Inference for Bugs model at "../Mi model JAGS simp6.txt", fit using jags,
3 chains, each with 4e+05 iterations (first 2e+05 discarded), n.thin = 300
n.sims = 1998 iterations saved
mu.vect sd.vect 2.5% 25% 50% 75% 97.5% Rhat n.eff
Loc[1] 1.769 0.060 1.652 1.727 1.768 1.808 1.891 1.003 830
Loc[2] 1.971 0.066 1.849 1.927 1.969 2.013 2.108 1.003 670
alpha[1] 1.530 18.113 -41.568 0.462 1.865 3.145 44.206 1.002 2000
alpha[2] -0.143 0.068 -0.280 -0.188 -0.144 -0.096 -0.018 1.001 1800
alpha[3] 0.135 0.073 -0.004 0.087 0.133 0.181 0.285 1.000 2000
alpha[4] 0.187 0.045 0.101 0.156 0.185 0.216 0.278 1.004 2000
alpha[5] -0.374 0.083 -0.536 -0.427 -0.376 -0.324 -0.206 1.001 2000
alpha[6] 0.163 0.132 -0.114 0.081 0.171 0.251 0.400 1.001 2000
alpha[7] 0.328 0.102 0.083 0.272 0.338 0.396 0.497 1.001 2000
alpha[8] -0.231 0.119 -0.449 -0.313 -0.237 -0.162 0.022 1.005 470
delta -2.417 0.658 -3.597 -2.891 -2.461 -1.988 -1.015 1.001 1800
p.val[1] 0.461 0.843 0.000 0.000 0.000 0.000 2.000 1.000 2000
p.val[2] 1.969 0.247 2.000 2.000 2.000 2.000 2.000 1.006 2000
p.val[3] 0.054 0.324 0.000 0.000 0.000 0.000 2.000 1.001 2000
p.val[4] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 1
p.val[5] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.000 1
p.val[6] 0.213 0.617 0.000 0.000 0.000 0.000 2.000 1.000 2000
p.val[7] 0.012 0.155 0.000 0.000 0.000 0.000 0.000 1.030 2000
p.val[8] 1.929 0.370 0.000 2.000 2.000 2.000 2.000 1.017 800
tau 15.466 2.975 10.315 13.258 15.284 17.503 21.753 1.001 2000
deviance 7.589 6.462 -2.387 2.915 6.744 11.518 22.120 1.003 1400
For each parameter, n.eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
DIC info (using the rule, pD = var(deviance)/2)
pD = 20.9 and DIC = 28.5
DIC is an estimate of expected predictive error (lower deviance is better).