Full.model<-function(){
###------------------------------------------------------------------------------------------------
	##Functional form (and timeseries/state-observation relationships)
###------------------------------------------------------------------------------------------------
	for(treenum in 1:n.trees) {

		for(yr in (start.year[treenum]+1):end.year[treenum]){

			z[treenum,yr] ~ dbern(P[treenum,yr])

			# dependency on the previous state
			P[treenum,yr] <- z[treenum,yr-1]*Psurv[treenum,yr]

			# link function, linear predictors
			logit(Psurv[treenum,yr])<- Intercept[treenum]+i.ba*ba[plotnum[treenum],yr]
+s.overall*size[treenum,yr] 
			}

		}



###------------------------------------------------------------##
	##Random effects
###----------------------------------------------------------------##
	for(treenum in 1:n.trees) {
			Intercept[treenum] <- i.overall+i.plot[plotnum[treenum]]
	}
	#Plot random effects
	for (j in 1:n.plots) {
		i.plot[j] ~dnorm(0,i.plot.prec)
	}


###----------------------------##
	##Priors
###----------------------------##
	#precisions 
	i.plot.prec<- pow(i.plot.sd,-2)
	i.plot.sd ~ dunif(0, 100)

	#means (overall intercept, slopes for covariates)
	i.overall<-logit(li.overall)
	li.overall~dunif(0, 1)
	i.ba ~ dnorm(0, 1.0E-6)
	s.overall ~ dnorm(0, 1.0E-6)

}