Scotch {bayesm}

Survey Data on Brands of Scotch Consumed

Description

Data 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 21 brand variables.
All variables are numeric vectors that are coded 1 if consumed in last year, 0 if not.

Source

Edwards, Yancy and Greg Allenby (2003), "Multivariate Analysis of Multiple Response Data," Journal of Marketing Research 40, 321–334.

References

Chapter 4, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.

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(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(0.01, 0.05, 0.5, 0.95, 0.99))
  attributes(mat)$class = "bayesm.mat"
  summary(mat)
  
  rdraw = matrix(double((R)*p*p), ncol=p*p)
  rdraw = t(apply(out$sigmadraw, 1, nmat))
  attributes(rdraw)$class = "bayesm.var"
  cat(" Draws of Correlation Matrix ", fill=TRUE)
  summary(rdraw)
}