One of the advantages of multilevel modeling is of course predictions for new groups. For classical regression with group indicators (fixed effects), prediction for a new group is ill-defined. As a result, I would think that leave-one-out cross validation for such a classical model with groups of size 1 would also be ill-defined when the “one” is the only member of its group.

However, if I fit such a model with `stan_glm()`

and then `loo()`

I get an answer (after following the suggestion to set a `k_threshold`

). So my question is: what is `loo()`

approximating in this case?