BayesFactor regressionBF function generates oddly binned small BFs
I'm using the BayesFactor package in R for a Neuroimaging analysis. I repeatedly compute the Bayes factor for a simple regression design with 'regressionBF' of a binary imaging variable on a continuous variable across a brain image space. (I know that a Bayesian t-test would be the first choice with a binary independent variable, but I'd like to keep the code as flexible as NHST equivalents that use GLMs instead of t-tests).
At first, everything looked fine; the BFs were computed across the whole brain image and spanned across the range from small to very large. Only later I realised that there were no really small BFs <1/10 at all. In one analysis (=repeated application of BF regression over the imaging space), the smallest BF I found was 0.2826627, the second smallest 0.2826627 (=the same except for changes in a decimal place not shown), the third 0.2826627, the fifteenth 0.2826628 (note the change in the shown decimal place) and so on. Basically, small BFs were binned into small groups with very minor changes to decimal places. On the other hand, positive BFs were not binned and looked very reasonable, going up to 6*10^14
Does anybody have an idea what happened to my data?
Is this something to be generally expected with such regression approach, maybe because of the way priors are chosen? Can I change something to get reliable small BFs to assess h0?
Is the Bayes Factor regression maybe invalid with binary independent variables? I am quite naive here, I thought that I could just do the same as I've done before with NHST statistics where a t-test can be formulated as a GLM with a binary predictor.