Computes the False Discovery Proportion (upper-bound) as a ratio of the PFER
(upper-bound) over the number of stably selected features. In stability
selection, the FDP corresponds to the expected proportion of stably selected
features that are not relevant to the outcome (i.e. proportion of False
Positives among stably selected features).

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

FDP function

<dl><dt>selprop</dt>
<dd>matrix or vector of selection proportions.</dd>
<dt>PFER</dt>
<dd>Per Family Error Rate.</dd>
<dt>pi</dt>
<dd>threshold in selection proportions.</dd></dl>

Arguments

False Discovery Proportion — FDP

<dl>

<dt>selprop</dt>
<dd>matrix or vector of selection proportions.</dd>


<dt>PFER</dt>
<dd>Per Family Error Rate.</dd>


<dt>pi</dt>
<dd>threshold in selection proportions.</dd>

</dl>

FDP: False Discovery Proportion

Description

Usage

Value

Arguments

See Also

Examples