performSelectionLoop

Implements Stage 2 of the FSCRE algorithm: the iterative competitive selection.

internal

Functions to perform robust variable selection and regression using the Fast and Scalable
Cellwise-Robust Ensemble (FSCRE) algorithm. The approach establishes a robust foundation
using the Detect Deviating Cells (DDC) algorithm and robust correlation estimates. It then
employs a competitive ensemble architecture where a robust Least Angle Regression (LARS)
engine proposes candidate variables and cross-validation arbitrates their assignment. A final
robust MM-estimator is applied to the selected predictors.

Anthony Christidis

srlars

Fast and Scalable Cellwise-Robust Ensemble

Gabriela Cohen-Freue

performSelectionLoop function

<dl><dt>Rx</dt>
<dd>Global robust predictor correlation matrix.</dd>
<dt>ry</dt>
<dd>Global robust predictor-response correlation vector.</dd>
<dt>x.imp</dt>
<dd>Imputed design matrix (for CV).</dd>
<dt>y.imp</dt>
<dd>Imputed response vector (for CV).</dd>
<dt>n_models</dt>
<dd>Number of models (K).</dd>
<dt>max_predictors</dt>
<dd>Maximum total predictors to select.</dd>
<dt>tolerance</dt>
<dd>Stopping tolerance.</dd></dl>

Arguments

FSCRE Competitive Selection Loop (Internal) — performSelectionLoop

<dl>

<dt>Rx</dt>
<dd>Global robust predictor correlation matrix.</dd>


<dt>ry</dt>
<dd>Global robust predictor-response correlation vector.</dd>


<dt>x.imp</dt>
<dd>Imputed design matrix (for CV).</dd>


<dt>y.imp</dt>
<dd>Imputed response vector (for CV).</dd>


<dt>n_models</dt>
<dd>Number of models (K).</dd>


<dt>max_predictors</dt>
<dd>Maximum total predictors to select.</dd>


<dt>tolerance</dt>
<dd>Stopping tolerance.</dd>

</dl>

performSelectionLoop: FSCRE Competitive Selection Loop (Internal)

Description

Usage

Value

Arguments