ParetoR_2C

Command function to optimize 2 non-adverse impact objectives via NBI
algorithm

Estimates Pareto-optimal solution for personnel selection
with 3 objectives using Normal Boundary Intersection (NBI) algorithm
introduced by Das and Dennis (1998) <doi:10.1137/S1052623496307510>.
Takes predictor intercorrelations and predictor-objective relations as
input and generates a series of solutions containing predictor weights
as output. Accepts between 3 and 10 selection predictors. Maximum 2
objectives could be adverse impact objectives. Partially modeled after
De Corte (2006) TROFSS Fortran program
<https://users.ugent.be/~wdecorte/trofss.pdf> and updated from
'ParetoR' package described in Song et al. (2017)
<doi:10.1037/apl0000240>. For details, see Study 3 of Zhang et al. (2023).

Chelsea Song

rMOST

Estimates Pareto-Optimal Solution for Hiring with 3 Objectives

Yesuel Kim

ParetoR_2C function

<dl><dt>Rx</dt>
<dd>Matrix with intercorrelations among predictors</dd>
<dt>Rxy1</dt>
<dd>Vector with correlation between each predictor and non-adverse impact objective 1</dd>
<dt>Rxy2</dt>
<dd>Vector with correlation between each predictor and non-adverse impact objective 2</dd>
<dt>Spac</dt>
<dd>Number of Pareto points</dd>
<dt>graph</dt>
<dd>If TRUE, plots will be generated for Pareto-optimal curve and
predictor weights</dd></dl>

Arguments

ParetoR_2C — ParetoR_2C

<dl>

<dt>Rx</dt>
<dd>Matrix with intercorrelations among predictors</dd>


<dt>Rxy1</dt>
<dd>Vector with correlation between each predictor and non-adverse impact objective 1</dd>


<dt>Rxy2</dt>
<dd>Vector with correlation between each predictor and non-adverse impact objective 2</dd>


<dt>Spac</dt>
<dd>Number of Pareto points</dd>


<dt>graph</dt>
<dd>If TRUE, plots will be generated for Pareto-optimal curve and
predictor weights</dd>

</dl>

ParetoR_2C: ParetoR_2C

Description

Usage

Value

Arguments