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.
http://www.perossi.org/home/bsm-1

### 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
lscoef[reg,3]=coef
}
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)