tuna {bayesm} | R Documentation |
Volume of canned tuna sales as well as a measure of display activity, log price and log wholesale price. Weekly data aggregated to the chain level. This data is extracted from the Dominick's Finer Foods database maintained by the University of Chicago http://http://research.chicagogsb.edu/marketing/databases/dominicks/dataset.aspx. Brands are seven of the top 10 UPCs in the canned tuna product category.
data(tuna)
A data frame with 338 observations on the following 30 variables.
WEEK
MOVE1
MOVE2
MOVE3
MOVE4
MOVE5
MOVE6
MOVE7
NSALE1
NSALE2
NSALE3
NSALE4
NSALE5
NSALE6
NSALE7
LPRICE1
LPRICE2
LPRICE3
LPRICE4
LPRICE5
LPRICE6
LPRICE7
LWHPRIC1
LWHPRIC2
LWHPRIC3
LWHPRIC4
LWHPRIC5
LWHPRIC6
LWHPRIC7
FULLCUST
Chevalier, A. Judith, Anil K. Kashyap and Peter E. Rossi (2003), "Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data," The American Economic Review , 93(1), 15-37.
Chapter 7, Bayesian Statistics and Marketing by Rossi et al.
http://faculty.chicagogsb.edu/peter.rossi/research/bsm.html
data(tuna) cat(" Quantiles of sales",fill=TRUE) mat=apply(as.matrix(tuna[,2:5]),2,quantile) print(mat) ## ## example of processing for use with rivGibbs ## if(0) { data(tuna) t = dim(tuna)[1] customers = tuna[,30] sales = tuna[,2:8] lnprice = tuna[,16:22] lnwhPrice= tuna[,23:29] share=sales/mean(customers) shareout=as.vector(1-rowSums(share)) lnprob=log(share/shareout) # create w matrix I1=as.matrix(rep(1, t)) I0=as.matrix(rep(0, t)) intercept=rep(I1, 4) brand1=rbind(I1, I0, I0, I0) brand2=rbind(I0, I1, I0, I0) brand3=rbind(I0, I0, I1, I0) w=cbind(intercept, brand1, brand2, brand3) ## choose brand 1 to 4 y=as.vector(as.matrix(lnprob[,1:4])) X=as.vector(as.matrix(lnprice[,1:4])) lnwhPrice=as.vector(as.matrix (lnwhPrice[1:4])) z=cbind(w, lnwhPrice) Data=list(z=z, w=w, x=X, y=y) Mcmc=list(R=R, keep=1) set.seed(66) out=rivGibbs(Data=Data,Mcmc=Mcmc) cat(" betadraws ",fill=TRUE) summary(out$betadraw) if(0){ ## plotting examples plot(out$betadraw) } }