model {
for (i in 1:N) {
y[i] ~ dnorm(mu[i], tau)
mu[i] <- alpha + beta[1]*x[i] + beta[2]*(x[i] - theta)
* step(x[i] - theta)
}
tau ~ dgamma(0.001, 0.001)
alpha ~ dnorm(0.0, 1.0E-6)
for (j in 1:2) {
beta[j] ~ dnorm(0.0, 1.0E-6)
}
sigma <- 1/sqrt(tau)
theta ~ dunif(-1.3, 1.1)
}
   
Data:
list(y = c(1.12, 1.12, 0.99, 1.03, 0.92, 0.90, 0.81, 0.83, 0.65, 0.67, 0.60, 0.59, 0.51, 0.44, 0.43, 0.43, 0.33, 0.30, 0.25, 0.24, 0.13, -0.01, -0.13, -0.14, -0.30, -0.33, -0.46, -0.43, -0.65),
x = c(-1.39, -1.39, -1.08, -1.08, -0.94, -0.80, -0.63, -0.63, -0.25, -0.25, -0.12, -0.12, 0.01, 0.11, 0.11, 0.11, 0.25, 0.25, 0.34, 0.34, 0.44, 0.59, 0.70, 0.70, 0.85, 0.85, 0.99, 0.99, 1.19),
N = 29)

Inits:
list(alpha = 0.2, beta = c(-0.45, 0), tau = 5, theta = 0)


   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   alpha   0.5482   0.01337   4.934E-4   0.5228   0.5475   0.5756   501   10000
   beta[1]   -0.4187   0.01555   5.097E-4   -0.4485   -0.4193   -0.3869   501   10000
   beta[2]   -0.5944   0.0212   2.248E-4   -0.6362   -0.5942   -0.5528   501   10000
   sigma   0.02218   0.003352   5.138E-5   0.01673   0.02178   0.02995   501   10000
   theta   0.02563   0.03378   0.001399   -0.04016   0.02783   0.08707   501   10000
   
[example-11_7_1-stagnant0]