print.PLR

print.PLR_boot

print.PLR_cv

Prints the arguments, explained Gini coefficient and estimated coefficients of an object of class <code>"PLR"</code>.

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

print.PLR function

<dl><dt>x</dt>
<dd>An object of S3 class <code>"PLR"</code>. The object might also have S3 classes <code>"PLR_boot"</code> and/or <code>"PLR_cv"</code> (both inherit from class <code>"PLR"</code>)</dd>
<dt>digits</dt>
<dd>The number of significant digits to be passed.</dd>
<dt>...</dt>
<dd>Additional arguments.</dd></dl>

Arguments

Printing method for the penalized Lorenz regression — print.PLR

<dl>

<dt>x</dt>
<dd>An object of S3 class <code>"PLR"</code>. The object might also have S3 classes <code>"PLR_boot"</code> and/or <code>"PLR_cv"</code> (both inherit from class <code>"PLR"</code>)</dd>


<dt>digits</dt>
<dd>The number of significant digits to be passed.</dd>


<dt>...</dt>
<dd>Additional arguments.</dd>

</dl>

print.PLR: Printing method for the penalized Lorenz regression

Description

Usage

Value

Arguments

Details

See Also

Examples