fs_weight: Fuzzy supplementary poverty estimation (Steps 4 and 5)

In FuzzyPovertyR: Estimation of Fuzzy Poverty Measures

Fuzzy supplementary poverty estimation (Steps 4 and 5)

Description

Step 4 and Step 5. Calculates the weights of dimensions discovered after Dimension analysis.

Usage

fs_weight(dimensions, step2, rho = NULL)

Arguments

dimensions	A numeric vector (of length ncol(data)) of assignments of items in data to dimensions
step2	The data frame resulting from step2
rho	Optional critical value to be used for calculation of weights in the Kendall correlation matrix. If NULL rho is set equal to the point of largest gap between the ordered set of correlation values encountered (see Betti and Verma, 2008)

Details

This function calculates the two set of weights w_a and w_b (see References)

Value

An object of class FuzzySupplementary with calculated weights and deprivation scores in each dimension identified.

References

Betti, G., & Verma, V. (2008). Fuzzy measures of the incidence of relative poverty and deprivation: a multi-dimensional perspective. Statistical Methods and Applications, 17, 225-250.

Betti, G., Gagliardi, F., & Verma, V. (2018). Simplified Jackknife variance estimates for fuzzy measures of multidimensional poverty. International Statistical Review, 86(1), 68-86.

Examples

#This example is based on the dataset eusilc included in the package
#The step 2-3 are the following (step 1 is the eusilc dataset)
#For more on each step see the ad hoc function included in the package

#Step 2

step2 = fs_transform(eusilc[,4:23], weight = eusilc$DB090, ID = eusilc$ID)

#Step 3 is the definition of the dimension.
#For more about the step see Betti et al. (2018)

dimensions = c(1,1,1,1,2,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5)

#Step 4-5 finding weights

steps4_5 = fs_weight(dimensions, step2 = step2, rho = NULL)

FuzzyPovertyR documentation built on Oct. 3, 2024, 5:06 p.m.