HubNetwork: Hub network generation
In hglasso: Learning graphical models with hubs

Description Usage Arguments Value Author(s) References Examples

View source: R/HubNetwork.R

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

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

`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.

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

Kean Ming Tan

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

# 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