model
{
for(i in 1 : N) {
Y[i] ~ dnorm(mu[i], tau)
mu[i] <- alpha + beta[J[i]] * (x[i] - x.change)
J[i] <- 1 + step(x[i] - x.change)
}
tau ~ dgamma(0.001, 0.001)
alpha ~ dnorm(0.0,1.0E-6)
for(j in 1 : 2) {
beta[j] ~ dnorm(0.0,1.0E-6)
}
sigma <- 1 / sqrt(tau)
x.change ~ dunif(-1.3,1.1)
}