Marginal and Conditional R2 for Stan's GLMMs

Ok I see. Again, this was more to get an absolute index of “explanatory” power of the model and of its fixed effects in a way that it would be understandable to a frequentist reviewer or supervisor. But as you said, it might be worth to take the time explaining the core difference of the Bayesian framework rather than trying to mimic the frequentist approach. Thanks.