bayesm (version 3.0-2)

Scotch: Survey Data on Brands of Scotch Consumed

Description

from Simmons Survey. Brands used in last year for those respondents who report consuming scotch.

Usage

data(Scotch)

Format

A data frame with 2218 observations on the following 21 variables. All variables are coded 1 if consumed in last year, 0 if not.

Chivas.Regal

a numeric vector

Dewar.s.White.Label

a numeric vector

Johnnie.Walker.Black.Label

a numeric vector

J...B

a numeric vector

Johnnie.Walker.Red.Label

a numeric vector

Other.Brands

a numeric vector

Glenlivet

a numeric vector

Cutty.Sark

a numeric vector

Glenfiddich

a numeric vector

Pinch..Haig.

a numeric vector

Clan.MacGregor

a numeric vector

Ballantine

a numeric vector

Macallan

a numeric vector

Passport

a numeric vector

Black...White

a numeric vector

Scoresby.Rare

a numeric vector

Grants

a numeric vector

Ushers

a numeric vector

White.Horse

a numeric vector

Knockando

a numeric vector

the.Singleton

a numeric vector

References

Chapter 4, Bayesian Statistics and Marketing by Rossi et al. http://www.perossi.org/home/bsm-1

Examples

data(Scotch)
cat(" Frequencies of Brands", fill=TRUE)
mat=apply(as.matrix(Scotch),2,mean)
print(mat)
##
## use Scotch data to run Multivariate Probit Model
##
if(0){
##

y=as.matrix(Scotch)
p=ncol(y); n=nrow(y)
dimnames(y)=NULL
y=as.vector(t(y))
y=as.integer(y)
I_p=diag(p)
X=rep(I_p,n)
X=matrix(X,nrow=p)
X=t(X)

R=2000
Data=list(p=p,X=X,y=y)
Mcmc=list(R=R)
set.seed(66)
out=rmvpGibbs(Data=Data,Mcmc=Mcmc)

ind=(0:(p-1))*p + (1:p)
cat(" Betadraws ",fill=TRUE)
mat=apply(out$betadraw/sqrt(out$sigmadraw[,ind]),2,quantile,probs=c(.01,.05,.5,.95,.99))
attributes(mat)$class="bayesm.mat"
summary(mat)
rdraw=matrix(double((R)*p*p),ncol=p*p)
rdraw=t(apply(out$sigmadraw,1,nmat))
attributes(rdraw)$class="bayesm.var"
cat(" Draws of Correlation Matrix ",fill=TRUE)
summary(rdraw)

}