JGL (version 2.3.2)

subnetworks: Identify subnetwork membership

Description

For each class, returns lists of all features belonging to subnetworks. (A subnetwork is defined as a collection of features C for which theta[C,!C]==0, and within which no further subnetworks can be identified. In other words, a block in the block diagonal structure of theta, or a set of features that can be connected through theta's edges.)

Usage

subnetworks(theta)

Value

A list length K, each element of which is a list of subnetworks in class K. Each subnetwork is represented as a vector of feature names.

Arguments

theta

A list of pXp matrices, each an estimated sparse inverse covariance matrix. (For example, the result of FGL or GGL.)

Author

Patrick Danaher

References

Patrick Danaher, Pei Wang and Daniela Witten (2011). The joint graphical lasso for inverse covariance estimation across multiple classes. http://arxiv.org/abs/1111.0324

Examples

## load an example dataset with K=two classes, p=200 features, and n=100 samples per class:
data(example.data)
str(example.data)
## run fgl:
fgl.results = JGL(Y=example.data,penalty="fused",lambda1=.25,lambda2=.1)
## identify subnetworks
subnetworks(fgl.results$theta)