Package 'ineqJD'

Title: Inequality Joint Decomposition
Description: Computes and decomposes Gini, Bonferroni and Zenga 2007 point and synthetic concentration indexes. Decompositions are intended: by sources, by subpopulations and by sources and subpopulations jointly. References, Zenga M. M.(2007) <doi:10.1400/209575> Zenga M. (2015) <doi:10.1400/246627> Zenga M., Valli I. (2017) <doi:10.26350/999999_000005> Zenga M., Valli I. (2018) <doi:10.26350/999999_000011>.
Authors: Alberto Arcagni [aut, cre], Igor Valli [aut], Michele Zenga [ctb]
Maintainer: Alberto Arcagni <[email protected]>
License: GPL (>= 2)
Version: 1.0
Built: 2025-02-14 03:40:04 UTC
Source: https://github.com/cran/ineqJD

Help Index

Point and synthetic Bonferroni indexes

Description

Computes the decomposition of the Bonferroni point inequality indexes of a statistical variable YY described in the object x.

Usage

bonferroni(x)

Arguments

x

An object of class "dataProcessed". x is usually the result of dataProcessing function. More details are given in the "Details" section and dataProcessing help page.

Details

bonferroni computes the decomposition of the Bonferroni point inequality indexes from the object x of class "dataProcessed". x is usually the result of dataProcessing function.

Value

index

String denoting computed index.
decomposition

Array containing the decompositions. The dimensions of decomposition are c(g, g, r, s) where g is the number of groups, r the number of different values of YY and s the number of sources.
x

Object of class dataProcessed passed as input.

Author(s)

Alberto Arcagni, Igor Valli.

References

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.

See Also

gini and zenga for other inequality indexes and dataProcessing for the class "dataProcessed".

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 <- bonferroni(x)
decomposition

Data Processing

Description

Convert raw data to frequency distribution framework and returns cumulative sums.

Usage

dataProcessing(
  units,
  groups = rep("G1", nrow(as.matrix(units))),
  weights = rep(1, nrow(as.matrix(units)))
)

Arguments

units

Numeric vector of length n or matrix of dimension c(n, s) containing s sources referred to n statistical units
groups

Vector of length n of group membership. If empty only one group is considered, otherwise the number of groups g is defined by the number of distinct values or levels in this vector.
weights

Vector of lweights of length n. If empty uniform weights are considered.

Details

dataProcessing convert raw data in the frequency distribution framework with r distinct values of YY. In this way repeated values are removed as well as ordering issues. Moreover cumulative frequencies and cumulative sources values are evaluated in order to prepare data for inequality decompositions.

Value

yh

Vector of length r of distinct values of YY.
Phl

Matrix of absolute cumulative frequencies of dimension c(r, g).
Qhlk

Array of cumulative sum of sources of dimension c(r, g, s).

Author(s)

Alberto Arcagni, Igor Valli

References

Zenga M.M. (2007). Inequality curve and inequality index based on the ratios between lower and upper means. Statistica and Applicazioni, V(1), 3-27

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(
  units = data[, c('X1', 'X2')],
  groups = data[, 'G'],
)
x

Point and synthetic Gini indexes

Description

Computes point and synthetic Gini indexes on a variable YY.

Usage

gini(x)

Arguments

x

List containing: 'yh', the vector of unique values of the variable YY whose Bonferroni index is computed; 'Phl', the matrix of absoute cumulative frequencies; 'Qhlk', the matrix of cumlative sums of YY or its sources. x is usually the result of dataProcessing function. More details are given in the "Details" section and dataProcessing help page.

Details

gini compute point and synthetic Gini indexes on a variable y, e.g. income, on a statistical population that could be partitioned in g subpopultions and could be considered as sum of c sources, e.g. income sources.

Value

index

String denoting computed index.
decomposition

array containing the decompositions.
x

object usually of class dataProcessed passed as input.

Author(s)

Alberto Arcagni, Igor Valli.

References

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

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 <- gini(x)
decomposition

Inequality curves evaluation

Description

Generates step-functions (see stepfun) representing inequality curves or sources/subpopulations point contributions in the decomposition x generated by functions gini, bonferroni or zenga.

Usage

inequalityCurves(x, ...)
## S3 method for class 'decomposition'
inequalityCurves(x, l = 1:dim(x$decomposition)[2], k = 1:dim(x$decomposition)[4], ...)

Arguments

x

An object of class decomposition output of functions gini, bonferroni, zenga.

l

Vector of selected subpopulations. If only one subpopulation is selected the resulting step function provides its point contributions. More than one subpopulation can be selected and contributions are cumulated. If empty all subpopulations are considered and contributions are marginalized by subpopulations.

k

Vector of selected sources. If only one source is selected the resulting step function provides its point contributions. More than one source can be selected and contributions are cumulated. If empty all sources are considered and contributions are marginalized by sources.

...

Potentially further arguments (required by the generic).

Details

By default generates step functions representing inequality curves of the whole population. If arguments l and/or k are defined, resuts are step functions representing point contributions of the selected subpopulations and/or sources.

The class of the result is "inequality_curves" that is associated to the method plot.inequality_curves for graphical representations. Such derived class hinerits the features of the "stepfun" class.

Value

An object of class "inequality_curves" hineriting the features of class "stepfun" with the following additional attributes:

index

String denoting computed index.
min, max

The range of values assumed by the function.
groups

Vector of names of the subpopulations partitioning the whole population .
sources

Vector of names of the all the sources that sum to the total variable YY.
selected_groups

Vector of names of the selected subpopulations to evaluate the point contributions.
selected_sources

Vector of names of the selected sources to evaluate the point contributions.

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

See gini, bonferroni, zenga to obtain objects of class decomposition and see plot.inequality_curves for the graphical representation.

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)

ic <- inequalityCurves(decomposition)
ic
contrib1 <- inequalityCurves(decomposition, l = 1)
contrib1
contrib12 <- inequalityCurves(decomposition, l = 1:2)
contrib12

Plot inequality curves

Description

Method of the generic plot for objects generated by the function inequalityCurves.

Usage

## S3 method for class 'inequality_curves'
plot(
  x,
  pch = 16,
  from = 0,
  to = 1,
  xlim = NULL,
  ylim = NULL,
  xaxs = "i",
  yaxs = "i",
  xlab = "p",
  ylab = NULL,
  main = attributes(x)$index,
  sub = paste0(
    "grp: ",
    paste(attributes(x)$groups, collapse = ", "),
    "; src: ",
    paste(attributes(x)$sources, collapse = ", ")
  ),
  ...
)

Arguments

x

Object of class "decomposition".

pch

A vector of plotting characters or symbols: see points.

from, to

The range over which the function will be plotted.

xlim

The x limits (x1, x2) of the plot. The default value, NULL, indicates that the range of the function should be used.

ylim

the y limits of the plot.

xaxs

The style of axis interval calculation to be used for the x-axis. See par for details.

yaxs

The style of axis interval calculation to be used for the y-axis. See xaxs above.

xlab

a title for the x axis: see title.

ylab

a title for the y axis: see title.

main

an overall title for the plot: see title.

sub

a sub title for the plot: see title.

...

Arguments to be passed to methods, such as graphical parameters (see par). Many methods will accept the following arguments:

Details

This method is a convenience wrapper for plotting inequality curves. Default values of the plot are modified in order to plot inequality curves in the unitary square. Moreover, the default value of the argument sub shows in the plot if the curve represents the whole inequality or a contribution.

Value

The same output of the function plot.stepfun, a list with two components:

t

abscissa (x) values, including the two outermost ones.
y

y values ‘in between’ the t[].

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

plot, graphical parameters, par, plot.stepfun, inequalityCurves

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)
ic <- inequalityCurves(decomposition)
contrib1 <- inequalityCurves(decomposition, l = 1)
contrib12 <- inequalityCurves(decomposition, l = 1:2)

plot(ic)
plot(contrib1, add = TRUE)
plot(contrib12, add = TRUE)
text(0.1, 1/6+0:2/3, labels = c("G1", "G2", "G3"))

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)

Point and synthetic Zenga 2007 indexes

Description

Computes point and synthetic Zenga 2007 indexes on a variable YY.

Usage

zenga(x)

Arguments

x

List containing: 'yh', the vector of unique values of the variable YY whose Bonferroni index is computed; 'Phl', the matrix of absoute cumulative frequencies; 'Qhlk', the matrix of cumlative sums of y or its sources. x is usually the result of dataProcessing function. More details are given in the "Details" section and dataProcessing help page.

Details

zenga compute point and synthetic Zenga 2007 indexes on a variable y, e.g. income, on a statistical population that could be partitioned in g subpopultions and could be considered as sum of c sources, e.g. income sources.

Value

index

String denoting computed index.
decomposition

Array containing the decompositions.
x

Object usually of class dataProcessed passed as input.

Author(s)

Alberto Arcagni, Igor Valli

References

Zenga M. M.(2007). Inequality Curve and Inequality Index based on the Ratios between lower 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.

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)
decomposition