bayesm (version 3.0-2)

runiregGibbs: Gibbs Sampler for Univariate Regression

Description

runiregGibbs implements a Gibbs Sampler to draw from posterior of a univariate regression with a conditionally conjugate prior.

Usage

runiregGibbs(Data, Prior, Mcmc)

Arguments

Data

list(y,X)

Prior

list(betabar,A, nu, ssq)

Mcmc

list(sigmasq,R,keep,nprint)

Value

list of MCMC draws

betadraw

R x k array of betadraws

sigmasqdraw

R vector of sigma-sq draws

Details

Model: \(y = X\beta + e\). \(e\) \(\sim\) \(N(0,\sigma^2)\).

Priors: \(\beta\) \(\sim\) \(N(betabar,A^{-1})\). \(\sigma^2\) \(\sim\) \((nu*ssq)/\chi^2_{nu}\). List arguments contain

• Xn x k Design Matrix

• yn x 1 vector of observations

• betabark x 1 prior mean (def: 0)

• Ak x k prior precision matrix (def: .01I)

• nu d.f. parm for Inverted Chi-square prior (def: 3)

• ssq scale parm for Inverted Chi-square prior (def:var(y))

• R number of MCMC draws

• keep thinning parameter - keep every keepth draw

• nprint print the estimated time remaining for every nprint'th draw (def: 100)

References

For further discussion, see Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch, Chapter 3. http://www.perossi.org/home/bsm-1

See Also

runireg

Examples

if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=10}
set.seed(66)
n=100
X=cbind(rep(1,n),runif(n)); beta=c(1,2); sigsq=.25
y=X%*%beta+rnorm(n,sd=sqrt(sigsq))

Data1=list(y=y,X=X); Mcmc1=list(R=R) 

out=runiregGibbs(Data=Data1,Mcmc=Mcmc1)

cat("Summary of beta and Sigma draws",fill=TRUE)
summary(out$betadraw,tvalues=beta)
summary(out$sigmasqdraw,tvalues=sigsq)

if(0){
## plotting examples
plot(out$betadraw)
}