model senil1 { for (i in 1:n) { y[i] ~ dbern(p[i]) logit(p[i]) <- beta[1] + beta[2]*x[i] } Oddsratio <- exp(beta[2]) Prob10 <- exp(beta[1] + 10*beta[2])/(1+exp(beta[1]+ 10*beta[2])) Prob15 <- exp(beta[1] + 15*beta[2])/(1+exp(beta[1] + 15*beta[2])) for (k in 1:2) { beta[k] ~ dnorm(0,0.001) } } list(x=c(9,13,6,8,10,4,14,8,11,7,9,7,5,14,13,16,10,12,11,14,15,18,7,16,9,9,11,13,15, 13,10,11,6,17,14,19,9,11,14,10,16,10,16,14,13,13,9,15,10,11,12,4,14,20), y=c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), n=54) list(beta=c(0,0)) model senil2 { for (i in 1:n) { y[i] ~ dbern(p[i]) logit(p[i]) <- beta[1] + beta[2]*x[i] } Oddsratio <- exp(beta[2]) Prob10 ~ dbeta(3,7) Prob15 ~ dbeta(1,9) beta[2] <- (logit(Prob15) - logit(Prob10))/5 beta[1] <- logit(Prob10) - 10*( (logit(Prob15) - logit(Prob10))/5 ) } list(x=c(9,13,6,8,10,4,14,8,11,7,9,7,5,14,13,16,10,12,11,14,15,18,7,16,9,9,11,13,15, 13,10,11,6,17,14,19,9,11,14,10,16,10,16,14,13,13,9,15,10,11,12,4,14,20), y=c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), n=54) list(Prob10=0.4, Prob15=0.3)