I'm very new to Bayesian. I have an effect size similar to R SQUARE ($R^2$) in the regression context. That is, this effect size goes from 0 to 1 (i.e., 0 is taken as the null effect, and 1 as the perfect upper limit). Let's assume that, a $Beta$ prior e.g., $Beta(x, ~\alpha = 2, ~\beta = 5)$ would make a reasonable choice on this effect size. To review, we have a $Beta$ Prior, and an F distribution as the likelihood for this effect size.
How does Savage-Dickey density ratio to obtain a Bayes Factor become possible in this setting? That is, the density value at the null value (i.e., here 0) taken from the $Beta$ prior divided over the density value at the null value (i.e., here 0) taken from the Posterior distribution? Do I need to pick a different prior that allows some weight on the null value?