Bayesian Methods in Health Economics

140 Bayesian Methods in Health Economics

params4 <- c("alpha","beta","sigma","y[1116]")

and running the model with the usual call to the function jags. The command

print(m3,digits=3,intervals=c(0.025, 0.975))

would now produce a table with the four monitored nodes (plus the deviance

if the option DIC=TRUE is selected), such as the following.

Inference for Bugs model at "modelNormal.txt", fit using jags,

2 chains, each with 50000 iterations (first 9500 discarded),

n.thin = 81, n.sims = 1000 iterations saved

mu.vect sd.vect 2.5% 97.5% Rhat n.eff

alpha -2335.986 165.296 -2660.064 -2015.828 1.010 410

beta 143.144 4.245 134.955 151.506 1.009 440

sigma 456.108 ...