model
  {
   x.bar <- mean(x[]) ;
   for(i in 1:N){
      Y[i] ~ dnorm(mu[i], tau)
      Ypred[i] ~ dnorm(mu[i], tau)              # predicted values
      aresid[i] <- abs(Y[i] - mu[i])            # actual residuals
      arespred[i] <- abs(Ypred[i] - mu[i])      # predicted residuals
      mu[i] <- alpha + beta * (x[i] - x.bar)
   }
   s <-8 
   largest <- ranked(arespred[], s)
   largthan <- step(largest - aresid[3])
   sigma <- 1/sqrt(tau)
   alpha ~ dnorm(0, 1.0E-6)
   beta ~ dnorm(0, 1.0E-6)
   tau ~ dgamma(1.0E-3, 1.0E-3)
  }

list( Y = c(1479, 1500, 516, 1815, 1118, 1702, 1348, 1337), x = c(0.45,
0.0, 0.0, 0.24, 0.12, 0.0, 0.35, 0.44),
N = 8)

list(alpha = 0, beta = 0, tau = 1)