CompWiseGibbsSimple: Generate the posterior samples from the posterior...

In BSGS: Bayesian Sparse Group Selection

Description

Generate the posterior samples using MCMC procedures.

Usage

CompWiseGibbsSimple(Y, X, beta.value, r, tau2, rho, sigma2, nu, lambda,
                    num.of.inner.iter, num.of.iteration, MCSE.Sigma2.Given)

Arguments

Y	vector of observations of length n.
X	design matrix of dimension n \times p.
beta.value	Initial values of regression coefficients, β.
r	Initial values of indicator variables for individual regressors.
tau2	Variance in the prior distribution for regression coefficients.
rho	Prior probability including a variable.
sigma2	Initial value of σ^2.
nu	The hyperparameter in the prior distribution of σ^2.
lambda	The hyperparameter in the prior distribution of σ^2.
num.of.inner.iter	The number of iterations before sampling σ^2.
num.of.iteration	The number of iterations to be runned for sparse group variable selection.
MCSE.Sigma2.Given	Prespecified value which is used to stop simulating samples when the MCSE of estimate of σ^2 less then given values.

Value

A list is returned with posterior samples of regression coefficients, β, variance σ^2, binary variables, γ, the number of iterations performed, and the time in second required for the run.

Examples

## Not run: 
CompWiseGibbsSimple(Y, X, beta.value, r, tau2, rho, sigma2, nu, lambda,
 num.of.inner.iter.default, num.of.iteration, MCSE.Sigma2.Given)
## End(Not run)

BSGS documentation built on May 2, 2019, 4:21 a.m.