huge_glasso

Graphical lasso

Package

huge

Version

1.3.5

Language

R

Docs

here

Paper

Zhao et al.[1], Friedman et al.[2]

Graph type

UG

Docker

bpimages/huge:1.3.5

Module folder

huge_glasso

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 10

  • select_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
    }
  ]
]