bayesm (version 2.0-9)

cheese: 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)

source

Boatwright et al (1999), "Account-Level Modeling for Trade Promotion," JASA 94, 1063-1073.

References

Chapter 3, Bayesian Statistics and Marketing by Rossi et al. http://faculty.chicagogsb.edu/peter.rossi/research/bsm.html

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(nchar(Sys.getenv("LONG_TEST")) != 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)

betamean=array(double(nreg*nvar),dim=c(nreg,nvar))
burnin=100

set.seed(66)
out=rhierLinearModel(Data=Data,Mcmc=Mcmc)

for (k in 1:nvar) { betamean[,k]=apply(out$betadraw[,k,burnin:R],1,mean)}
print(betamean)
cat("Deltadraws ",fill=TRUE)
mat=apply(out$Deltadraw,2,quantile,probs=c(.01,.05,.5,.95,.99))
print(mat)
cat("Vbetadraws ",fill=TRUE)
mat=apply(out$Vbetadraw,2,quantile,probs=c(.01,.05,.5,.95,.99))
print(mat)

if(0){
coefn=c("Intercept","Display","LnPrice")
colors=c("blue","green","red","yellow")
par(mfrow=c(nvar,1),mar=c(5.1,15,4.1,13))
for (n in 1:nvar) 
{
   plot(range(betamean[,n]),range(betamean[,n]),
     type="n",main=coefn[n],xlab="ls coef",ylab="post mean")
        points(lscoef[,n],betamean[,n],pch=17,col="blue",cex=1.2) 
   abline(c(0,1))
}
}

}