I have been using the following random intercept model with non-informative
prior:
model {
for (i in 1:n.samples) {
vomit[i] ~ dbern(p[i])
logit(p[i]) <- beta0 + alpha[siteid[i]]
}
for (j in 1:n.sites) {
alpha[j]~dnorm(0,tau)
}
beta0 ~ dnorm(0.0,1.0E-6)
tau ~ dgamma(0.01,0.01)
}
list(n.samples=3780,n.sites=63)How could I use a beta conjugate prior for the same model so that p(i) ~ dbeta(alpha,beta)? Thanks for your help. > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html> > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
