I am running the following model with MCMC for a binary outcome:
runmlwin dipstick_available_for_use cons private solo centered_distance_lab99 centered_pc_turna_time3, level1(id:) discrete(distribution(binomial) link(logit) denom(cons) pql2) nopause
(This is a first model with a single level, but I will follow on with a second model in which I will add a second level: country , to then compare the models and asses the contribution of country to the outcome.)runmlwin dipstick_available_for_use cons private solo centered_distance_lab99 centered_pc_turna_time3, level1(id:) discrete(distribution(binomial) link(logit) denom(cons) pql2) mcmc(burnin(1000) chain(10300000)) initsprevious nopause nogroup
I understand that the trajectories and five ways graphs look all “healthy” ( attached), but in terms of Raftery-Lewis and Brooks-Draper diagnostics I get the following for the parameter [FP1] solo:
Mean 0.0635883 0.50% -0.7214065 Thinned Chain Length 10000
MCSE of Mean 0.0081857 2.50% -0.5319016 Effective Sample Size 1540
Std. Dev. 0.320784 5% -0.4458137 Raftery Lewis (2.5%) 15424
Mode 0.042611 25% -0.1491841 Raftery Lewis (97.5%) 16195
P(mean) 0.441 Brooks Draper (mean) 1.03E+07
P(mode) 0.441 50% 0.0536541
I want to report the estimate with 2 significant figures. I understand that then I need to rerun the model with 1.03e+07 iterations, which I did and produce slightly different results but took much much longer.
How can I know the number of iterations needed when I get such extreme Brooks-Draper diagnostics?
Do I need to use thining:
If I do that I understand that the parameter means and standard deviations will then be based on all iterations, while the ESS's and 95% credible intervals will be based on the stored iterations depending on the specified thinning.mcmc(burnin(1000) chain(10300000) thinning())
But how many thinning figures do you advise? 50, 500, 5000?
Or do I need to check diagnostics only for the next model, ie the multilevel model?