Scotch {bayesm} | R Documentation

## Survey Data on Brands of Scotch Consumed

### Description

from Simmons Survey. Brands used in last year for those respondents who report consuming scotch.

### Usage

    data(Scotch)

### Format

A data frame with 2218 observations on the following 21 variables. All variables are coded 1 if consumed in last year, 0 if not.

`Chivas.Regal`
  a numeric vector
`Dewar.s.White.Label`
  a numeric vector
`Johnnie.Walker.Black.Label`
  a numeric vector
`J...B`
  a numeric vector
`Johnnie.Walker.Red.Label`
  a numeric vector
`Other.Brands`
  a numeric vector
`Glenlivet`
  a numeric vector
`Cutty.Sark`
  a numeric vector
`Glenfiddich`
  a numeric vector
`Pinch..Haig.`
  a numeric vector
`Clan.MacGregor`
  a numeric vector
`Ballantine`
  a numeric vector
`Macallan`
  a numeric vector
`Passport`
  a numeric vector
`Black...White`
  a numeric vector
`Scoresby.Rare`
  a numeric vector
`Grants`
  a numeric vector
`Ushers`
  a numeric vector
`White.Horse`
  a numeric vector
`Knockando`
  a numeric vector
`the.Singleton`
  a numeric vector

### Source

Edwards, Y. and G. Allenby (2003), "Multivariate Analysis of Multiple Response Data," JMR 40, 321-334.

### References

Chapter 4, Bayesian Statistics and Marketing by Rossi et al.
http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html

### Examples

    data(Scotch)
    cat(" Frequencies of Brands", fill=TRUE)
    mat=apply(as.matrix(Scotch),2,mean)
    print(mat)
    ##
    ## use Scotch data to run Multivariate Probit Model
    ##
    if(nchar(Sys.getenv("LONG_TEST")) != 0){
    ##

    y=as.matrix(Scotch)
    p=ncol(y); n=nrow(y)
    dimnames(y)=NULL
    y=as.vector(t(y))
    y=as.integer(y)
    I_p=diag(p)
    X=rep(I_p,n)
    X=matrix(X,nrow=p)
    X=t(X)

    R=2000
    Data=list(p=p,X=X,y=y)
    Mcmc=list(R=R)
    set.seed(66)
    out=rmvpGibbs(Data=Data,Mcmc=Mcmc)

    ind=(0:(p-1))*p + (1:p)
    cat(" Betadraws ",fill=TRUE)
    mat=apply(out$betadraw/sqrt(out$sigmadraw[,ind]),2,quantile,probs=c(.01,.05,.5,.95,.99))
    print(mat)
    rdraw=matrix(double((R)*p*p),ncol=p*p)
    rdraw=t(apply(out$sigmadraw,1,nmat))
    cat(" Draws of Correlation Matrix ",fill=TRUE)
    mat=apply(rdraw,2,quantile,probs=c(.01,.05,.5,.95,.99))
    ## correlation matrix too large to print -- summarize
    quantile(round(mat,digits=2))

    }

[Package bayesm version 2.0-8 Index]