HubNetwork: Hub network generation

In hglasso: Learning Graphical Models with Hubs

Hub network generation

Description

Generate an inverse covariance matrix, covariance matrix, or binary network with hub structure

Usage

HubNetwork(p, sparsity, hubnumber, hubsparsity, type = "Gaussian")

Arguments

p	The number of features
sparsity	Sparsity of the network
hubnumber	The number of hubs in the network
hubsparsity	Sparsity level within each hub
type	Type of network. The default value type="Gaussian" generates an inverse covariance matrix. type="covariance" generates a covariance matrix with hubs. type="binary" generates a binary network with hubs.

Value

Theta	Theta is the generated inverse covariance matrix, covariance matrix, or binary network.
hubcol	hubcol contains indices for features that are hubs.

Author(s)

Kean Ming Tan

References

Tan et al. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.

Examples

# Generate inverse covariance matrix with 5 hubs
# 30% of the elements within a hub are zero 
# 95% of the elements that are not within hub nodes are zero
p <- 100
Theta <- HubNetwork(p,0.95,5,0.3)$Theta

# Generate covariance matrix with 5 hubs with similar structure
Sigma <- HubNetwork(p,0.95,5,0.3,type="covariance")$Theta

# Generate binary network with 2 hubs with p=10
Theta <- HubNetwork(p=10,0.95,2,0.3,type="binary")$Theta

hglasso documentation built on May 13, 2022, 9:06 a.m.