BSGS (version 2.0)

CompWiseGibbsSMP: Stochastic matching pursuit for variable selection.

Description

Perform MCMC procedure to generate the posterior samples to estimate posterior quantities of interest in Bayesian variable selection using stochastic matching pursuit approach (SMP).

Usage

CompWiseGibbsSMP(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

Given value in the prior distribution of $\sigma^2$.

lambda

Given value 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: 
# CompWiseGibbsSMP(Y, X, beta.value, r, tau2, rho, sigma2, nu0, lambda0, 
# num.of.inner.iter, num.of.iteration, MCSE.Sigma2.Given)## End(Not run)