extendedBIC {iDINGO}

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

Author(s)

Min Jin Ha <[e-mail]>

References

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

Examples

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