PLR.scores

<code>PLR.scores</code> computes the Gini scores (either OOB-scores or CV-scores) obtained for a specific validation sample and associated to a list of parameters obtained by the Penalized Lorenz Regression.

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.scores function

<dl><dt>y</dt>
<dd>the vector of responses</dd>
<dt>x</dt>
<dd>the design matrix (after data management steps, i.e. standardization and transformations of the categorical covariates into binaries)</dd>
<dt>weights</dt>
<dd>vector of sample weights. By default, each observation is given the same weight.</dd>
<dt>theta.list</dt>
<dd>list of matrices. Each element of the list correspond to a value of the grid parameter. The columns of the matrices correspond to values of the penalty parameters. The rows correspond to the different covariates.</dd></dl>

Arguments

Computes Gini scores for the Penalized Lorenz Regression — PLR.scores

<dl>

<dt>y</dt>
<dd>the vector of responses</dd>


<dt>x</dt>
<dd>the design matrix (after data management steps, i.e. standardization and transformations of the categorical covariates into binaries)</dd>


<dt>weights</dt>
<dd>vector of sample weights. By default, each observation is given the same weight.</dd>


<dt>theta.list</dt>
<dd>list of matrices. Each element of the list correspond to a value of the grid parameter. The columns of the matrices correspond to values of the penalty parameters. The rows correspond to the different covariates.</dd>

</dl>

PLR.scores: Computes Gini scores for the Penalized Lorenz Regression

Description

Usage

Value

Arguments