networkInferenceGlassoBIC: Network inference using the glasso algorithm

In shock: Slope Heuristic for Block-Diagonal Covariance Selection in High Dimensional Gaussian Graphical Models

Description

This function performs network inference using the glasso algorithm for several regularization parameters and selects a network based on the BIC of the model.

Usage

networkInferenceGlassoBIC(dataNet, nb.rho = 100)

Arguments

dataNet	matrix of data
nb.rho	number of regularization parameters to test in the glasso algorithm

Value

A	selected adjacency matrix based on BIC
Theta	selected precision matrix based on BIC
Sigma	selected covariance matrix based on BIC
penaltieslist	list of regularization parameters
pathA	list of adjacency matrices for each regularization parameter
pathTheta	list of precision matrices for each regularization parameter
pathSigma	list of covariance matrices for each regularization parameter
pathBIC	list of BIC values for each regularization parameter

References

https://cran.r-project.org/web/packages/glasso/glasso.pdf

Examples

## load data to test 
data(dataTest)

## perform network inference 
resNet <- networkInferenceGlassoBIC(dataTest)

shock documentation built on May 2, 2019, 8:55 a.m.