cheese {bayesm} | R Documentation |
Sliced Cheese Data
Description
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.
Usage
data(cheese)
Format
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 |
Source
Boatwright, Peter, Robert McCulloch, and Peter Rossi (1999), "Account-Level Modeling for Trade Promotion," Journal of the American Statistical Association 94, 1063–1073.
References
Chapter 3, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.
Examples
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)}
}
[Package bayesm version 3.1-6 Index]