net.edges: List the edges in a network

In JGL: Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes

Description

For each class, list every pair of connected nodes.

Usage

net.edges(theta)

Arguments

theta

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

Value

edges, a K-length list, each element of the list an igraph.es object detailing all pairs of connected nodes in the class.

Author(s)

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)
## get edges list:
net.edges(fgl.results$theta)

JGL documentation built on May 2, 2019, 12:40 p.m.