model {
for (t in 1:n) {
y[t] <- log(cpue[t])
y[t] ~ dnorm(mu[t], inv.sigma.squared)
yr[t] <- t + 1966
mu[t] <- log(q*K*P[t])
log(P[t]) <- LP[t]
LP[t] ~ dnorm(f[t], inv.omega.squared)
}
f[1] <- 0
for (t in 2:n) {
f[t] <- log(P[t-1] + r*P[t-1]*(1 - P[t-1])
- C[t-1]/K)
}
q <- 1/inv.q
log(K) <- log.K
log(r) <- log.r
sigma <- 1/sqrt(inv.sigma.squared)
omega <- 1/sqrt(inv.omega.squared)
inv.q ~ dgamma(0.001, 0.001)I(0.5, 100)
log.K ~ dnorm(5.043, 3.760)I(2.303, 6.908)
log.r ~ dnorm(-1.380, 3.845)I(-4.605, 0.1823)
inv.sigma.squared ~ dgamma(1.709, 0.008614)
inv.omega.squared ~ dgamma(3.786, 0.01022)
}
Inits:
list(log.r = -0.223, log.K = 5.298, inv.q = 5,
inv.sigma.squared = 100, inv.omega.squared = 100)
Data:
list(n = 23,
cpue = c(61.89,78.98,55.59,44.61,56.89,38.27,33.84,36.13,41.95,36.63,36.33,38.82,34.32,37.64,34.01,32.16,26.88,36.61,30.07,30.75,23.36,22.36,21.91),
C = c(15.9,25.7,28.5,23.7,25,33.3,28.2,19.7,17.5,19.3,21.6,23.1,22.5,22.5,23.6,29.1,14.4,13.2,28.4,34.6,37.5,25.9,25.3))
node mean sd MC error 2.5% median 97.5% start sample
K 275.5 60.24 2.227 183.6 266.1 418.8 4001 100000
omega 0.05383 0.01472 2.334E-4 0.03371 0.05106 0.09079 4001 100000
q 0.2425 0.05435 0.002133 0.1475 0.239 0.3611 4001 100000
r 0.2976 0.08411 0.00295 0.1483 0.2932 0.4757 4001 100000
sigma 0.1086 0.01939 1.755E-4 0.07575 0.1067 0.152 4001 100000