net.hubs | R Documentation |
List the degrees of the most connected nodes in each class.
net.hubs(theta, nhubs = 10)
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. |
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.
Patrick Danaher
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
## 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)
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