CompWiseGibbsSMP: Stochastic matching pursuit for variable selection.

In BSGS: Bayesian Sparse Group 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, 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	Given value in the prior distribution of σ^2.
lambda	Given value 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: 
CompWiseGibbsSMP(Y, X, beta.value, r, tau2, rho, sigma2, nu0, lambda0, 
num.of.inner.iter, num.of.iteration, MCSE.Sigma2.Given)
## End(Not run)

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