margarine {bayesm} | R Documentation

## Household Panel Data on Margarine Purchases

### Description

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

### Usage

    data(margarine)

### Format

This is an R object that is a list of two data frames, list(choicePrice,demos)

List of 2
$ choicePrice:`data.frame': 4470 obs. of 12 variables:
... $ hhid : int [1:4470] 2100016 2100016 2100016 2100016
... $ choice : num [1:4470] 1 1 1 1 1 4 1 1 4 1
... $ PPk_Stk : num [1:4470] 0.66 0.63 0.29 0.62 0.5 0.58 0.29
... $ PBB_Stk : num [1:4470] 0.67 0.67 0.5 0.61 0.58 0.45 0.51
... $ PFl_Stk : num [1:4470] 1.09 0.99 0.99 0.99 0.99 0.99 0.99
... $ PHse_Stk: num [1:4470] 0.57 0.57 0.57 0.57 0.45 0.45 0.29
... $ PGen_Stk: num [1:4470] 0.36 0.36 0.36 0.36 0.33 0.33 0.33
... $ PImp_Stk: num [1:4470] 0.93 1.03 0.69 0.75 0.72 0.72 0.72
... $ PSS_Tub : num [1:4470] 0.85 0.85 0.79 0.85 0.85 0.85 0.85
... $ PPk_Tub : num [1:4470] 1.09 1.09 1.09 1.09 1.07 1.07 1.07
... $ PFl_Tub : num [1:4470] 1.19 1.19 1.19 1.19 1.19 1.19 1.19
... $ PHse_Tub: num [1:4470] 0.33 0.37 0.59 0.59 0.59 0.59 0.59

Pk is Parkay; BB is BlueBonnett, Fl is Fleischmanns, Hse is house, Gen is generic, Imp is Imperial, SS is Shed Spread. _Stk indicates stick, _Tub indicates Tub form.

$ demos :`data.frame': 516 obs. of 8 variables:
... $ hhid : num [1:516] 2100016 2100024 2100495 2100560
... $ Income : num [1:516] 32.5 17.5 37.5 17.5 87.5 12.5
... $ Fs3_4 : int [1:516] 0 1 0 0 0 0 0 0 0 0
... $ Fs5 : int [1:516] 0 0 0 0 0 0 0 0 1 0
... $ Fam_Size : int [1:516] 2 3 2 1 1 2 2 2 5 2
... $ college : int [1:516] 1 1 0 0 1 0 1 0 1 1
... $ whtcollar: int [1:516] 0 1 0 1 1 0 0 0 1 1
... $ retired : int [1:516] 1 1 1 0 0 1 0 1 0 0

Fs3_4 is dummy (family size 3-4). Fs5 is dummy for family size >= 5. college,whtcollar,retired are dummies reflecting these statuses.

### Details

choice is a multinomial indicator of one of the 10 brands (in order listed under format). All prices are in $.

### Source

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

### References

Chapter 5, Bayesian Statistics and Marketing by Rossi et al.
http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html

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

    begin=100/keep; end=R/keep
    cat(" Posterior Mean of Delta ",fill=TRUE)
    mat=apply(out$Deltadraw[begin:end,],2,mean)
    print(matrix(mat,ncol=6))
    pmom=momMix(out$probdraw[begin:end,],out$compdraw[begin:end])
    cat(" posterior expectation of mu",fill=TRUE)
    print(pmom$mu)
    cat(" posterior expectation of sd",fill=TRUE)
    print(pmom$sd)
    cat(" posterior expectation of correlations",fill=TRUE)
    print(pmom$corr)

    }

[Package bayesm version 2.0-8 Index]