| blockBAGENII {baygel} | R Documentation |
Type II naive Bayesian adaptive graphical elastic net block Gibbs sampler for Gaussian graphical models.
Description
Implements the Type II naive Bayesian adaptive graphical elastic net block Gibbs sampler to simulate the posterior distribution of the precision matrix for Gaussian graphical models.
Usage
blockBAGENII(X, burnin, iterations, verbose = TRUE, s = 0.1, b = 0.001)
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. |
verbose |
A logical determining whether the progress of the MCMC sampler should be displayed. |
s |
A double specifying the value of the rate parameter for the exponential prior associated with the Bayesian graphical lasso penalty term. |
b |
A double specifying the value of the rate parameter for the exponential prior associated with the Bayesian graphical ridge penalty term. |
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 <- blockBAGENII(X, iterations = 1000, burnin = 500)
# Estimated precision matrix using the mean of the posterior:
OmegaEst <- apply(simplify2array(posterior$Omega), 1:2, mean)