I’m not being able to run some Bayesian models with the brms package and I would be grateful if you could help me. I’m new in Bayesian modeling. The problem is the following:
I have the attached dataset ‘dd.csv’ with the following seven variables:dd.csv (4.5 KB)
- Subj: Participant ID
- y: independent variable; count data
- Group: a categorical variable with 9 levels (18 subjects in each level)
- x1,…,x4: four dependent variables; count data
For each independent variable xi, I would like to assess the effect of the interaction “xi * Group” on y, for i=1,…4. Since y consists of counts, I am using the Conway-Maxwell-Poisson family. In the frequentist approach, I use the following models (which run ok):
mf <- glmmTMB(y ~ x1*Group + (1|Subj), data =dd, family=compois)
In the Bayesian framework, I would like to perform the following equivalent models
mb1 <- brm(y ~ 0+ x1*Group + (1|Subj), data=dd, family = brmsfamily(“com_poisson”), save_all_pars = TRUE, sample_prior=TRUE);
mb2,mb3,mb4 defined analogously.
However, all these mbi models run very, very slowly and do not converge!
Before these models, I ran similar brms models involving only the variable Group (not xi) and everything worked perfectly, including further analysis conducted with the hypothesis method produced very similar results and significance, compared to the frequentist results.
If I change to brmsfamily(“negbinomial”), the models mbi run o.k.; however, the significances of the hypothesis results do not agree with the frequentist approach, which is disappointing because it did not happen with the previous models where the com_poisson family converged.
Can you please help me running the bmi models with com_poisson family? Do you think that adding priors may help? Any suggestions?
Thank you very much!
- Operating System: mac OS X El Capitan Version 10.11.6
- brms Version: 2.8.0