How to compute the Bayes Factor inclusion in JASP?
I read this blog:
I tried to follow the calculations and everything worked fine.
However, I got stuck when I tried to understand the article by van den Bergh et al. (2020): A tutorial on conducting and interpreting a bayesian ANOVA in JASP.
I can reconstruct the BF_M in table 2 (I think, given rounding errors?): For each effect, the BF_M is OR with P(incl) and P(incl|D). Eg., for Model S, .6 and 0.445 gives 0.547.
For Table 3, the authors claim that the BF inclusion is inspired by the blog. I can figure out the P(incl) and P(incl|D) from Table 2, but it seems that the BF_inclusion is not simply the OR of these probabilities (i.e., it is not computed as the OR in Table 2).
I thought that maybe it is computed as in the blog (only comparing P(incl|D) between models), but this does not seem to be true, either.
Does anybody know how to get the BF_inclusion in Table 3? Can it be computed from values in Table 1?