huge_glasso
Graphical lasso
Package |
|
Version |
1.3.5 |
Language |
|
Docs |
|
Paper |
|
Graph type |
|
Docker |
|
Module folder |
Description
Abstract: We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (∼500000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
Some fields described
lambda
A positive number to control the regularization. Typical usage is to leave the input lambda: null and have the program compute its own.nlambda
The number of regularization/thresholding parameters. The default value is 10select_criterion
Model selection criterion. ric, stars, and ebic are available. The default value is ric.
Example JSON
[
[
{
"id": "huge_glasso",
"lambda": [
2,
1,
0.8,
0.6,
0.4,
0.2,
0.1,
0.05,
0.01
],
"nlambda": null,
"select_criterion": "ebic",
"timeout": null
}
]
]