Lorenz.Suggestions

<code>Lorenz.Suggestions</code> creates suggestions for the genetic algorithm used to solve the 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

Lorenz.Suggestions function

<dl><dt>suggestions</dt>
<dd>either a character string 'OLS' or a numeric matrix with at most <code>popSize</code> rows and <code>ncol(x)</code> columns.</dd>
<dt>popSize</dt>
<dd>population size of the genetic algorithm</dd>
<dt>y</dt>
<dd>vector of responses</dd>
<dt>x</dt>
<dd>matrix of covariates (after standardization if applied)</dd>
<dt>pi</dt>
<dd>vector of normalized weights</dd>
<dt>x.scale</dt>
<dd>vector of standard deviations of the covariates</dd>
<dt>seed</dt>
<dd>seed used in the generation of the suggestions</dd></dl>

Arguments

Defines the suggestions used in the genetic algorithm — Lorenz.Suggestions

<dl>

<dt>suggestions</dt>
<dd>either a character string 'OLS' or a numeric matrix with at most <code>popSize</code> rows and <code>ncol(x)</code> columns.</dd>


<dt>popSize</dt>
<dd>population size of the genetic algorithm</dd>


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


<dt>x</dt>
<dd>matrix of covariates (after standardization if applied)</dd>


<dt>pi</dt>
<dd>vector of normalized weights</dd>


<dt>x.scale</dt>
<dd>vector of standard deviations of the covariates</dd>


<dt>seed</dt>
<dd>seed used in the generation of the suggestions</dd>

</dl>

Lorenz.Suggestions: Defines the suggestions used in the genetic algorithm

Description

Usage

Value

Arguments

See Also