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