ci_owa function

Ordered Weighted Average (OWA)

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

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".

Returns

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".

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.

Author(s)

Fusco E., Liborio M.P.

See Also

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