iDINGO (version 1.0.4)

extendedBIC: Extended bayesian information criteria for gaussian graphical models

Description

Extended bayesian information criteria for gaussian graphical models

Usage

extendedBIC(gamma,omegahat,S,n)

Arguments

gamma

a tuning parameter taking a scalar in [0,1] and leading to stronger penalization of large graphs

omegahat

a p x p matrix indicating an estimates of precision (inverse covariance) matrix

S

a p x p matrix indicating sample covariance matrix

n

a scalar indicating sample size

Value

Extended BIC penalized by the size of graphs

References

Foygel, R. and Drton, M. (2010). Extended bayesian information criteria for gaussian graphical models. arXiv preprint arXiv:1011.6640 .

Examples

# NOT RUN {
library(glasso)
data(gbm)
x = gbm[,1]
Y = gbm[,-1]

# Estimating inverse covariance matrix using GLasso #
S = cov(Y)

rhoarray = exp(seq(log(0.001),log(1),length=100))
BIC = rep(0,length(rhoarray))
for (rh in 1:length(rhoarray)) {
    fit.gl1 = glasso(S,rho=rhoarray[rh])
    BIC[rh] = extendedBIC(gamma=0,omegahat=fit.gl1$wi,S=S,n=nrow(Y))
}
rho = rhoarray[which.min(BIC)]
fit.gl2 = glasso(S,rho=rho)
Omega = fit.gl2$wi


# }