Compind (version 3.2)

ci_owa: 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)

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".

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".

Author

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