| blockBGEN {baygel} | R Documentation |
Naive Bayesian graphical elastic net block Gibbs sampler for Gaussian graphical models.
Description
Implements the Bayesian graphical elastic net block Gibbs sampler to simulate the posterior distribution of the precision matrix for Gaussian graphical models.
Usage
blockBGEN(X, burnin, iterations, lambda = 1, sig = 1, verbose = TRUE)
Arguments
X |
A numeric matrix, assumed to be generated from a multivariate Gaussian distribution. |
burnin |
An integer specifying the number of burn-in iterations. |
iterations |
An integer specifying the length of the Markov chain after the burn-in iterations. |
lambda |
A numeric value representing the rate parameter for the double exponential and exponential prior associated with the Bayesian graphical lasso penalty term. |
sig |
A numeric value representing the standard deviation parameter for the double Gaussian and truncated Gaussian prior associated with the Bayesian graphical ridge penalty term. |
verbose |
A logical determining whether the progress of the MCMC sampler should be displayed. |
Value
A list containing precision 'Omega' and covariance 'Sigma' matrices from the Markov chains.
Examples
# Generate true precision matrix:
p <- 10
n <- 500
OmegaTrue <- pracma::Toeplitz(c(0.7^rep(1:p-1)))
SigTrue <- pracma::inv(OmegaTrue)
# Generate expected value vector:
mu <- rep(0,p)
# Generate multivariate normal distribution:
set.seed(123)
X <- MASS::mvrnorm(n, mu = mu, Sigma = SigTrue)
# Generate posterior distribution:
posterior <- blockBGEN(X, iterations = 1000, burnin = 500, lambda = 1, sig = 1)
# Estimated precision matrix using the mean of the posterior:
OmegaEst <- apply(simplify2array(posterior$Omega), 1:2, mean)