ci_owa: Ordered Weighted Average (OWA)

In Compind: Composite Indicators Functions

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.

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

Compind documentation built on June 8, 2025, 11:08 a.m.