net.degree: List the degree of every node in all classes.

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

List the degree of every node in all classes.

Description

For each class, lists the degree of every node.

Usage

net.degree(theta)

Arguments

theta

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

Value

degree, a list of p-length vectors, each giving the degree of all p nodes in the network for 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 degree list:
net.degree(fgl.results$theta)

patrickjdanaher/JGL-package documentation built on Jan. 11, 2024, 12:56 a.m.