Continuous prior for r...
model {
for (i in 1:6) {
for (j in 1:3) {
y[i,j] ~ dnegbin(p[i], r)
}
p[i] <- r/(mu[i] + r)
log(mu[i]) <- alpha + beta*log(x[i] + 10) + gamma*x[i]
}
r ~ dunif(1, max)
alpha ~ dnorm(0, 0.0001)
beta ~ dnorm(0, 0.0001)
gamma ~ dnorm(0, 0.0001)
}
Data:
list(max = 1000,
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, r = 10)
Dbar = post.mean of -2logL; Dhat = -2LogL at post.mean of stochastic nodes
Dbar Dhat pD DIC
y 130.739 129.179 1.560 132.298
total 130.739 129.179 1.560 132.298
node mean sd MC error 2.5% median 97.5% start sample
alpha 2.167 0.3164 0.0134 1.553 2.17 2.798 5001 100000
beta 0.3209 0.08478 0.003641 0.1508 0.3202 0.4867 5001 100000
gamma -0.001011 3.678E-4 1.361E-5 -0.001739 -0.001011 -2.721E-4 5001 100000
r 72.95 147.0 1.387 7.78 26.58 630.6 5001 100000
Continuous prior for log(r)...
model {
for (i in 1:6) {
for (j in 1:3) {
y[i,j] ~ dnegbin(p[i], r)
}
p[i] <- r/(mu[i] + r)
log(mu[i]) <- alpha + beta*log(x[i] + 10) + gamma*x[i]
}
logr.cont ~ dunif(0, 10)
log(r.cont) <- logr.cont
r <- round(r.cont)
alpha ~ dnorm(0, 0.0001)
beta ~ dnorm(0, 0.0001)
gamma ~ dnorm(0, 0.0001)
}
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, logr.cont = 1)
Dbar = post.mean of -2logL; Dhat = -2LogL at post.mean of stochastic nodes
Dbar Dhat pD DIC
y 130.224 125.896 4.328 134.553
total 130.224 125.896 4.328 134.553
node mean sd MC error 2.5% median 97.5% start sample
alpha 2.198 0.3636 0.01521 1.48 2.206 2.91 5001 100000
beta 0.3127 0.09866 0.004201 0.122 0.3107 0.5126 5001 100000
gamma -9.745E-4 4.334E-4 1.612E-5 -0.001841 -9.703E-4 -1.174E-4 5001 100000
logr.cont 2.857 0.7325 0.007033 1.693 2.793 4.353 5001 100000
r 43.9 489.4 2.045 5.0 16.0 78.0 5001 100000
r.cont 43.89 489.4 2.045 5.437 16.32 77.69 5001 100000