| rmnlIndepMetrop {bayesm} | R Documentation |
rmnIndepMetrop implements Independence Metropolis for the MNL.
rmnlIndepMetrop(Data, Prior, Mcmc)
Data |
list(p,y,X) |
Prior |
list(A,betabar) optional |
Mcmc |
list(R,keep,nu) |
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:
pyXAbetabarRkeepnua 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 |
Peter Rossi, Graduate School of Business, University of Chicago, Peter.Rossi@ChicagoGsb.edu.
For further discussion, see Bayesian Statistics and Marketing
by Rossi, Allenby and McCulloch, Chapter 5.
http://faculty.chicagogsb.edu/peter.rossi/research/bsm.html
##
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)
simmnl= function(p,n,beta) {
# note: create X array with 2 alt.spec vars
k=length(beta)
X1=matrix(runif(n*p,min=-1,max=1),ncol=p)
X2=matrix(runif(n*p,min=-1,max=1),ncol=p)
X=createX(p,na=2,nd=NULL,Xd=NULL,Xa=cbind(X1,X2),base=1)
Xbeta=X%*%beta # now do probs
p=nrow(Xbeta)/n
Xbeta=matrix(Xbeta,byrow=TRUE,ncol=p)
Prob=exp(Xbeta)
iota=c(rep(1,p))
denom=Prob%*%iota
Prob=Prob/as.vector(denom)
# draw y
y=vector("double",n)
ind=1:p
for (i in 1:n)
{ yvec=rmultinom(1,1,Prob[i,]); y[i]=ind%*%yvec }
return(list(y=y,X=X,beta=beta,prob=Prob))
}
simout=simmnl(p,n,beta)
Data1=list(y=simout$y,X=simout$X,p=p); Mcmc1=list(R=R,keep=1)
out=rmnlIndepMetrop(Data=Data1,Mcmc=Mcmc1)
cat("Summary of beta draws",fill=TRUE)
summary(out$betadraw,tvalues=beta)
if(0){
## plotting examples
plot(out$betadraw)
}