I uploaded a model which includes seasonal weather covariates for abundance. I have convergence and "Failure to calculate log density" error issues. I have then deleted some random effects and used tighter priors to solve the issue, but no success thus far. I am still working on it. One JAGS output example of a failed run is below:
out2 <- jags(dat, init, pars, paste("bkt trend power weather cov model.r", sep=""),
-
n.chains=3, n.thin=10, n.iter=60000, n.burnin=30000, parallel=TRUE)
Processing function input.......
Done.
Beginning parallel processing using 3 cores. Console output will be suppressed.
Parallel processing completed.
Calculating statistics.......
Done.
print(out2, dig=3)
JAGS output for model 'bkt trend power weather cov model.r', generated by jagsUI.
Estimates based on 3 chains of 60000 iterations,
burn-in = 30000 iterations and thin rate = 10,
yielding 9000 total samples from the joint posterior.
MCMC ran in parallel for 105.759 minutes at time 2016-04-01 10:37:34.
mean sd 2.5% 50% 97.5% overlap0 f Rhat n.eff
mu 3.975 0.614 2.666 3.808 4.992 FALSE 1.000 3.080 4
trend 0.018 0.086 -0.088 -0.018 0.157 TRUE 0.402 8.487 3
sd.site 0.828 0.057 0.706 0.828 0.944 FALSE 1.000 1.138 190
sd.year 1.018 0.620 0.225 0.881 1.979 FALSE 1.000 7.240 3
sigma 0.989 0.714 0.468 0.490 2.000 FALSE 1.000 175.590 3
p.mean 0.507 0.222 0.200 0.632 0.703 FALSE 1.000 11.769 3
p.b 2.941 4.150 -0.116 0.049 9.461 TRUE 0.823 25.051 3
mu.b[1] -0.009 0.093 -0.268 0.021 0.093 TRUE 0.333 1.838 6
mu.b[2] -0.745 0.783 -2.370 -0.268 -0.080 FALSE 0.998 5.888 3
mu.b[3] 0.262 0.302 0.013 0.078 0.906 FALSE 0.992 5.443 3
mu.b[4] 0.379 0.380 0.053 0.146 1.174 FALSE 1.000 5.752 3
mu.b[5] 0.061 0.085 -0.116 0.049 0.288 TRUE 0.899 1.331 28
mu.b[6] 0.576 0.650 0.029 0.170 1.890 FALSE 0.992 6.670 3
sigma.b[1] 0.334 0.442 0.001 0.037 1.115 FALSE 1.000 12.057 3
sigma.b[2] 0.674 0.895 0.002 0.060 1.995 FALSE 1.000 38.604 3
sigma.b[3] 0.566 0.744 0.002 0.060 1.804 FALSE 1.000 17.520 3
sigma.b[4] 0.463 0.618 0.003 0.040 1.485 FALSE 1.000 18.121 3
sigma.b[5] 0.483 0.660 0.002 0.026 1.557 FALSE 1.000 20.712 3
sigma.b[6] 0.625 0.803 0.007 0.089 1.949 FALSE 1.000 16.750 3
deviance 119126.240 127444.583 28178.612 31181.704 348014.485 FALSE 1.000 10.494 3
WARNING Rhat values indicate convergence failure.
Rhat is the potential scale reduction factor (at convergence, Rhat=1).
For each parameter, n.eff is a crude measure of effective sample size.
overlap0 checks if 0 falls in the parameter's 95% credible interval.
f is the proportion of the posterior with the same sign as the mean;
i.e., our confidence that the parameter is positive or negative.
DIC info: (pD = var(deviance)/2)
pD = 240865855 and DIC = 240984981
DIC is an estimate of expected predictive error (lower is better).
I uploaded a model which includes seasonal weather covariates for abundance. I have convergence and "Failure to calculate log density" error issues. I have then deleted some random effects and used tighter priors to solve the issue, but no success thus far. I am still working on it. One JAGS output example of a failed run is below:
Processing function input.......
Done.
Beginning parallel processing using 3 cores. Console output will be suppressed.
Parallel processing completed.
Calculating statistics.......
Done.
mu 3.975 0.614 2.666 3.808 4.992 FALSE 1.000 3.080 4
trend 0.018 0.086 -0.088 -0.018 0.157 TRUE 0.402 8.487 3
sd.site 0.828 0.057 0.706 0.828 0.944 FALSE 1.000 1.138 190
sd.year 1.018 0.620 0.225 0.881 1.979 FALSE 1.000 7.240 3
sigma 0.989 0.714 0.468 0.490 2.000 FALSE 1.000 175.590 3
p.mean 0.507 0.222 0.200 0.632 0.703 FALSE 1.000 11.769 3
p.b 2.941 4.150 -0.116 0.049 9.461 TRUE 0.823 25.051 3
mu.b[1] -0.009 0.093 -0.268 0.021 0.093 TRUE 0.333 1.838 6
mu.b[2] -0.745 0.783 -2.370 -0.268 -0.080 FALSE 0.998 5.888 3
mu.b[3] 0.262 0.302 0.013 0.078 0.906 FALSE 0.992 5.443 3
mu.b[4] 0.379 0.380 0.053 0.146 1.174 FALSE 1.000 5.752 3
mu.b[5] 0.061 0.085 -0.116 0.049 0.288 TRUE 0.899 1.331 28
mu.b[6] 0.576 0.650 0.029 0.170 1.890 FALSE 0.992 6.670 3
sigma.b[1] 0.334 0.442 0.001 0.037 1.115 FALSE 1.000 12.057 3
sigma.b[2] 0.674 0.895 0.002 0.060 1.995 FALSE 1.000 38.604 3
sigma.b[3] 0.566 0.744 0.002 0.060 1.804 FALSE 1.000 17.520 3
sigma.b[4] 0.463 0.618 0.003 0.040 1.485 FALSE 1.000 18.121 3
sigma.b[5] 0.483 0.660 0.002 0.026 1.557 FALSE 1.000 20.712 3
sigma.b[6] 0.625 0.803 0.007 0.089 1.949 FALSE 1.000 16.750 3
deviance 119126.240 127444.583 28178.612 31181.704 348014.485 FALSE 1.000 10.494 3
WARNING Rhat values indicate convergence failure.
Rhat is the potential scale reduction factor (at convergence, Rhat=1).
For each parameter, n.eff is a crude measure of effective sample size.
overlap0 checks if 0 falls in the parameter's 95% credible interval.
f is the proportion of the posterior with the same sign as the mean;
i.e., our confidence that the parameter is positive or negative.
DIC info: (pD = var(deviance)/2)
pD = 240865855 and DIC = 240984981
DIC is an estimate of expected predictive error (lower is better).