plot_clusterpath

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

plot_clusterpath function

<dl><dt>X</dt>
<dd>numeric matrix</dd>
<dt>mglasso_res</dt>
<dd>object of class <code>mglasso</code></dd>
<dt>colnames_</dt>
<dd>columns labels</dd></dl>

Arguments

Plot MGLasso Clusterpath — plot_clusterpath

<dl>

<dt>X</dt>
<dd>numeric matrix</dd>


<dt>mglasso_res</dt>
<dd>object of class <code>mglasso</code></dd>


<dt>colnames_</dt>
<dd>columns labels</dd>

</dl>

plot_clusterpath: Plot MGLasso Clusterpath

Description

Usage

Value

Arguments

Details