beta_to_vector

Transform a matrix of regression coefficients to vector removing the diagonal

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

beta_to_vector function

<dl><dt>beta_mat</dt>
<dd>matrix of regressions vectors</dd></dl>

Arguments

Transform a matrix of regression coefficients to vector removing the diagonal — beta_to_vector

<dl>

<dt>beta_mat</dt>
<dd>matrix of regressions vectors</dd>

</dl>

beta_to_vector: Transform a matrix of regression coefficients to vector removing the diagonal

Description

Usage

Value

Arguments