Bayes Factors for Linear Mixed Models in JASP
Tl;dr: Is it possible to get (or calculate/derive) Bayes Factors for linear mixed models, for planned contrasts or at all?
The output seems to provide 95% credible intervals for the highest posterior density, but no BF.
A little more detail:
I’m running a mixed model with Condition (2 levels) and Group (3 levels) as fixed factors, and Subject as a random factor.
I would like to unpack the Group*Condition interaction to compare the Condition effect between groups. Essentially, the difference between the conditions for Group A is different from that for Group B and from the condition difference for Group C, while the latter two do not significantly differ from one another. (says the frequentist analysis)
I was hoping to use the Bayesian mixed models to obtain something akin to a Bayes factor for these three contrasts, that could indicate the strength of evidence for these three comparisons. Especially for the non-difference between groups B and C it would be nice to have an indication of how robust this non-difference is. (e.g. can I state that they are the same, or should I say that the study was underpowered to detect the difference).
I have specified the three contrasts and for these I get 95% credible intervals. Can I somehow use the credible intervals to arrive a conclusion about the evidence for H0? Alternatively, there are some fit indices (WAIS, LOO) for the entire model; could I somehow run different models and compare based on those?
Many thanks for your help,