bayesm (version 3.0-2)

bank: Bank Card Conjoint Data of Allenby and Ginter (1995)

Description

Data from a conjoint experiment in which two partial profiles of credit cards were presented to 946 respondents. The variable bank$choiceAtt$choice indicates which profile was chosen. The profiles are coded as the difference in attribute levels. Thus, a "-1" means the profile coded as a choice of "0" has the attribute. A value of 0 means that the attribute was not present in the comparison.

data on age,income and gender (female=1) are also recorded in bank$demo

Usage

data(bank)

Format

This R object is a list of two data frames, list(choiceAtt,demo).

List of 2

$ choiceAtt:`data.frame': 14799 obs. of 16 variables: …$ id : int [1:14799] 1 1 1 1 1 1 1 1 1 1 …$ choice : int [1:14799] 1 1 1 1 1 1 1 1 0 1 …$ Med_FInt : int [1:14799] 1 1 1 0 0 0 0 0 0 0 …$ Low_FInt : int [1:14799] 0 0 0 0 0 0 0 0 0 0 …$ Med_VInt : int [1:14799] 0 0 0 0 0 0 0 0 0 0 …$ Rewrd_2 : int [1:14799] -1 1 0 0 0 0 0 1 -1 0 …$ Rewrd_3 : int [1:14799] 0 -1 1 0 0 0 0 0 1 -1 …$ Rewrd_4 : int [1:14799] 0 0 -1 0 0 0 0 0 0 1 …$ Med_Fee : int [1:14799] 0 0 0 1 1 -1 -1 0 0 0 …$ Low_Fee : int [1:14799] 0 0 0 0 0 1 1 0 0 0 …$ Bank_B : int [1:14799] 0 0 0 -1 1 -1 1 0 0 0 …$ Out_State : int [1:14799] 0 0 0 0 -1 0 -1 0 0 0 …$ Med_Rebate : int [1:14799] 0 0 0 0 0 0 0 0 0 0 …$ High_Rebate : int [1:14799] 0 0 0 0 0 0 0 0 0 0 …$ High_CredLine: int [1:14799] 0 0 0 0 0 0 0 -1 -1 -1 …$ Long_Grace : int [1:14799] 0 0 0 0 0 0 0 0 0 0

$ demo :`data.frame': 946 obs. of 4 variables: …$ id : int [1:946] 1 2 3 4 6 7 8 9 10 11 …$ age : int [1:946] 60 40 75 40 30 30 50 50 50 40 …$ income: int [1:946] 20 40 30 40 30 60 50 100 50 40 …$ gender: int [1:946] 1 1 0 0 0 0 1 0 0 0

Details

Each respondent was presented with between 13 and 17 paired comparisons. Thus, this dataset has a panel structure.

References

Appendix A, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch. http://www.perossi.org/home/bsm-1

Examples

data(bank)
cat(" table of Binary Dep Var", fill=TRUE)
print(table(bank$choiceAtt[,2]))
cat(" table of Attribute Variables",fill=TRUE)
mat=apply(as.matrix(bank$choiceAtt[,3:16]),2,table)
print(mat)
cat(" means of Demographic Variables",fill=TRUE)
mat=apply(as.matrix(bank$demo[,2:3]),2,mean)
print(mat)

## example of processing for use with rhierBinLogit
##
if(0)
{
choiceAtt=bank$choiceAtt
Z=bank$demo

## center demo data so that mean of random-effects
## distribution can be interpreted as the average respondent

Z[,1]=rep(1,nrow(Z))
Z[,2]=Z[,2]-mean(Z[,2])
Z[,3]=Z[,3]-mean(Z[,3])
Z[,4]=Z[,4]-mean(Z[,4])
Z=as.matrix(Z)

hh=levels(factor(choiceAtt$id))
nhh=length(hh)
lgtdata=NULL
for (i in 1:nhh) {
	y=choiceAtt[choiceAtt[,1]==hh[i],2]
	nobs=length(y)
	X=as.matrix(choiceAtt[choiceAtt[,1]==hh[i],c(3:16)])
	lgtdata[[i]]=list(y=y,X=X)
		}

cat("Finished Reading data",fill=TRUE)
fsh()

Data=list(lgtdata=lgtdata,Z=Z)
Mcmc=list(R=10000,sbeta=0.2,keep=20)
set.seed(66)
out=rhierBinLogit(Data=Data,Mcmc=Mcmc)

begin=5000/20
end=10000/20

summary(out$Deltadraw,burnin=begin)
summary(out$Vbetadraw,burnin=begin)

if(0){
## plotting examples

## plot grand means of random effects distribution (first row of Delta)
index=4*c(0:13)+1
matplot(out$Deltadraw[,index],type="l",xlab="Iterations/20",ylab="",
main="Average Respondent Part-Worths")

## plot hierarchical coefs
plot(out$betadraw)

## plot log-likelihood
plot(out$llike,type="l",xlab="Iterations/20",ylab="",main="Log Likelihood")

}
}