I am trying to fit a Bayesian multilevel model in R and have several questions. I found two packages (brms and rstanarm) and am able to perform the analysis with both of them, so the technical part is not the problem. But:
- How do I decide for one of the two packages? Do they calculate differently or are they basically the same? Is there a way to tell if one the two should be preferred? If so, how?
- I did not specify any priors yet because I didn’t want to do anything wrong. However, I expect a positive effect because the treatment I examine has shown positive effects in numerous studies before (though with slightly different material, hence I cannot be completely sure either, especially about the magnitude of the effect). How is the best way to specify priors in a way that translates to “I’m not so sure about the magnitude but I expect a positive effect”?
- Is it common to just interpret the single parameters of my final model (e.g., median and MPE) or should I include the Bayes Factor for my central effect as well?
I never used Bayesian statistics in my work before, but I see the benefits and would like to get a better understanding. If anyone has any advice, general of specifically regarding my questions above, it would be greatly appreciated.
Here is what I mainly used to work with the brms package: https://cran.r-project.org/web/packages/brms/vignettes/brms_multilevel.pdf
And here is the tutorial I used for rstanarm: https://cran.r-project.org/web/packages/psycho/vignettes/bayesian.html#mixed-models