blockGLasso {BayesianGLasso} R Documentation

## Block Gibbs sampler function

### Description

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

### Usage

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


### Arguments

 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

### Details

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

### Value

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

### Author(s)

Patrick Trainor (University of Louisville)

Hao Wang

### References

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

### Examples


# 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)


[Package BayesianGLasso version 0.2.0 Index]