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?