summary.decomposition {ineqJD}

Summarizing inequality decomposition

Description

summary method for class "decomposition".

Usage

## S3 method for class 'decomposition'
summary(object, ...)
## S3 method for class 'summary.decomposition'
print(x, ...)

Arguments

object

An object of class "decomposition", usually, as result of a call to gini, bonferroni and zenga.

x

rtrtrt

...

further arguments passed to or from other methods.

Details

summary.decomposition method use

Value

index

String denoting computed index.

joint

Array of joint decompositions by sources and subpopulations.

pairs

Matrix of decompositions by subpopulations.

within

Vector of within part to the overall inequality. It denotes the part of the overall inequality derived from the inequality inside each subpopulation.

between

Vector of between part to the overall inequality. It denotes the part of the overall inequality derived from the comparison between subpopulations.

groups

Vector of subpopulations contribution to the overall inequality.

groups_sources

Matrix of subpopulations contributions for each source to the overall inequality.

sources

Vector of sources contribution to the overall inequality.

synthetic

Scalar denoting the value of the synthetic index.

Author(s)

Alberto Arcagni, Igor Valli.

References

Zenga M. M.(2007). Inequality Curve and Inequality Index based on the Ratios between llower and upper Means . Statistica and Applicazioni, V (1), 3-27.

Zenga M. (2015) Joint decomposition by subpopulations and sources of the point and synthetic Zenga(2007) Index I(Y). Statistica and Applicazioni, XIII (2), pp.163-195.

Zenga M., Valli I. (2017). Joint decomposition by Subpopulations and Sources of the Point and Synthetic Bonferroni Inequality Measures. Statistics and Applications, XV (2), pp. 83-120.

Zenga M., Valli I. (2018). Joint decomposition by Subpopulations and Sources of the Point and Synthetic Gini Indexes. Statistics and Applications, XVI (1).

See Also

gini, bonferroni, zenga, dataProcessing.

Examples

G <- c(1, 2, 3, 1, 2, 3, 1, 1, 2, 3, 3, 3) # vector denoting group membership
X1 <- c(0, 0, 0, 500, 700, 300, 750, 1000, 500, 500, 500, 1000) # vector of the first source
X2 <- c(0, 0, 0, 500, 300, 700, 750, 500, 700, 700, 1000,600) # vector of the second source
data <- data.frame(G, X1, X2) # no sample weights are considered
x <- dataProcessing( # data preparation
  units = data[, c('X1', 'X2')],
  groups = data[, 'G'],
)
decomposition <- zenga(x)

summary(decomposition)