cheese: Sliced Cheese Data

In bayesm: Bayesian Inference for Marketing/Micro-Econometrics

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)}
}

bayesm documentation built on Sept. 24, 2023, 1:07 a.m.