ineq.weighted {wINEQ}

Weighted inequality measures

Description

Calculates weighted mean and sum of X (or median of X), and a set of relevant inequality measures.

Usage

ineq.weighted(
  X,
  W = rep(1, length(X)),
  AF.norm = TRUE,
  Atkinson.e = 1,
  Jenkins.alfa = 0.8,
  Entropy.e = 0.5,
  Kolm.p = 1,
  Kolm.scale = "Standardization",
  Leti.norm = T,
  AN_Y.a = 1,
  AN_Y.b = 1,
  Apouey.a = 2/(1 - length(W[!is.na(W) & !is.na(X)])),
  Apouey.b = length(W[!is.na(W) & !is.na(X)])/(length(W[!is.na(W) & !is.na(X)]) - 1),
  BL.withsqrt = FALSE
)

Arguments

X

is a data vector

W

is a vector of weights

AF.norm

(logical). If TRUE (default) then index is divided by its maximum possible value

Atkinson.e

is a parameter for Atkinson coefficient

Jenkins.alfa

is a parameter for Jenkins coefficient

Entropy.e

is a generalized entropy index parameter

Kolm.p

is a parameter for Kolm index

Kolm.scale

method of data standardization before computing

Leti.norm

(logical). If TRUE (default) then Leti index is divided by a maximum possible value

AN_Y.a

is a positive parameter for Abul Naga and Yalcin inequality measure

AN_Y.b

is a parameter for Abul Naga and Yalcin inequality measure

Apouey.a

is a parameter for Apouey inequality measure

Apouey.b

is a parameter for Apouey inequality measure

BL.withsqrt

if TRUE function returns index given by BL2, elsewhere by BL (default). See more in details of BL function.

Details

Function checks if X is a numeric or an ordered factor. Then it calculates all appropriate inequality measures.

Value

The data frame with weighted mean and sum of X, and all inequality measures relevant for a numeric data. In a case of an ordered factor, the data frame with median of X, and all relevant inequality measures.

Examples

# Compare weighted and unweighted result.
X=1:10
W=1:10
ineq.weighted(X)
ineq.weighted(X,W)


data(Tourism)
# Results for Total expenditure with sample weights:
X=Tourism$`Total expenditure`
W=Tourism$`Sample weight`
ineq.weighted(X)
ineq.weighted(X,W)