cost

<code>cost</code> computes the cost function of <code>Mglasso</code> method.

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

cost function

<dl><dt>beta</dt>
<dd>p by p numeric matrix. In rows, regression vectors coefficients after node-wise regression. <code>diag(beta) = 0</code>.</dd>
<dt>x</dt>
<dd>n by p numeric matrix. Data with variables in columns.</dd>
<dt>lambda1</dt>
<dd>numeric scalar. Lasso penalization parameter.</dd>
<dt>lambda2</dt>
<dd>numeric scalar. Fused-group Lasso penalization parameter.</dd></dl>

Arguments

Mglasso cost function — cost

<dl>

<dt>beta</dt>
<dd>p by p numeric matrix. In rows, regression vectors coefficients after node-wise regression. <code>diag(beta) = 0</code>.</dd>


<dt>x</dt>
<dd>n by p numeric matrix. Data with variables in columns.</dd>


<dt>lambda1</dt>
<dd>numeric scalar. Lasso penalization parameter.</dd>


<dt>lambda2</dt>
<dd>numeric scalar. Fused-group Lasso penalization parameter.</dd>

</dl>

cost: `Mglasso` cost function

Description

Usage

Value

Arguments