| cheese {bayesm} | R Documentation |
Panel data with sales volume for a package of Borden Sliced Cheese as well as a measure of display activity and price. Weekly data aggregated to the "key" account or retailer/market level.
data(cheese)
A data frame with 5555 observations on the following 4 variables.
RETAILERVOLUMEDISPPRICEBoatwright et al (1999), "Account-Level Modeling for Trade Promotion," JASA 94, 1063-1073.
Chapter 3, Bayesian Statistics and Marketing by Rossi et al.
http://faculty.chicagogsb.edu/peter.rossi/research/bsm.html
data(cheese)
cat(" Quantiles of the Variables ",fill=TRUE)
mat=apply(as.matrix(cheese[,2:4]),2,quantile)
print(mat)
##
## example of processing for use with rhierLinearModel
##
if(0)
{
retailer=levels(cheese$RETAILER)
nreg=length(retailer)
nvar=3
regdata=NULL
for (reg in 1:nreg) {
y=log(cheese$VOLUME[cheese$RETAILER==retailer[reg]])
iota=c(rep(1,length(y)))
X=cbind(iota,cheese$DISP[cheese$RETAILER==retailer[reg]],
log(cheese$PRICE[cheese$RETAILER==retailer[reg]]))
regdata[[reg]]=list(y=y,X=X)
}
Z=matrix(c(rep(1,nreg)),ncol=1)
nz=ncol(Z)
##
## run each individual regression and store results
##
lscoef=matrix(double(nreg*nvar),ncol=nvar)
for (reg in 1:nreg) {
coef=lsfit(regdata[[reg]]$X,regdata[[reg]]$y,intercept=FALSE)$coef
if (var(regdata[[reg]]$X[,2])==0) { lscoef[reg,1]=coef[1]; lscoef[reg,3]=coef[2]}
else {lscoef[reg,]=coef }
}
R=2000
Data=list(regdata=regdata,Z=Z)
Mcmc=list(R=R,keep=1)
set.seed(66)
out=rhierLinearModel(Data=Data,Mcmc=Mcmc)
cat("Summary of Delta Draws",fill=TRUE)
summary(out$Deltadraw)
cat("Summary of Vbeta Draws",fill=TRUE)
summary(out$Vbetadraw)
if(0){
#
# plot hier coefs
plot(out$betadraw)
}
}