adj_mat

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

adj_mat function

<dl><dt>mat</dt>
<dd>matrix of regression coefficients</dd>
<dt>sym_rule</dt>
<dd>symmetrization rule, either AND or OR</dd></dl>

Arguments

Adjacency matrix — adj_mat

<dl>

<dt>mat</dt>
<dd>matrix of regression coefficients</dd>


<dt>sym_rule</dt>
<dd>symmetrization rule, either AND or OR</dd>

</dl>

adj_mat: Adjacency matrix

Description

Usage

Value

Arguments