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)
   }