CompWiseGibbsSimple {BSGS}R Documentation

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, 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

The hyperparameter in the prior distribution of σ^2.

lambda

The hyperparameter 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: 
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)


[Package BSGS version 2.0 Index]