Consider a model with random effects (1 | A) + (1 | B) + (1 | C). How to test for significance of effect A?
Currently might use rcompare(~ (1 | B) + (1 | C)). Less than ideal because that's a lot of typing and more importantly the thing you're testing is the the one you're specifically not mentioning.
Maybe extend random so that you can specify a grouping factor to drop? i.e. random() would still work for a single random effect, but e.g. random("A") would just drop A.
Consider a model with random effects
(1 | A) + (1 | B) + (1 | C). How to test for significance of effect A?Currently might use
rcompare(~ (1 | B) + (1 | C)). Less than ideal because that's a lot of typing and more importantly the thing you're testing is the the one you're specifically not mentioning.Maybe extend
randomso that you can specify a grouping factor to drop? i.e.random()would still work for a single random effect, but e.g.random("A")would just dropA.