PLR.normalize

<code>PLR.normalize</code> transforms the estimated coefficients of a penalized Lorenz regression to match the model where the first category of each categorical variable is omitted.

internal

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

PLR.normalize function

<dl><dt>object</dt>
<dd>An object of S3 class <code>"PLR"</code>. The object must also contain a <code>"theta"</code> slot with the estimated coefficients</dd></dl>

Arguments

Re-normalizes the estimated coefficients of a penalized Lorenz regression — PLR.normalize

<dl>

<dt>object</dt>
<dd>An object of S3 class <code>"PLR"</code>. The object must also contain a <code>"theta"</code> slot with the estimated coefficients</dd>

</dl>

PLR.normalize: Re-normalizes the estimated coefficients of a penalized Lorenz regression

Description

Usage

Value

Arguments

See Also