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)


[Package baygel version 0.3.0 Index]