| Title: | Bayesian Graphical Lasso |
| Version: | 0.2.0 |
| Description: | Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012) <doi:10.1214/12-BA729>. |
| Depends: | R (≥ 3.0.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | statmod, MASS |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2017-07-18 22:45:03 UTC; patrick |
| Author: | Patrick Trainor [aut, cre], Hao Wang [aut] |
| Maintainer: | Patrick Trainor <patrick.trainor@louisville.edu> |
| Repository: | CRAN |
| Date/Publication: | 2017-07-19 10:52:36 UTC |
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)