model {
for (i in 1:6) {
for (j in 1:3) {
y[i,j] ~ dpois(mu[i,j])
log(mu[i,j]) <- log.fit[i] + lambda[i,j]
lambda[i,j] ~ dnorm(0, inv.omega.lambda.squared)
}
log.fit[i] <- alpha + beta*log(x[i] + 10)
+ gamma*x[i]
log(fit[i]) <- log.fit[i]
y.pred[i] ~ dpois(mu.pred[i])
log(mu.pred[i]) <- log.fit[i] + lambda.pred[i]
lambda.pred[i] ~ dnorm(0, inv.omega.lambda.squared)
}
alpha ~ dnorm(0, 0.0001)
beta ~ dnorm(0, 0.0001)
gamma ~ dnorm(0, 0.0001)
omega.lambda ~ dunif(0, 100)
inv.omega.lambda.squared <- 1/pow(omega.lambda, 2)
}
Data:
list(y = structure(.Data = c(15,21,29,16,18,21,16,26,33,27,41,60,33,38,41,20,27,42),
.Dim = c(6, 3)),
x = c(0, 10, 33, 100, 333, 1000))
Inits:
list(alpha = 0, beta = 0, gamma = 0, omega.lambda = 1)
Dbar = post.mean of -2logL; Dhat = -2LogL at post.mean of stochastic nodes
Dbar Dhat pD DIC
y 110.560 97.162 13.398 123.958
total 110.560 97.162 13.398 123.958
node mean sd MC error 2.5% median 97.5% start sample
alpha 2.147 0.3758 0.01461 1.384 2.152 2.881 1001 100000
beta 0.3173 0.1026 0.004063 0.1168 0.3166 0.5267 1001 100000
deviance 110.6 6.065 0.02823 100.7 109.9 124.3 1001 100000
gamma -9.96E-4 4.612E-4 1.601E-5 -0.001936 -9.912E-4 -8.523E-5 1001 100000
mu[1,1] 16.41 3.234 0.03884 10.66 16.2 23.35 1001 100000
mu[1,2] 19.78 3.632 0.03916 13.41 19.51 27.66 1001 100000
mu[1,3] 24.6 4.463 0.04394 16.97 24.22 34.39 1001 100000
mu[2,1] 18.6 3.474 0.02592 12.26 18.43 25.86 1001 100000
mu[2,2] 19.75 3.55 0.02487 13.3 19.54 27.22 1001 100000
mu[2,3] 21.55 3.738 0.02335 14.88 21.31 29.53 1001 100000
mu[3,1] 20.41 3.85 0.02228 13.34 20.25 28.4 1001 100000
mu[3,2] 26.61 4.264 0.0155 18.91 26.39 35.68 1001 100000
mu[3,3] 31.26 4.754 0.01714 22.88 30.93 41.48 1001 100000
mu[4,1] 29.48 4.665 0.03084 20.86 29.28 39.2 1001 100000
mu[4,2] 39.25 5.507 0.03255 29.41 38.91 51.0 1001 100000
mu[4,3] 53.53 7.206 0.05295 40.47 53.18 68.64 1001 100000
mu[5,1] 34.91 5.197 0.03925 25.38 34.69 45.74 1001 100000
mu[5,2] 38.45 5.453 0.03848 28.52 38.18 49.97 1001 100000
mu[5,3] 40.63 5.623 0.04008 30.4 40.34 52.48 1001 100000
mu[6,1] 23.19 4.286 0.02812 15.47 22.98 32.18 1001 100000
mu[6,2] 27.65 4.609 0.0236 19.43 27.38 37.48 1001 100000
mu[6,3] 37.93 5.828 0.03021 27.54 37.57 50.31 1001 100000
omega.lambda 0.2813 0.0836 9.769E-4 0.1438 0.2719 0.4726 1001 100000
y.pred[1] 18.81 7.861 0.1027 7.0 18.0 37.0 1001 100000
y.pred[2] 23.03 9.039 0.06764 9.0 22.0 44.0 1001 100000
y.pred[3] 28.63 10.8 0.04708 12.0 27.0 54.0 1001 100000
y.pred[4] 36.15 13.5 0.1233 15.0 34.0 67.0 1001 100000
y.pred[5] 41.3 15.5 0.1744 18.0 39.0 77.0 1001 100000
y.pred[6] 30.26 12.63 0.09479 12.0 28.0 60.0 1001 100000