I have a question regarding `bayes_R2()`

. From the docs, I understand that `re.form`

can be used to condition or refrain from conditioning on random effects in mixed models:

The default, NULL, indicates that all estimated group-level parameters are conditioned on.

However, I don’t quite understand the output. Similar to a frequentist framework, I would expect the conditional R2, i.e. the R2 for the model taking random effects into account, to be higher than the “marginal” R2 that only considers fixed effects. In other word, I would expect the point estimate for `bayes_R2(m, re.form = NULL)`

to be higher than for `bayes_R2(m, re.form = NA)`

.

```
library(rstanarm)
m <- stan_lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris, refresh = 0, seed = 333)
# The default, NULL, indicates that all estimated
# group-level parameters are conditioned on.
mean(bayes_R2(m, re.form = NULL))
#> [1] 0.8278072
# "marginal" R2?
mean(bayes_R2(m, re.form = NA))
#> [1] 0.9515886
```

Is there anything I am missing?