model {
for(i in 1:N){
Y[i] ~ dnorm(mu,tau)
Y.rep[i] ~ dnorm(mu,tau)
}
mu ~ dunif(-100, 100)
tau ~ dgamma(0.001, 0.001)
N.50 <- round(N/2)
N.25 <- round(N/4)
Y.rep.min <- ranked(Y.rep[], 1)
Y.rep.50 <- ranked(Y.rep[], N.50)
Y.rep.25 <- ranked(Y.rep[], N.25)
T.rep <- (Y.rep.min - Y.rep.50)/(Y.rep.25 - Y.rep.50)
P.T <- step(T.rep - T.obs)
V.obs <- sd(Y[])*sd(Y[])
V.rep <- sd(Y.rep[])*sd(Y.rep[])
P.V <- step(V.rep - V.obs)
}
Inits:
list(mu = 0, tau = 1)
Data:
list(N=66, T.obs = 23.7, Y=c(
28, 26, 33, 24, 34, -44, 27, 16, 40, -2,
29, 22, 24, 21, 25, 30, 23, 29, 31, 19,
24, 20, 36, 32, 36, 28, 25, 21, 28, 29,
37, 25, 28, 26, 30, 32, 36, 26, 30, 22,
36, 23, 27, 27, 28, 27, 31, 27, 26, 33,
26, 32, 32, 24, 39, 28, 24, 25, 32, 25,
29, 27, 28, 29, 16, 23))
node mean sd MC error 2.5% median 97.5% start sample
P.T 0.0 0.0 1.0E-12 0.0 0.0 0.0 1001 10000
P.V 0.4911 0.4999 0.005363 0.0 0.0 1.0 1001 10000
T.rep 3.743 1.061 0.01047 2.237 3.563 6.292 1001 10000
V.rep 118.9 30.09 0.3198 70.5 114.9 188.1 1001 10000
mu 26.22 1.342 0.01334 23.57 26.22 28.88 1001 10000
tau 0.008665 0.001524 1.616E-5 0.005922 0.008604 0.01189 1001 10000