data(tuna)
cat(" Quantiles of sales", fill=TRUE)
mat = apply(as.matrix(tuna[,2:5]), 2, quantile)
print(mat)
## example of processing for use with rivGibbs
if(0) {
data(tuna)
t = dim(tuna)[1]
customers = tuna[,30]
sales = tuna[,2:8]
lnprice = tuna[,16:22]
lnwhPrice = tuna[,23:29]
share = sales/mean(customers)
shareout = as.vector(1-rowSums(share))
lnprob = log(share/shareout)
## create w matrix
I1 = as.matrix(rep(1,t))
I0 = as.matrix(rep(0,t))
intercept = rep(I1,4)
brand1 = rbind(I1, I0, I0, I0)
brand2 = rbind(I0, I1, I0, I0)
brand3 = rbind(I0, I0, I1, I0)
w = cbind(intercept, brand1, brand2, brand3)
## choose brand 1 to 4
y = as.vector(as.matrix(lnprob[,1:4]))
X = as.vector(as.matrix(lnprice[,1:4]))
lnwhPrice = as.vector(as.matrix(lnwhPrice[1:4]))
z = cbind(w, lnwhPrice)
Data = list(z=z, w=w, x=X, y=y)
Mcmc = list(R=R, keep=1)
set.seed(66)
out = rivGibbs(Data=Data, Mcmc=Mcmc)
cat(" betadraws ", fill=TRUE)
summary(out$betadraw)
## plotting examples
if(0){plot(out$betadraw)}
}
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