SelectionProportionsRegression

Extracts the selection proportions of the (calibrated) stability selection
model.

internal

In stability selection (N Meinshausen, P Bühlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>) and consensus clustering (S Monti et al (2003) <doi:10.1023/A:1023949509487>), resampling techniques are used to enhance the reliability of the results. In this package (B Bodinier et al (2025) <doi:10.18637/jss.v112.i05>), hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) <doi:10.1093/jrsssc/qlad058> and B Bodinier et al (2023b) <doi:10.1093/bioinformatics/btad635>). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering.

Barbara Bodinier

sharp

Stability-enHanced Approaches using Resampling Procedures

SelectionProportionsRegression function

<dl><dt>stability</dt>
<dd>output of <code>VariableSelection</code>.</dd>
<dt>argmax_id</dt>
<dd>optional indices of hyper-parameters. If
<code>argmax_id=NULL</code>, the calibrated hyper-parameters are used.</dd></dl>

Arguments

Selection proportions (variable selection) — SelectionProportionsRegression

<dl>

<dt>stability</dt>
<dd>output of <code>VariableSelection</code>.</dd>


<dt>argmax_id</dt>
<dd>optional indices of hyper-parameters. If
<code>argmax_id=NULL</code>, the calibrated hyper-parameters are used.</dd>

</dl>

SelectionProportionsRegression: Selection proportions (variable selection)

Description

Usage

Value

Arguments