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# model-020b.bug - model for means of grouped observations - sum to zero
# parameterisation corresponding R file model-010.r
model{
for (i in 1:n) {
# each value of y is normal with a mean for some reference category then a bit
# you add on for the other category
weight[i] ~ dnorm(mu[i], tau.within)
mu[i] <- alpha + nu[breed[i]]
}
for (j in 2:n.breeds) {
nu[j] ~ dnorm(0, tau.between)
}
alpha ~ dnorm(0, 1e-06)
# the trick to sum to zero paramertisation is to set one of the categories to
# minus the sum of the other categories in this case this is equivalent of
# setting to minus nu[2]
nu[1] <- -sum(nu[2:2])
# note here we sample a sd within and between then convert them to the precisions
tau.within <- var.within^-1
tau.between <- var.between^-1
sd.within ~ dunif(0, 10)
sd.between ~ dunif(0, 10)
var.within <- sd.within^2
var.between <- sd.between^2
}
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