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.

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]