hglassoBIC {hglasso} | R Documentation |
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 |
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). |
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.
See Also
Examples
#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))]
[Package hglasso version 1.3 Index]