# CompWiseGibbsSMP: Stochastic matching pursuit for variable selection.

Description Usage Arguments Value Examples

### 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

 1 2 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

 1 2 3 4 ## 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) 

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