CompWiseGibbsSMP: Stochastic matching pursuit for variable selection.

Description Usage Arguments Value Examples

View source: R/CompWiseGibbs_SMP.r

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

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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

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## 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 29, 2017, 5:46 p.m.

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