BSGS (version 2.0)

CompWiseGibbsSimple: Generate the posterior samples from the posterior distribution using the component-wise Gibbs sampler (CWGS).

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, $\beta$.

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 $\sigma^2$.

nu

The hyperparameter in the prior distribution of $\sigma^2$.

lambda

The hyperparameter in the prior distribution of $\sigma^2$.

num.of.inner.iter

The number of iterations before sampling $\sigma^2$.

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 $\sigma^2$ less then given values.

Value

$\beta$, variance $\sigma^2$, binary variables, $\gamma$, 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)