hglasso (version 1.3)

hglassoBIC: BIC-type criterion for hglasso

Description

This function calculates the BIC-type criterion for tuning parameter selection for hglasso proposed in Section 3.4 in Tan et al. (2014)

Usage

hglassoBIC(x, S, c=0.2)

Arguments

x

An object of class hglasso.

S

A p by p correlation/covariance matrix. Cannot contain missing values.

c

A constant between 0 and 1. When c is small, the BIC-type criterion will favor more hub nodes. The default value is c=0.2.

Value

BIC

The calculated BIC-type criterion in Section 3.4 in Tan et al. (2014).

References

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

See Also

hglasso

Examples

# NOT RUN {
#library(mvtnorm)
#library(glasso)
#set.seed(1)
#n=100
#p=100

# A network with 4 hubs
#network<-HubNetwork(p,0.99,4,0.1)
#Theta <- network$Theta
#truehub <- network$hubcol
# The four hub nodes have indices 14, 42, 45, 78
#print(truehub)

# Generate data matrix x
#x <- rmvnorm(n,rep(0,p),solve(Theta))
#x <- scale(x)
#S <- cov(x)
# Run Hub Graphical Lasso with different tuning parameters
#lambdas2 <- seq(0,0.5,by=0.05)
#BICcriterion <- NULL
#for(lambda2 in lambdas2){
#res1 <- hglasso(S,0.3,lambda2,1.5)
#BICcriterion <- c(BICcriterion,hglassoBIC(res1,S)$BIC)
#}
#lambda2 <- lambdas2[which(BICcriterion==min(BICcriterion))]
# }