rmnlIndepMetrop {bayesm}R Documentation

MCMC Algorithm for Multinomial Logit Model

Description

rmnIndepMetrop implements Independence Metropolis for the MNL.

Usage

rmnlIndepMetrop(Data, Prior, Mcmc)

Arguments

Data list(p,y,X)
Prior list(A,betabar) optional
Mcmc list(R,keep,nu)

Details

Model: y ~ MNL(X,beta). Pr(y=j) = exp(x_j'beta)/sum_k{e^{x_k'beta}}.

Prior: beta ~ N(betabar,A^{-1})

list arguments contain:

Value

a list containing:

betadraw R/keep x nvar array of beta draws
loglike R/keep vector of loglike values for each draw
acceptr acceptance rate of Metropolis draws

Author(s)

Peter Rossi, Graduate School of Business, University of Chicago, Peter.Rossi@ChicagoGsb.edu.

References

For further discussion, see Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch, Chapter 5.
http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html

See Also

rhierMnlRwMixture

Examples

##

if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10}

set.seed(66)
n=200; p=3; beta=c(1,-1,1.5,.5)
simout=simmnl(p,n,beta)
A=diag(c(rep(.01,length(beta)))); betabar=rep(0,length(beta))

Data=list(y=simout$y,X=simout$X,p=p); Mcmc=list(R=R,keep=1) ; Prior=list(A=A,betabar=betabar)
out=rmnlIndepMetrop(Data=Data,Prior=Prior,Mcmc=Mcmc)
cat(" Betadraws ",fill=TRUE)
mat=apply(out$betadraw,2,quantile,probs=c(.01,.05,.5,.95,.99))
mat=rbind(beta,mat); rownames(mat)[1]="beta"; print(mat)


[Package bayesm version 2.0-8 Index]