Bayesian GLMM and BayesFactors
Hi. I'm having a bit of trouble and can't seem to find any help for it.
I have a 3 x 3 factorial design and wish to analyse with Bayesian. I was originally planning on using a mixed repeated measures ANOVA (Bayesian), although my the residuals shown in the QQ plot are not normally distributed and skewed at either end, therefore not meeting the assumptions. So I have attempted other ways of addressing this, transforming the data did not appease the distribution of the residuals. I have since then re-coded the data into categories (clinically relevant decrease, decrease, no change, increase, clinically relevant increase), and have run a generalised linear mixed effect model. However, the results do not include Bayes Factors, so all of the explanations in the JASP Bayesian handbook are not relevant and don't explain the output. I can't find any guidance relating the Bayesian mixed models on your website, just people in forums for several years ago asking for it to be included in the software.
Please help, I don't know if the model fits or not. Or is there any other way to deal with non-normally (skewed at either end) distributed data in a Bayesian approach? I'm trying to do my dissertation but everyone I ask for help says they can't because it relates to Bayesian.
Thank you.
Comments
The solutions to deal with model misspecification apply to both frequentist and Bayesian approaches. I am aware that the GLMMs do not do Bayes factors, and we really should address that. Issuing a request on our GitHub page will help with that. I am not averse to your solution of recoding the data to a coarser level, but it does result in having to apply a model for which we do not (yet) have BFs. I am not sure what aspects of the data are non-normal...do they look t-distributed, with fat tails?
Also my apologies for the tardy reply -- vacation and a grant deadline got in the way.
EJ