.Fitness_cpp

Computes the fitness of a candidate in the genetic algorithm displayed in function Lorenz.GA.cpp

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

.Fitness_cpp function

<dl><dt>x</dt>
<dd>vector of size (p-1) giving the proposed candidate, where p is the number of covariates</dd>
<dt>Y</dt>
<dd>vector of size n gathering the response, where n is the sample size</dd>
<dt>X</dt>
<dd>matrix of dimension (n*p) gathering the covariates</dd>
<dt>Z</dt>
<dd>vector of size n gathering iid repetitions of a U[0,1]</dd>
<dt>pi</dt>
<dd>vector of size n gathering the observation weights (notice that sum(pi)=1)</dd>
<dt>tolerance</dt>
<dd>A small positive number used to determine the threshold for considering two floating-point numbers as equal. This is primarily used to
address issues with floating-point precision when comparing values that should theoretically be identical but may differ slightly due to numerical inaccuracies.</dd></dl>

Arguments

Computes the fitness used in the GA — .Fitness_cpp

<dl>

<dt>x</dt>
<dd>vector of size (p-1) giving the proposed candidate, where p is the number of covariates</dd>


<dt>Y</dt>
<dd>vector of size n gathering the response, where n is the sample size</dd>


<dt>X</dt>
<dd>matrix of dimension (n*p) gathering the covariates</dd>


<dt>Z</dt>
<dd>vector of size n gathering iid repetitions of a U[0,1]</dd>


<dt>pi</dt>
<dd>vector of size n gathering the observation weights (notice that sum(pi)=1)</dd>


<dt>tolerance</dt>
<dd>A small positive number used to determine the threshold for considering two floating-point numbers as equal. This is primarily used to
address issues with floating-point precision when comparing values that should theoretically be identical but may differ slightly due to numerical inaccuracies.</dd>

</dl>

.Fitness_cpp: Computes the fitness used in the GA

Description

Usage

Value

Arguments