I’m using brms to estimate Bayes factors for null effects found when fitting lmer models. To do so, I’ve fitted the full brm model to the data (using a normal distribution so comparable to lmer) and then used bayes_factor() to compare this model to models without the predictor of interest. However, I’m getting very large Bayes factors (over 1000) for effects that are nowhere near significance (t values are < 0.5) and I don’t think they’re quite right. I’ve tried running with more iterations, but it doesn’t seem to make much difference. I’ve also ran pp_check and the normal distribution doesn’t fit particularly well, but I’m not keen on changing it since the models showing null effects were fitted in lmer. Any idea what might be going on, or how I can solve this issue?
I attach the data and script in case they’re helpful. I fit a full model with three predictors, and then two null models (one without a main effect of CC, one without an interaction between CC and PC). I’m running brms version 2.2.0 on macOS High Sierra.