GAM model smoothing indices jagam.txt

## model used to smooth series of annual indices, that have an estimated precision

model {

  for (i in 1:ncounts) { 
	lindex[i] ~ dnorm(mu[i],preci[i]) # response
	mu[i] ~ dnorm(x.gam[i],tau) #expected response
	
} ## expected response

	sd <- 1/pow(tau,0.5)
  scale <- 1/tau ## convert tau to standard GLM scale
  tau ~ dgamma(.05,.005) ## precision parameter prior 
  ## Parametric effect priors CHECK tau=1/10^2 is appropriate!
 

  x.gam <- X %*% b ## expected response

 for (i in 1:1) { b[i] ~ dnorm(0,0.01) }
  ## prior for s(year)... 
  K1 <- S1[1:(nknots-1),1:(nknots-1)] * lambda[1]  + S1[1:(nknots-1),(nknots):((nknots*2)-2)] * lambda[2]
  b[2:(nknots)] ~ dmnorm(zero[2:(nknots)],K1) 
  ## smoothing parameter priors CHECK...
  for (i in 1:2) {
    lambda[i] ~ dgamma(.05,.005)
    rho[i] <- log(lambda[i])
  }





### derived parameters
ind.pred <- X.pred %*% b #full predicted values

}