Inspired by this post on the difference between explaining and predicting. I want to ask
- is mixed model primarily used to get better explanation (such as, but not limited to, getting better coefficients and standard errors, or being able to decompose the variation), or is it primarily used to get better prediction?
I imagine the answer would be the former (explaining), and if that's the case,
- does it add any value to prediction?
(would appreciate any form of discussion, but would also be delighted to see published references on the issue)