Sliced Cheese Data
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:
...$RETAILER |
a list of 88 retailers |
...$VOLUME |
unit sales |
...$DISP |
percent ACV on display (a measure of advertising display activity) |
...$PRICE |
in U.S. dollars |
Boatwright, Peter, Robert McCulloch, and Peter Rossi (1999), "Account-Level Modeling for Trade Promotion," Journal of the American Statistical Association 94, 1063–1073.
Chapter 3, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.
http://www.perossi.org/home/bsm-1
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$Vbetadraw)
# plot hier coefs
if(0) {plot(out$betadraw)}
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