model { a ~ dnorm(0, 1.0E-6) # uninformative intercept term b ~ dnorm(0, 1.0E-6) # uninformative effect of PA for (i in 1:n) # for each island { logit(p[i]) <- a + b*PA[i] # logit(p) a function of PA y[i] ~ dbern(p[i]) # observed occurrence drawn from Bernoulli dist'n } for(j in 1:n.pred) { p.hat[j] <- ilogit(a + b*x[j]) } p10 <- exp(a + b*10) / (1 + exp(a + b*10)) p20 <- exp(a + b*20) / (1 + exp(a + b*20)) diff <- p10 - p20 } #end of model