, 3)
apply.CRADIS(mat, weights, beneficial.vector)

Apply CRITIC on comparison matrix

Description

Apply CRITIC on comparison matrix

Usage

apply.CRITIC(A)

Arguments

Value

the weight percentages related to matrix A obtained through the CRITIC method

Examples

A <- matrix(c(250, 200, 300, 275,
 225, 16, 16, 32,
  32, 16, 12, 8,
   16, 8, 16, 5,
    3, 4, 4, 2), nrow=5, ncol=4)
colnames(A)<-c("Price", "Storage space", "Camera", "Looks")
rownames(A)<-paste0("Mobile ", seq(1, 5, 1))
A[,"Price"] <- -A[,"Price"]
apply.CRITIC(A)

Apply DEMATEL method

Description

Apply DEMATEL method

Usage

apply.DEMATEL(comparisons.mat)

Arguments

Value

a list containing two vectors one holding D-R and the other D+R

Examples

comparisons.mat <- matrix(c(0, 3, 3, 4,
1, 0, 2, 1,
1, 2, 0, 2,
1, 2, 1, 0), nrow=4)
rownames(comparisons.mat)<-c("Price/cost", "Storage Space", "Camera", "Processor")
colnames(comparisons.mat)<-c("Price/cost", "Storage Space", "Camera", "Processor")
apply.DEMATEL(comparisons.mat)

Function to apply the Evaluation based on Distance from Average Solution (EDAS) method

Description

Function to apply the Evaluation based on Distance from Average Solution (EDAS) method

Usage

apply.EDAS(mat, weights)

Arguments

Value

the AS_i index from EDAS from which the final ranking can be found

Examples

mat <- matrix(c(250, 200, 300, 275, 225,
16, 16, 32, 32, 16,
12, 8, 16, 8, 16,
5, 3, 4, 4, 2), nrow=5)
colnames(mat)<-c("Price/cost", "Storage Space", "Camera", "Looks")
rownames(mat)<-paste0("Mobile", 1:5)
mat[,"Price/cost"]<--mat[,"Price/cost"]
weights <- c(0.35, 0.25, 0.25, 0.15)
apply.EDAS(mat, weights)

Apply ELECTRE I method

Description

Apply ELECTRE I method

Usage

apply.ELECTRE1(mat, weights)

Arguments

Value

a list containing three matrices, the first one is the intersection of concordance and discordance matrices, the second one is the concordance matrix, and the third one is the discordance matrix.

Examples

mat <- matrix(c(25, 10, 30, 20, 30, 10, 15, 20, 30, 30, 30, 10), nrow=3)
colnames(mat)<-c("c1", "c2", "c3", "c4")
rownames(mat)<-c("a1", "a2", "a3")
weights <- c(0.2, 0.15, 0.4, 0.25)

# Apply ELECTRE I method
results <- apply.ELECTRE1(mat, weights)

Apply fuzzy AHP on criteria comparison matrix

Description

Apply fuzzy AHP on criteria comparison matrix

Usage

apply.FAHP(A)

Arguments

Value

the fuzzy weights for each criteria

Examples

# example code
data <- read.csv(system.file("extdata", "AHP_input_file.csv", package = "RMCDA"), header=FALSE)
mat.lst <- read.csv.AHP.matrices(data)
mat.lst[[1]]->A
result <- apply.FAHP(A)

Apply Grey Relational Analysis (GRA) method

Description

Apply Grey Relational Analysis (GRA) method

Usage

apply.GRA(mat, weights, beneficial.vector, epsilon = 0.5)

Arguments

Value

a vector containing the calculated GRA scores

Examples

mat <- matrix(c(80, 60, 90,
                75, 85, 95,
                70, 65, 85,
                60, 75, 80),
              nrow = 4, byrow = TRUE)
colnames(mat) <- c("Criterion 1", "Criterion 2", "Criterion 3")
weights <- c(0.4, 0.3, 0.3)
beneficial.vector <- c(1, 2, 3)
apply.GRA(mat, weights, beneficial.vector)

Apply Integrated Determination of Objective Criteria Weights (IDOCRIW) method

Description

Apply Integrated Determination of Objective Criteria Weights (IDOCRIW) method

Usage

apply.IDOCRIW(mat, beneficial.vector)

Arguments

Value

a vector containing the calculated weights for the criteria

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)
colnames(mat) <- c("Toughness Index", "Yield Strength", "Young's Modulus",
"Density", "Thermal Expansion", "Thermal Conductivity", "Specific Heat")
rownames(mat) <- c("AI 2024-T6", "AI 5052-O", "SS 301 FH",
"SS 310-3AH", "Ti-6AI-4V", "Inconel 718", "70Cu-30Zn")
beneficial.vector <- c(1, 2, 3, 6, 7)
apply.IDOCRIW(mat, beneficial.vector)

Apply Multi-Attributive Border Approximation Area Comparison (MABAC)

Description

R implementation of the MABAC method. The MABAC method computes the distance between each alternative and the Boundary Approximation Area (BAA), based on a weighted normalized decision matrix.

Usage

apply.MABAC(mat, weights, types)

Arguments

Value

A numeric vector with the MABAC preference values for each alternative. A higher value indicates a more preferred alternative.

Examples

# Example usage:
mat <- matrix(c(
  22600, 3800, 2,   5, 1.06, 3.00, 3.5,  2.8, 24.5, 6.5,
  19500, 4200, 3,   2, 0.95, 3.00, 3.4,  2.2, 24.0, 7.0,
  21700, 4000, 1,   3, 1.25, 3.20, 3.3,  2.5, 24.5, 7.3,
  20600, 3800, 2,   5, 1.05, 3.25, 3.2,  2.0, 22.5, 11.0,
  22500, 3800, 4,   3, 1.35, 3.20, 3.7,  2.1, 23.0, 6.3,
  23250, 4210, 3,   5, 1.45, 3.60, 3.5,  2.8, 23.5, 7.0,
  20300, 3850, 2,   5, 0.90, 3.25, 3.0,  2.6, 21.5, 6.0
), nrow = 7, byrow = TRUE)

weights <- c(0.146, 0.144, 0.119, 0.121, 0.115, 0.101, 0.088, 0.068, 0.050, 0.048)
types <- c(-1, 1, 1, 1, -1, -1, 1, 1, 1, 1)

apply.MABAC(mat, weights, types)

Apply MACBETH (Measuring Attractiveness by a Categorical Based Evaluation TecHnique)

Description

Apply MACBETH (Measuring Attractiveness by a Categorical Based Evaluation TecHnique)

Usage

apply.MACBETH(mat, beneficial.vector, weights)

Arguments

Value

A numeric vector V of length nrow(mat), the final attractiveness scores.

Examples

# Example matrix: 3 alternatives x 2 criteria
mat <- matrix(c(10, 5,
                12, 4,
                11, 6), nrow=3, byrow=TRUE)

# Suppose first column is beneficial, second is non-beneficial
benef.vec <- c(1)
wts <- c(0.6, 0.4)

# Get MACBETH scores
res <- apply.MACBETH(mat, benef.vec, wts)

Apply Multi-Attributive Real Ideal Comparative Analysis (MAIRCA)

Description

R implementation of the MAIRCA method. The MAIRCA method computes the gap between ideal (theoretical) and empirical ratings to rank alternatives.

Usage

apply.MAIRCA(mat, weights, types)

Arguments

Value

A numeric vector with the MAIRCA preference values for each alternative. Higher values indicate more preferred alternatives.

Examples

# Example usage
mat <- matrix(c(70, 245, 16.4, 19,
                52, 246, 7.3, 22,
                53, 295, 10.3, 25,
                63, 256, 12.0, 8,
                64, 233, 5.3, 17),
              nrow = 5, byrow = TRUE)
weights <- c(0.04744, 0.02464, 0.51357, 0.41435)
types <- c(1, 1, 1, 1)
apply.MAIRCA(mat, weights, types)

Apply the MARA (Magnitude of the Area for the Ranking of Alternatives) Method

Description

MARA ranks alternatives based on multiple criteria, each weighted. Columns in beneficial.vector are treated as "max" (beneficial), and columns not in beneficial.vector are treated as "min" (cost).

Usage

apply.MARA(mat, weights, beneficial.vector)

Arguments

Details

The following function is the R implementation of the python function mara from the pyDecision package Source: https://github.com/Valdecy/pyDecision/blob/master/pyDecision/algorithm/mara.py

Value

A numeric vector of MARA scores for each alternative.

Examples

# Example
mat <- matrix(c(10, 2,
                20, 4,
                15, 5),
              nrow = 3, byrow = TRUE)
weights <- c(0.7, 0.3)
beneficial.vector <- c(1)  # First column is beneficial (max); second is cost (min)
apply.MARA(mat, weights, beneficial.vector)

Apply Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS)

Description

Apply Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS)

Usage

apply.MARCOS(mat, weights, beneficial.vector)

Arguments

Value

a vector containing the aggregated appraisal scores.

Examples

mat <- matrix(c(660, 1000, 1600, 18, 1200,
                800, 1000, 1600, 24, 900,
                980, 1000, 2500, 24, 900,
                920, 1500, 1600, 24, 900,
                1380, 1500, 1500, 24, 1150,
                1230, 1000, 1600, 24, 1150,
                680, 1500, 1600, 18, 1100,
                960, 2000, 1600, 12, 1150), nrow = 8, byrow = TRUE)
weights <- c(0.1061, 0.3476, 0.3330, 0.1185, 0.0949)
beneficial.vector <- c(2, 3, 4, 5)  # Columns 2, 3, 4, and 5 are beneficial
apply.MARCOS(mat, weights, beneficial.vector)

Apply Multi-Attribute Utility Theory (MAUT) Method

Description

Apply Multi-Attribute Utility Theory (MAUT) Method

Usage

apply.MAUT(mat, weights, beneficial.vector, utility.functions, step.size = 1)

Arguments

Value

a matrix containing the calculated utility scores

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237, 420, 91), nrow = 3, byrow = TRUE)
weights <- c(0.3, 0.5, 0.2)
beneficial.vector <- c(1, 3)
utility.functions <- c("exp", "log", "quad")
step.size <- 1
result <- apply.MAUT(mat, weights, beneficial.vector, utility.functions, step.size)

Apply Multi-Objective Optimization on the basis of Ratio Analysis (MOORA)

Description

Apply Multi-Objective Optimization on the basis of Ratio Analysis (MOORA)

Usage

apply.MOORA(mat, weights, beneficial.vector)

Arguments

Value

a vector containing the calculated quantitative utility

Examples

mat <- matrix(c(60, 6.35, 6.8, 10, 2.5, 4.5, 3,
0.4, 0.15, 0.1, 0.2, 0.1, 0.08, 0.1,
2540, 1016, 1727.2, 1000, 560, 1016, 177,
500, 3000, 1500, 2000, 500, 350, 1000,
990, 1041, 1676, 965, 915, 508, 920), nrow=7)
colnames(mat)<-c("Load capacity", "Repeatability", "Maximum tip speed",
"Memory capacity", "Manipulator reach")
rownames(mat)<-paste0("A", 1:7)
weights <- c(0.1574, 0.1825, 0.2385, 0.2172, 0.2043)
beneficial.vector <- c(1, 3, 4, 5)
apply.MOORA(mat, weights, beneficial.vector)

Multi-objective Optimization on the Basis of Simple Ratio Analysis (MOOSRA)

Description

Multi-objective Optimization on the Basis of Simple Ratio Analysis (MOOSRA)

Usage

apply.MOOSRA(mat, weights, beneficial.vector)

Arguments

Value

A matrix containing the alternatives and their calculated scores, sorted by rank.

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)
weights <- c(0.1, 0.2, 0.3, 0.1, 0.1, 0.1, 0.1)
beneficial.vector<- c(1, 2, 3, 6, 7)
apply.MOOSRA(mat, weights, beneficial.vector)

Apply MULTIMOORA method

Description

Apply MULTIMOORA method

Usage

apply.MULTIMOORA(mat, beneficial.vector)

Arguments

Value

A list of matrices containing rankings for MOORA, MOORA RP, and MULTIMOORA methods.

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
                420, 91, 1365, 1120, 875, 1190, 200,
                74.2, 70, 189, 210, 112, 217, 112,
                2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53), nrow = 4, byrow = TRUE)
beneficial.vector <- c(1, 3) # Columns 1 and 3 are beneficial
apply.MULTIMOORA(mat, beneficial.vector)

Apply Operational Competitiveness Rating (OCRA) method

Description

The OCRA method independently evaluates alternatives with respect to beneficial (profit) and non-beneficial (cost) criteria, then combines these evaluations into an overall operational competitiveness rating.

Usage

apply.OCRA(mat, weights, beneficial.vector)

Arguments

Value

A numeric vector with the OCRA preference values for each alternative. Higher values indicate a more preferred alternative.

Examples

mat <- matrix(c(
  7.7, 256, 7.2, 7.3, 7.3,
  8.1, 250, 7.9, 7.8, 7.7,
  8.7, 352, 8.6, 7.9, 8.0,
  8.1, 262, 7.0, 8.1, 7.2,
  6.5, 271, 6.3, 6.4, 6.1,
  6.8, 228, 7.1, 7.2, 6.5
), nrow = 6, byrow = TRUE)

weights <- c(0.239, 0.225, 0.197, 0.186, 0.153)
beneficial.vector <- c(1, 3, 4, 5)

apply.OCRA(mat, weights, beneficial.vector)

Apply Ordinal Priority Approach (OPA)

Description

This function applies the Ordinal Priority Approach (OPA) to determine the optimal weights for experts, criteria, and alternatives based on expert opinions, ranks, and criterion importance.

Usage

apply.OPA(expert.opinion.lst, expert.rank, criterion.rank.lst)

Arguments

Value

A list of matrices where each matrix represents the optimal weights for the alternatives and criteria for a specific expert.

Examples

# Input Data
expert.x.alt <- matrix(c(1, 3, 2, 2, 1, 3), nrow = 3)
colnames(expert.x.alt) <- c("c", "q")
rownames(expert.x.alt) <- c("alt1", "alt2", "alt3")

expert.y.alt <- matrix(c(1, 2, 3, 3, 1, 2), nrow = 3)
colnames(expert.y.alt) <- c("c", "q")
rownames(expert.y.alt) <- c("alt1", "alt2", "alt3")

expert.opinion.lst <- list(expert.x.alt, expert.y.alt)
expert.rank <- c(1, 2)  # Ranks of experts

# Criterion ranks for each expert
criterion.x.rank <- c(1, 2)
criterion.y.rank <- c(2, 1)  # Adjusted criterion rank for expert y
criterion.rank.lst <- list(criterion.x.rank, criterion.y.rank)

# Apply OPA
weights <- apply.OPA(expert.opinion.lst, expert.rank, criterion.rank.lst)

Apply the ORESTE (Organisation Rangement Et SynThèsE de données relationnelles) Method

Description

Criteria with indexes in beneficial.vector are interpreted as beneficial (maximize), whereas others are cost-type (minimize). Rankings are performed for both the data matrix and the weights, then combined in the ORESTE manner.

Usage

apply.ORESTE(mat, weights, beneficial.vector, alpha = 0.4)

Arguments

Value

A numeric vector of ORESTE scores (summed ranks) for each alternative.

Examples

mat <- matrix(c(10, 2,
                20, 4,
                15, 5),
              nrow = 3, byrow = TRUE)
weights <- c(0.7, 0.3)
beneficial.vector <- c(1)   # 1st column "max", 2nd column "min"

apply.ORESTE(mat, weights, beneficial.vector, alpha = 0.4)

Apply Proximity Indexed Value (PIV) method

Description

Apply Proximity Indexed Value (PIV) method

Usage

apply.PIV(mat, weights, beneficial.vector)

Arguments

Value

A numeric vector containing the calculated PIV scores for each alternative.

Examples

mat <- matrix(c(80, 60, 90,
                75, 85, 95,
                70, 65, 85,
                60, 75, 80),
              nrow = 4, byrow = TRUE)
colnames(mat) <- c("Criterion 1", "Criterion 2", "Criterion 3")
weights <- c(0.4, 0.3, 0.3)
beneficial.vector <- c(1, 2, 3)
apply.PIV(mat, weights, beneficial.vector)

Function for applying PROMOTHEE I or II

Description

Function for applying PROMOTHEE I or II

Usage

apply.PROMETHEE(A, weights, type = "II")

Arguments

Value

the results of PROMOTHEE

Examples

A <- matrix(c(250, 200, 300, 275, 16, 16, 32, 32, 12, 8, 16, 8, 5, 3, 4, 2), nrow=4)
rownames(A)<-c("Mobile 1", "Mobile 2", "Mobile 3", "Mobile 4")
colnames(A)<-c("Price", "Memory", "Camera", "Looks")
weights <- c(0.35, 0.25, 0.25, 0.15)
apply.PROMETHEE(A, weights)

Apply Preference Selection Index (PSI) method

Description

Apply Preference Selection Index (PSI) method

Usage

apply.PSI(mat, beneficial.vector)

Arguments

Value

A numeric vector containing the calculated PSI scores for each alternative.

Examples

mat <- matrix(c(80, 60, 90,
                75, 85, 95,
                70, 65, 85,
                60, 75, 80),
              nrow = 4, byrow = TRUE)
colnames(mat) <- c("Criterion 1", "Criterion 2", "Criterion 3")
beneficial.vector <- c(1, 2, 3)
apply.PSI(mat, beneficial.vector)

Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI)

Description

Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI)

Usage

apply.RAFSI(
  mat,
  weights,
  beneficial.vector,
  ideal = NULL,
  anti_ideal = NULL,
  n_i = 1,
  n_k = 6
)

Arguments

Value

A numeric vector of final RAFSI scores, one per row of mat.

Examples

mat <- matrix(c(3, 2, 5,
4, 3, 2,
1, 6, 4),
nrow = 3, byrow = TRUE)
weights <- c(0.3, 0.5, 0.2)
beneficial.vector <- c(1, 2)
apply.RAFSI(mat, weights, beneficial.vector,   n_i = 1, n_k = 6)

Apply REGIME method (using a beneficial.vector)

Description

This function implements the REGIME method of pairwise comparisons to produce a character matrix (cp.matrix) that marks each pair of alternatives as either "P+" (row dominates column), "I" (indifferent), or "-" (for diagonals).

Usage

apply.REGIME(mat, beneficial.vector, weights, doPreOrder = FALSE)

Arguments

Details

It uses a beneficial.vector of column indices for "max" criteria. Columns not in beneficial.vector are treated as "min". The function can optionally run apply.po.ranking on the resulting matrix for partial-order analysis.

1. Weights Normalization: We first normalize the weights so their sum equals 1.

2. Pairwise Comparison Matrix (g_ind):

   • For each pair of alternatives and each criterion:

     • If the criterion is beneficial (maximization) and the value for one alternative is greater than or equal to the value for another alternative, the weight for that criterion is added to the pair's comparison score (g_ind). Otherwise, the weight is subtracted from the score.

     • If the criterion is non-beneficial (minimization) and the value for one alternative is less than the value for another alternative, the weight is added to the score. Otherwise, the weight is subtracted.

3. cp.matrix:

   • "P+" indicates that one alternative dominates another if the comparison score (g_ind) is greater than 0.

   • "I" indicates that the alternatives are indifferent if the comparison score is 0 or if the scores for both directions are equal.

   • "-" is assigned to diagonal entries, where the alternatives are compared with themselves.

4. If doPreOrder = TRUE, the function calls apply.po.ranking on cp.matrix to merge indifferent alternatives ("I") and construct a partial order.

Value

• If doPreOrder = FALSE, returns an n x n character matrix cp.matrix.

• If doPreOrder = TRUE, returns a list with two elements:

  • cp.matrix: the character matrix

  • po.result: the output from apply.po.ranking

Examples

# Example data: 3 alternatives x 2 criteria
mat <- matrix(c(10, 5,
                12, 4,
                11, 6), nrow = 3, byrow = TRUE)

# Suppose first column is beneficial, second is non-beneficial
benef.vec <- c(1)  # means col1 is "max", col2 is "min"
wts <- c(0.6, 0.4)

# Call apply.REGIME without partial-order
regime.out <- apply.REGIME(mat, benef.vec, wts, doPreOrder = FALSE)


# Or with partial-order
regime.out2 <- apply.REGIME(mat, benef.vec, wts, doPreOrder = TRUE)

Function to apply Reference Ideal Method (RIM) Note: function is rewritten from the MCDM package to match the formatting of the R RMCDA package SOURCE: https://github.com/cran/MCDM/blob/master/R/RIM.R

Description

The apply.RIM function implements the Reference Ideal Method (RIM) for multi-criteria decision making (MCDM) problems, allowing for degenerate intervals, i.e. cases where A == C or D == B.

Usage

apply.RIM(mat, weights, AB, CD)

Arguments

Value

A data frame containing:

• Alternatives: The index of each alternative.

• R: The R index (score) for each alternative.

• Ranking: The ranking of the alternatives based on the R score.

Reference: Cables, E.; Lamata, M.T.; Verdegay, J.L. (2016). RIM-reference ideal method in multicriteria decision making. Information Science, 337-338, 1-10.

Examples

# Example decision matrix
mat <- matrix(
  c(30,40,25,27,45,0,
    9,0,0,15,2,1,
    3,5,2,3,3,1,
    3,2,3,3,3,2,
    2,2,1,4,1,2),
  nrow = 5, ncol = 6, byrow = TRUE
)

#Example weights vector (must sum to 1)
weights <- c(0.2262,0.2143,0.1786,0.1429,0.119,0.119)

#Example AB matrix
AB <- matrix(
  c(23,60,0,15,0,10,
    1,3,1,3,1,5),
  nrow = 2, ncol = 6, byrow = TRUE
)

#Example CD matrix
CD <- matrix(
  c(30,35,10,15,0,0,
    3,3,3,3,4,5),
  nrow = 2, ncol = 6, byrow = TRUE
)


apply.RIM(mat, weights, AB, CD)

Apply Range of Value (ROV) method

Description

Apply Range of Value (ROV) method

Usage

apply.ROV(mat, weights, beneficial.vector)

Arguments

Value

A numeric vector containing the calculated ROV scores for each alternative.

Examples

mat <- matrix(c(80, 60, 90,
                75, 85, 95,
                70, 65, 85,
                60, 75, 80),
              nrow = 4, byrow = TRUE)
colnames(mat) <- c("Criterion 1", "Criterion 2", "Criterion 3")
weights <- c(0.4, 0.3, 0.3)
beneficial.vector <- c(1, 2, 3)
apply.ROV(mat, weights, beneficial.vector)

Apply Simple Additive Weighting Method (SAW)

Description

Apply Simple Additive Weighting Method (SAW)

Usage

apply.SAW(mat, weights, beneficial.vector)

Arguments

Value

a vector containing the score and corresponding ranking for the SAW function

Examples

mat <- matrix(c(60, 6.35, 6.8, 10, 2.5, 4.5, 3,
0.4, 0.15, 0.1, 0.2, 0.1, 0.08, 0.1,
2540, 1016, 1727.2, 1000, 560, 1016, 177,
500, 3000, 1500, 2000, 500, 350, 1000,
990, 1041, 1676, 965, 915, 508, 920), nrow=7)
colnames(mat)<-c("Load capacity", "Repeatability", "Maximum tip speed",
"Memory capacity", "Manipulator reach")
rownames(mat)<-paste0("A", 1:7)
weights <- c(0.1574, 0.1825, 0.2385, 0.2172, 0.2043)
beneficial.vector <- c(1, 3, 4, 5)
apply.SAW(mat, weights, beneficial.vector)

Function for applying the Stratified Best-Worst Method (SBWM)

Description

Function for applying the Stratified Best-Worst Method (SBWM)

Usage

apply.SBWM(
  comparison.mat,
  others.to.worst,
  others.to.best,
  state.worst.lst,
  state.best.lst,
  likelihood.vector
)

Arguments

Value

the result of SBWM

Examples

data <- read.csv(system.file("extdata",
 "stratified_BWM_case_study_I_example.csv",
  package = "RMCDA"), header = FALSE)
mat.lst <- read.csv.SBWM.matrices(data)
comparison.mat <- mat.lst[[1]]
others.to.worst <- mat.lst[[2]]
others.to.best <- mat.lst[[3]]
state.worst.lst <- mat.lst[[4]]
state.best.lst <- mat.lst[[5]]
likelihood.vector <- mat.lst[[6]]
apply.SBWM(comparison.mat, others.to.worst,
 others.to.best, state.worst.lst,
  state.best.lst, likelihood.vector)

Apply Simultaneous Evaluation of Criteria and Alternatives (SECA) method

Description

Apply Simultaneous Evaluation of Criteria and Alternatives (SECA) method

Usage

apply.SECA(mat, beneficial.vector, beta = 3)

Arguments

Value

A numeric vector containing the calculated weights for each criterion.

Examples

mat <- matrix(c(80, 60, 90,
                75, 85, 95,
                70, 65, 85,
                60, 75, 80),
              nrow = 4, byrow = TRUE)
colnames(mat) <- c("Criterion 1", "Criterion 2", "Criterion 3")
beneficial.vector <- c(1, 2, 3)
apply.SECA(mat, beneficial.vector)

Apply the SMART Method

Description

This function implements the SMART (Simple Multi-Attribute Rating Technique) method in R.

Usage

apply.SMART(dataset, grades, lower, upper, beneficial.vector)

Arguments

Value

A matrix (or data frame) named result with two columns: The row index (alternative) and the final SMART score for that alternative.

The rows of result are sorted by score in descending order.

Examples

# Example usage
data_mat <- matrix(c(10, 20, 15,  7,
                     30,  5,  8, 25),
                   nrow = 2, byrow = TRUE)
# Suppose we have 4 criteria (2 rows, 4 columns)
# We'll treat columns 1, 2, 3 as beneficial, and column 4 as non-beneficial
benef_vec <- c(1, 2, 3)

# Grades for each of 4 criteria
grades <- c(2, 2, 1, 3)
lower  <- c(0, 0, 0,  0)
upper  <- c(40, 40, 40, 40)

# Run SMART
result <- apply.SMART(dataset = data_mat,
                    grades = grades,
                    lower  = lower,
                    upper  = upper,
                    beneficial.vector = benef_vec)

result

Apply Stratified Multi-Criteria Decision Making (SMCDM) method

Description

Apply Stratified Multi-Criteria Decision Making (SMCDM) method

Usage

apply.SMCDM(
  comparison.mat,
  state.criteria.probs,
  likelihood.vector,
  independent.events = TRUE
)

Arguments

Value

the SMCDM results

Examples

data <- read.csv(system.file("extdata", "SMCDM_input.csv", package = "RMCDA"), header=FALSE)
mat.lst <- read.csv.SMCDM.matrices(data)
comparison.mat <- mat.lst[[1]]
state.criteria.probs <- mat.lst[[2]]
likelihood.vector <- mat.lst[[3]]
apply.SMCDM(comparison.mat, state.criteria.probs, likelihood.vector)

Apply the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method

Description

Apply the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method

Usage

apply.SPOTIS(matrix, weights, types, bounds)

Arguments

Value

A numeric vector of preference scores for alternatives. Lower scores indicate better alternatives.

Examples

# Decision matrix
matrix <- matrix(c(10.5, -3.1, 1.7,
                   -4.7, 0, 3.4,
                   8.1, 0.3, 1.3,
                   3.2, 7.3, -5.3), nrow = 4, byrow = TRUE)

# Criteria bounds
bounds <- matrix(c(-5, 12,
                   -6, 10,
                   -8, 5), nrow = 3, byrow = TRUE)

# Criteria weights
weights <- c(0.2, 0.3, 0.5)

# Criteria types
types <- c(1, -1, 1)

# Apply SPOTIS
preferences <- apply.SPOTIS(matrix, weights, types, bounds)

Apply SRMP (Simple Ranking Method using Reference Profiles) on data

Description

Apply SRMP (Simple Ranking Method using Reference Profiles) on data

Usage

apply.SRMP(evaluations.mat, reference.profiles, weights)

Arguments

Value

alternatives ranked using SRMP

Examples

evaluations.mat <- matrix(c(41, 46, 43, -2, -4, -5.5, 4, 2, 3), nrow=3)
colnames(evaluations.mat) <- c("S", "L", "J")
rownames(evaluations.mat) <- c("x", "y", "z")
reference.profiles <- matrix(c(42, 45, -5, -3, 2, 4), nrow=2)
colnames(reference.profiles) <- c("S", "L", "J")
rownames(reference.profiles) <- c("p1", "p2")
weights <- c(1/3, 1/3, 1/3)
apply.SRMP(evaluations.mat, reference.profiles, weights)

Apply TODIM (TOmada de Decisao Interativa e Multicriterio)

Description

Implements the core TODIM logic in R

Usage

apply.TODIM(mat, weights, beneficial.vector, teta = 1)

Arguments

Details

In the TODIM formula, theta acts as an “attenuation factor” or penalty for negative dominance differences. This parameter allows you to adjust how severely negative differences weigh in the final scoring. A common default is 1, but you could experiment with other values if you want to amplify or reduce the penalty effect.

If you set teta = 1, it uses the standard TODIM approach. If you do not want to vary this parameter, you can leave it at its default value of 1.

Value

A numeric vector of rescaled scores, one per alternative (row).

Examples

# Small synthetic example
mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)

colnames(mat)<-c("Toughness Index",	"Yield Strength",	"Young's Modulus",
"Density",	"Thermal Expansion",	"Thermal Conductivity","Specific Heat")
rownames(mat)<-c("AI 2024-T6", "AI 5052-O","SS 301 FH",
"SS 310-3AH","Ti-6AI-4V","Inconel 718","70Cu-30Zn")
weights <- c(0.28, 0.14, 0.05, 0.24, 0.19, 0.05, 0.05)
beneficial.vector<-c(1,2,3)

apply.TODIM(mat, weights, beneficial.vector, teta=1)

Apply TOPSIS on matrix A with weight of criteria stored in vector w

Description

Apply TOPSIS on matrix A with weight of criteria stored in vector w

Usage

apply.TOPSIS(A, w)

Arguments

Value

performance scores obtained through TOPSIS

Examples

A <- matrix(c(250, 200, 300, 275,
 225, 16, 16, 32,
  32, 16, 12, 8,
   16, 8, 16, 5,
    3, 4, 4, 2), nrow=5, ncol=4)
colnames(A)<-c("Price", "Storage space",
 "Camera", "Looks")
rownames(A)<-paste0("Mobile ", seq(1, 5, 1))
A[,"Price"] <- -A[,"Price"]
apply.TOPSIS(A, c(1/4, 1/4, 1/4, 1/4))

Function for applying VIKOR to data

Description

Function for applying VIKOR to data

Usage

apply.VIKOR(A, weights, nu = 0.5)

Arguments

Value

a list containing the names of Qi followed by values of Qi, Si, Ri, condition 1, and condition 2.

Examples

A <- matrix(c(250, 200, 300, 275,
 225, 16, 16, 32,
  32, 16, 12, 8,
   16, 8, 16, 5,
    3, 4, 4, 2), nrow=5, ncol=4)
colnames(A)<-c("Price", "Memory", "Camera", "Looks")
rownames(A)<-paste0("Mobile ", seq(1, 5, 1))
A[,"Price"] <- -A[,"Price"]
apply.VIKOR(A, c(0.35, 0.3, 0.2, 0.15))

Weighted Aggregated Sum Product Assessment (WASPAS)

Description

Weighted Aggregated Sum Product Assessment (WASPAS)

Usage

apply.WASPAS(mat, weights, beneficial.vector, lambda)

Arguments

Value

the Q index from WASPAS

Examples

mat <- matrix(c(0.04, 0.11, 0.05, 0.02, 0.08, 0.05, 0.03, 0.1, 0.03,
1.137, 0.854, 1.07, 0.524, 0.596, 0.722, 0.521, 0.418, 0.62,
960, 1920, 3200, 1280, 2400, 1920, 1600, 1440, 2560), nrow=9)
colnames(mat)<-c("Dimensional Deviation (DD)", "Surface Roughness (SR)",
"Material Removal Rate (MRR)")

rownames(mat)<-paste0("A", 1:9)
beneficial.vector <- c(3)
weights <- c(0.1047, 0.2583, 0.6369)
apply.WASPAS(mat, weights, beneficial.vector, 0.5)

Apply WINGS (Weighted Influence Non-linear Gauge System)

Description

This function implements the core calculations of the WINGS method, ignoring any plotting or quadrant labeling. It returns three vectors:

• r_plus_c: (R + C) for each row/column

• r_minus_c: (R - C) for each row/column

• weights: normalized weights derived from (R + C).

Usage

apply.WINGS(mat)

Arguments

Value

A list with three elements: r_plus_c, r_minus_c, and weights.

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)

colnames(mat)<-c("Toughness Index",	"Yield Strength",	"Young's Modulus",
"Density",	"Thermal Expansion",	"Thermal Conductivity","Specific Heat")
rownames(mat)<-c("AI 2024-T6", "AI 5052-O","SS 301 FH",
"SS 310-3AH","Ti-6AI-4V","Inconel 718","70Cu-30Zn")

result <- apply.WINGS(mat)
result$r_plus_c    # (R + C)
result$r_minus_c   # (R - C)
result$weights     # Weights

Apply WISP (Integrated Simple Weighted Sum Product) method,

Description

Performs the WISP method calculations, returning a utility score for each alternative. Columns whose indices appear in beneficial.vector are treated as beneficial (max); all other columns are treated as non-beneficial (min).

Usage

apply.WISP(mat, beneficial.vector, weights, simplified = FALSE)

Arguments

Value

A numeric vector of length nrow(mat) with the final WISP utility scores.

Examples

mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
420, 91, 1365, 1120, 875, 1190, 200,
74.2, 70, 189, 210, 112, 217, 112,
2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)

colnames(mat)<-c("Toughness Index",	"Yield Strength",	"Young's Modulus",
"Density",	"Thermal Expansion",	"Thermal Conductivity","Specific Heat")
rownames(mat)<-c("AI 2024-T6", "AI 5052-O","SS 301 FH",
"SS 310-3AH","Ti-6AI-4V","Inconel 718","70Cu-30Zn")

# Suppose the first two columns are beneficial, and the 3rd is non-beneficial
beneficial.vector <- c(1,2, 4)
weights <- c(0.28, 0.14, 0.05, 0.24, 0.19, 0.05, 0.05)

# Get the WISP scores
apply.WISP(mat, beneficial.vector, weights, simplified=FALSE)

Apply Weighted Sum Model (WSM) or Weighted Product Model (WPM) on data

Description

Apply Weighted Sum Model (WSM) or Weighted Product Model (WPM) on data

Usage

apply.WSM_WPM(mat, beneficial.vector, weights, method = "WSM")

Arguments

Value

a vector containing the calculated preference score, run rank(-apply.WSM(mat, beneficial.vector, weights)) to get the ranks.

Examples

mat <- matrix(c(250, 200, 300, 275,
 225, 16, 16, 32,
  32, 16, 12, 8,
   16, 8, 16, 5,
    3, 4, 4, 2), nrow=5, ncol=4)
colnames(mat)<-c("Price", "Storage space",
 "Camera", "Looks")
rownames(mat)<-paste0("Mobile ", seq(1, 5, 1))
beneficial.vector <- c(2, 3, 4)
weights <- c(0.25, 0.25, 0.25, 0.25)
apply.WSM_WPM(mat, beneficial.vector, weights, "WSM")

Find entropy of each criteria

Description

Find entropy of each criteria

Usage

apply.entropy(A)

Arguments

Value

the entropy value corresponding to each criteria

Examples

A <- matrix(c(250, 200, 300, 275,
 225, 16, 16, 32,
  32, 16, 12, 8,
   16, 8, 16, 5,
    3, 4, 4, 2), nrow=5, ncol=4)
colnames(A)<-c("Price", "Storage space",
 "Camera", "Looks")
rownames(A)<-paste0("Mobile ", seq(1, 5, 1))
A[,"Price"] <- -A[,"Price"]
apply.entropy(A)

Apply Pre-Order Ranking (partial-order analysis)

Description

This function is an R translation of the Python po.ranking() function It merges alternatives that are 'I' (indifferent), constructs a 0/1 partial-order matrix from 'P+' entries, sorts the alternatives by row sums, and then removes transitive edges.

Usage

apply.po.ranking(partial.order.str)

Arguments

Details

The function is an R implementation of the pre-order rank and regime function in the pyDecision package Source: https://github.com/Valdecy/pyDecision/blob/master/pyDecision/algorithm/regime.py

Value

A list with elements:

• partial.order.str: An updated partial.order.str after merges. Dimensions may be smaller than the input.

• partial.order.mat: An n' x n' numeric matrix of 0/1, where 1 indicates 'P+'.

• alts: A character vector of alternative labels, possibly merged (e.g., "a2; a1").

• alts_rank: The final ordering of alternatives from most dominating to least dominating.

• rank: A 0/1 matrix after removing transitive edges.

Examples

# Create a small 3x3 partial-order matrix
po_str <- matrix(c("P+", "P+", "R",
                   "R",   "-",   "I",
                   "R",   "I",   "-"), nrow=3, byrow=TRUE)

# Apply the pre-order ranking
res <- apply.po.ranking(po_str)

Finding the weights for each criteria given a pairwise comparison matrix A in the AHP method

Description

Finding the weights for each criteria given a pairwise comparison matrix A in the AHP method

Usage

find.weight(A)

Arguments

Value

a list containing the value of CI/RI and a vector containing the weights of each criteria

Generate bounds for criteria from a decision matrix

Description

Generate bounds for criteria from a decision matrix

Usage

generate.SPOTIS.bounds(matrix)

Arguments

Value

A numeric matrix with two columns: minimum and maximum bounds for each criterion.

Examples

# Decision matrix
matrix <- matrix(c(96, 145, 200,
                   100, 145, 200,
                   120, 170, 80,
                   140, 180, 140,
                   100, 110, 30), nrow = 5, byrow = TRUE)

# Generate bounds
bounds <- generate.SPOTIS.bounds(matrix)

Read csv file containing pairwise comparison matrices for applying AHP or ANP

Description

Read csv file containing pairwise comparison matrices for applying AHP or ANP

Usage

read.csv.AHP.matrices(data)

Arguments

Value

a list containing a matrix A related to pairwise comparison of criteria and a list containing multiple matrices related to pairwise comparisons of different competitor products

Examples

data <- read.csv(system.file("extdata", "AHP_input_file.csv",
 package = "RMCDA"), header=FALSE)
mat.lst <- read.csv.AHP.matrices(data)

Read csv file containing input to the stratified BWM method

Description

Read csv file containing input to the stratified BWM method

Usage

read.csv.SBWM.matrices(data)

Arguments

Value

the inputs to the SBWM method

Examples

data <- read.csv(system.file("extdata",
"stratified_BWM_case_study_I_example.csv",
 package = "RMCDA"), header = FALSE)
mat.lst <- read.csv.SBWM.matrices(data)

Read csv file containing pairwise comparison matrices for applying SMCDM

Description

Read csv file containing pairwise comparison matrices for applying SMCDM

Usage

read.csv.SMCDM.matrices(data)

Arguments

Value

a list containing a matrix A related to pairwise comparison of criteria and a list containing multiple matrices related to pairwise comparisons of different competitor products

Examples

data <- read.csv(system.file("extdata", "SMCDM_input.csv", package = "RMCDA"), header = FALSE)
mat.lst <- read.csv.SMCDM.matrices(data)

Plot spider plot

Description

Plot spider plot

Usage

spider.plot(data, colors = palette("default"))

Arguments

Value

the spider plot