ci_owa {Compind} R Documentation

## Ordered Weighted Average (OWA)

### Description

The Ordered Weighted Averaging (OWA) operator is a multi-criteria decision aggregation method that is structurally non-compensatory (Yager, 1988).

### Usage

ci_owa(x, id, indic_col, atleastjp)

### Arguments

 x A data.frame containing score of the simple indicators. id Units' unique identifier. indic_col Simple indicators column number. atleastjp Fuzzy linguistic quantifier "At least j".

### Value

An object of class "CI". This is a list containing the following elements:

 CI_OWA_n Composite indicator estimated values for OWA-. CI_OWA_p Composite indicator estimated values for OWA+. wp OWA weights' vector "More than j". wn OWA weights' vector "At least j". ci_method Method used; for this function ci_method="owa".

### Author(s)

Fusco E., Liborio M.P.

### References

Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on systems, Man, and Cybernetics, 18(1), 183-190.

ci_ogwa

### Examples

data(data_HPI)

data_HPI = data_HPI[complete.cases(data_HPI),]
data_HPI_2019 = data_HPI[data_HPI$year==2019,] Indic_name = c("Life_Expectancy","Ladder_of_life","Ecological_Footprint") Indic_norm = data.frame("ISO"=data_HPI_2019$ISO,
normalise_ci(data_HPI_2019[, Indic_name],
c(1:3),
c("POS","POS","NEG"),
method=2)$ci_norm) Indic_norm = Indic_norm[Indic_norm$Life_Expectancy>0 &
Indic_norm$Ladder_of_life>0 & Indic_norm$Ecological_Footprint >0 ,]

atleast = 2
CI_owa_n = ci_owa(Indic_norm, id="ISO",
indic_col=c(2:4),
atleastjp=atleast)$CI_OWA_n CI_owa_p = ci_owa(Indic_norm, id="ISO", indic_col=c(2:4), atleastjp=atleast)$CI_OWA_p


[Package Compind version 3.1 Index]