What are the best strategies to select between a small collection of possible models when the models are non-nested and modeled using a GLMM with correlated residuals (longitudinal random effects or $R$-side random effects)?
My only thought was to graphically compare model diagnostics and choose the model that best fits the assumptions. If models were generally comparable, I'd consider the most parsimonious model to be the best candidate. However, I was looking for more statistical methods to compare these kinds of models.
I use both R and SAS, so a connection to either of those would be helpful.