coef.PLR

coef.PLR_boot

coef.PLR_cv

Provides the estimated coefficients for 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

coef.PLR function

<dl><dt>object</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>renormalize</dt>
<dd>A logical value determining whether the coefficient vector should be re-normalized to match the representation where the first category of each categorical variable is omitted. Default value is TRUE</dd>
<dt>pars.idx</dt>
<dd>What grid and penalty parameters should be used for parameter selection. Either a character string specifying the selection method, where the possible values are:<ul>
<li><code>"BIC"</code> (default) - Always available.</li>
<li><code>"Boot"</code> - Available if <code>object</code> inherits from <code>"PLR_boot"</code>.</li>
<li><code>"CV"</code> - Available if <code>object</code> inherits from <code>"PLR_cv"</code>.</li>
</ul>Or a numeric vector of length 2, where the first element is the index of the grid parameter and the second is the index of the penalty parameter.</dd>
<dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Estimated coefficients for the penalized Lorenz regression — coef.PLR

<dl>

<dt>object</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>renormalize</dt>
<dd>A logical value determining whether the coefficient vector should be re-normalized to match the representation where the first category of each categorical variable is omitted. Default value is TRUE</dd>


<dt>pars.idx</dt>
<dd>What grid and penalty parameters should be used for parameter selection. Either a character string specifying the selection method, where the possible values are:<ul>
<li><code>"BIC"</code> (default) - Always available.</li>
<li><code>"Boot"</code> - Available if <code>object</code> inherits from <code>"PLR_boot"</code>.</li>
<li><code>"CV"</code> - Available if <code>object</code> inherits from <code>"PLR_cv"</code>.</li>
</ul>Or a numeric vector of length 2, where the first element is the index of the grid parameter and the second is the index of the penalty parameter.</dd>


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

</dl>

coef.PLR: Estimated coefficients for the penalized Lorenz regression

Description

Usage

Value

Arguments

See Also

Examples