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:

[FP1]solo

Percentiles

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

P(median) 0.441

75% 0.2672179

95% 0.6101967

97.50% 0.7130077

99.50% 0.9634089

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?

Thank you

Manuel