Data.Incomes

Fictitious cross-sectional dataset used to illustrate the Lorenz regression methodology. It covers 7 variables for 200 individuals aged between 25 and 30 years.

datasets

Inference for the Lorenz and penalized Lorenz regressions. More broadly, the package proposes functions to assess inequality and graphically represent it. The Lorenz Regression procedure is introduced in Heuchenne and Jacquemain (2022) <doi:10.1016/j.csda.2021.107347> and in Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024) <doi:10.1214/23-EJS2200>.

Alexandre Jacquemain

LorenzRegression

Lorenz and Penalized Lorenz Regressions

Xingjie Shi

Data.Incomes function

A data frame with 200 rows and 7 columns:<dl>
 <dt>Income</dt>
<dd>Individual's labor income</dd> <dt>Sex</dt>
<dd>Sex (0=Female, 1=Male)</dd> <dt>Health.level</dt>
<dd>Variable ranging from 0 to 10 indicating the individual health's level (0 is worst, 10 is best)</dd> <dt>Age</dt>
<dd>Individual's age in years, ranging from 25 to 30</dd> <dt>Work.Hours</dt>
<dd>Individual's weekly work hours</dd> <dt>Education</dt>
<dd>Individual's highest grade completed in years</dd> <dt>Seniority</dt>
<dd>Length of service in years with the individual's employer</dd>
</dl>

Format

Simulated income data — Data.Incomes

A data frame with 200 rows and 7 columns:<dl>
 <dt>Income</dt>
<dd>Individual's labor income</dd>

 <dt>Sex</dt>
<dd>Sex (0=Female, 1=Male)</dd>

 <dt>Health.level</dt>
<dd>Variable ranging from 0 to 10 indicating the individual health's level (0 is worst, 10 is best)</dd>

 <dt>Age</dt>
<dd>Individual's age in years, ranging from 25 to 30</dd>

 <dt>Work.Hours</dt>
<dd>Individual's weekly work hours</dd>

 <dt>Education</dt>
<dd>Individual's highest grade completed in years</dd>

 <dt>Seniority</dt>
<dd>Length of service in years with the individual's employer</dd>


</dl>

Data.Incomes: Simulated income data

Description

Usage

Arguments

Format