bank {bayesm} | R Documentation

## 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.

### Source

Allenby and Ginter (1995), "Using Extremes to Design Products and Segment Markets," JMR, 392-403.

### References

Appendix A, Bayesian Statistics and Marketing by Rossi,Allenby and McCulloch.
http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html

### 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(nchar(Sys.getenv("LONG_TEST")) != 0)
    {
    choiceAtt=bank$choiceAtt
    Z=bank$demo

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

    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)

    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)
    }

[Package bayesm version 2.0-8 Index]