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\name{Scotch}
\alias{Scotch}
\docType{data}
\title{Survey Data on Brands of Scotch Consumed}
\description{
Data 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 21 brand variables. \cr
All variables are numeric vectors that are coded 1 if consumed in last year, 0 if not.
}
\source{Edwards, Yancy and Greg Allenby (2003), "Multivariate Analysis of Multiple Response Data," \emph{Journal of Marketing Research} 40, 321--334.}
\references{
Chapter 4, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch.}
\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(0.01, 0.05, 0.5, 0.95, 0.99))
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)
}
}
\keyword{datasets}
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