LambdaSequence

Generates a sequence of penalty parameters from extreme values and the
required number of elements. The sequence is defined on the log-scale.

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

LambdaSequence function

<dl><dt>lmax</dt>
<dd>maximum value in the grid.</dd>
<dt>lmin</dt>
<dd>minimum value in the grid.</dd>
<dt>cardinal</dt>
<dd>number of values in the grid.</dd></dl>

Arguments

Sequence of penalty parameters — LambdaSequence

<dl>

<dt>lmax</dt>
<dd>maximum value in the grid.</dd>


<dt>lmin</dt>
<dd>minimum value in the grid.</dd>


<dt>cardinal</dt>
<dd>number of values in the grid.</dd>

</dl>

LambdaSequence: Sequence of penalty parameters

Description

Usage

Value

Arguments

See Also

Examples