# Write model
model
  
{
  
  for (i in 1:n)			# for each of the plants
  {
     y[i] ~ dnorm(mean[i], prec)	# assume normal distribution
    mean[i] <- b[1] + b[2]*x[i] + b[3]*x2[i] + b[4]*tp[i] + b[5]*x[i]*tp[i]+ b[6]*x2[i]*tp[i]
  }
  
  # uninformative priors
  
  for (i in 1:6)
  {
    b[i] ~ dnorm(0, 1.0E-6)
  }
  prec ~ dgamma(0.001, 0.001)
  
}