merge_clusters

compute clusters partition from pairs of variables to merge

Inference of Multiscale graphical models with neighborhood
selection approach. The method is based on solving a convex
optimization problem combining a Lasso and fused-group Lasso
penalties. This allows to infer simultaneously a conditional
independence graph and a clustering partition. The optimization is
based on the Continuation with Nesterov smoothing in a
Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018)
<doi:10.1109/TMI.2018.2829802> implemented in python.

Edmond Sanou

mglasso

Multiscale Graphical Lasso

Tung Le

Christophe Ambroise

Geneviève Robin

merge_clusters function

<dl><dt>pairs_to_merge</dt>
<dd>table of the indices of variables to be merge</dd>
<dt>clusters</dt>
<dd>numeric vector. By default 1:p where p is the number of variables</dd></dl>

Arguments

compute clusters partition from pairs of variables to merge — merge_clusters

<dl>

<dt>pairs_to_merge</dt>
<dd>table of the indices of variables to be merge</dd>


<dt>clusters</dt>
<dd>numeric vector. By default 1:p where p is the number of variables</dd>

</dl>

merge_clusters: compute clusters partition from pairs of variables to merge

Description

Usage

Value

Arguments