fs_transform {FuzzyPovertyR}

Fuzzy supplementary poverty estimation (Step 2)

Description

Step 2. This function maps a set of answers to binary or categorical items to the (0,1) interval.

Usage

fs_transform(data, weight = NULL, ID = NULL, depr.score = "s", ...)

Arguments

data

A matrix or a data frame of identified items (see Step 1 of Betti et. al, 2018)

weight

A numeric vector of sampling weights. if NULL weights will set equal to n (n = sample size)

ID

A numeric or character vector of IDs. if NULL (the default) it is set as the row sequence

depr.score

The deprivation score to be used (see d or s in Betti et al (2018))

...

other parameters

Details

The function calculates deprivation score. To obtain consistent measures of supplementary poverty it is important that items are in the right order. Lower levels of the items have to correspond to more deprivation while higher levels of the items to a less deprivation.

Value

An object of class FuzzySupplementary containing a matrix of the same dimension of 'data' with items mapped into the (0,1) interval

References

Betti, G., Gagliardi, F., Lemmi, A., & Verma, V. (2015). Comparative measures of multidimensional deprivation in the European Union. Empirical Economics, 49(3), 1071-1100.

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
#step 1 is the choice of the eusilc dataset 

#Step 2 

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