net.hubs {JGL}

Get degrees of most connected nodes.

Description

List the degrees of the most connected nodes in each class.

Usage

net.hubs(theta, nhubs = 10)

Arguments

theta

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

nhubs

The number of hubs to be identified. net.hubs() will list the degree of the nhubs most connected nodes in each class.

Value

hubs, a list of length K, each element of which is a vector giving the degree of the most connected nodes in the corresponding 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 hubs list:
net.hubs(fgl.results$theta)