blockGLasso: Block Gibbs sampler function
In BayesianGLasso: Bayesian Graphical Lasso

Description Usage Arguments Details Value Author(s) References Examples

View source: R/blockGLasso.R

Blockwise sampling from the conditional distribution of a permuted column/row for simulating the posterior distribution for the concentration matrix specifying a Gaussian Graphical Model

1 2	blockGLasso(X, iterations = 2000, burnIn = 1000, lambdaPriora = 1, lambdaPriorb = 1/10, verbose = TRUE)

`X`	Data matrix
`iterations`	Length of Markov chain after burn-in
`burnIn`	Number of burn-in iterations
`lambdaPriora`	Shrinkage hyperparameter (lambda) gamma distribution shape
`lambdaPriorb`	Shrinkage hyperparameter (lambda) gamma distribution scale
`verbose`	logical; if TRUE return MCMC progress

Implements the block Gibbs sampler for the Bayesian graphical lasso introduced in Wang (2012). Samples from the conditional distribution of a permuted column/row for simulating the posterior distribution for the concentration matrix specifying a Gaussian Graphical Model

`Sigma`	List of covariance matrices from the Markov chain
`Omega`	List of concentration matrices from the Markov chains
`Lambda`	Vector of simulated lambda parameters

Patrick Trainor (University of Louisville)

Hao Wang

Wang, H. (2012). Bayesian graphical lasso models and efficient posterior computation. Bayesian Analysis, 7(4). <doi:10.1214/12-BA729> .

# Generate true covariance matrix:
s<-.9**toeplitz(0:9)
# Generate multivariate normal distribution:
set.seed(5)
x<-MASS::mvrnorm(n=100,mu=rep(0,10),Sigma=s)
blockGLasso(X=x)

# Same example with short MCMC chain:
s<-.9**toeplitz(0:9)
set.seed(6)
x<-MASS::mvrnorm(n=100,mu=rep(0,10),Sigma=s)
blockGLasso(X=x,iterations=100,burnIn=100)