margarine: Household Panel Data on Margarine Purchases

In bayesm: Bayesian Inference for Marketing/Micro-Econometrics

Household Panel Data on Margarine Purchases

Description

Panel data on purchases of margarine by 516 households. Demographic variables are included.

Usage

data(margarine)

Format

The detailing object is a list containing two data frames, choicePrice and demos.

Details

In the choicePrice data frame:

...$hhid	household ID
...$choice	multinomial indicator of one of the 10 products

The products are indicated by brand and type.

Brands:

...$Pk	Parkay
...$BB	BlueBonnett
...$Fl	Fleischmanns
...$Hse	house
...$Gen	generic
...$Imp	Imperial
...$SS	Shed Spread

Product type:

...$_Stk	stick
...$_Tub	tub

In the demos data frame:

...$Fs3_4	dummy for family size 3-4
...$Fs5	dummy for family size >= 5
...$college	dummy for education status
...$whtcollar	dummy for job status
...$retired	dummy for retirement status

All prices are in U.S. dollars.

Source

Allenby, Greg and Peter Rossi (1991), "Quality Perceptions and Asymmetric Switching Between Brands," Marketing Science 10, 185–205.

References

Chapter 5, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.

Examples

data(margarine)
cat(" Table of Choice Variable ", fill=TRUE)
print(table(margarine$choicePrice[,2]))

cat(" Means of Prices", fill=TRUE)
mat=apply(as.matrix(margarine$choicePrice[,3:12]), 2, mean)
print(mat)

cat(" Quantiles of Demographic Variables", fill=TRUE)
mat=apply(as.matrix(margarine$demos[,2:8]), 2, quantile)
print(mat)


## example of processing for use with 'rhierMnlRwMixture'
if(0) {
  select = c(1:5,7)  ## select brands
  chPr = as.matrix(margarine$choicePrice)
  
  ## make sure to log prices
  chPr = cbind(chPr[,1], chPr[,2], log(chPr[,2+select]))
  demos = as.matrix(margarine$demos[,c(1,2,5)])
  
  ## remove obs for other alts
  chPr = chPr[chPr[,2] <= 7,]
  chPr = chPr[chPr[,2] != 6,]
  
  ## recode choice
  chPr[chPr[,2] == 7,2] = 6
  
  hhidl = levels(as.factor(chPr[,1]))
  lgtdata = NULL
  nlgt = length(hhidl)
  p = length(select)  ## number of choice alts
  
  ind = 1
  for (i in 1:nlgt) {
    nobs = sum(chPr[,1]==hhidl[i])
    if(nobs >=5) {
      data = chPr[chPr[,1]==hhidl[i],]
      y = data[,2]
      names(y) = NULL
      X = createX(p=p, na=1, Xa=data[,3:8], nd=NULL, Xd=NULL, INT=TRUE, base=1)
      lgtdata[[ind]] = list(y=y, X=X, hhid=hhidl[i])
      ind = ind+1
    }
  }
  nlgt = length(lgtdata)
  
  ## now extract demos corresponding to hhs in lgtdata
  Z = NULL
  nlgt = length(lgtdata)
  for(i in 1:nlgt){
     Z = rbind(Z, demos[demos[,1]==lgtdata[[i]]$hhid, 2:3])
  }
  
  ## take log of income and family size and demean
  Z = log(Z)
  Z[,1] = Z[,1] - mean(Z[,1])
  Z[,2] = Z[,2] - mean(Z[,2])
  
  keep = 5
  R = 20000
  mcmc1 = list(keep=keep, R=R)
  
  out = rhierMnlRwMixture(Data=list(p=p,lgtdata=lgtdata, Z=Z),
                          Prior=list(ncomp=1), Mcmc=mcmc1)
  
  summary(out$Deltadraw)
  summary(out$nmix)
  
  ## plotting examples
  if(0){
    plot(out$nmix)
    plot(out$Deltadraw)
  }
}

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