ML vs. REML to find a parsimonious mixed model

My take is that it usually doesn't matter and you can tell when it's likely
to before you've started running models based on sample size.

In practice, I almost always use ML unless I've got some specific reason to
try REML (either I have sample size issues or it is for pedagogical
purposes). REML will return less downwardly biased random effects so long
as your sample size is small so you might as well do it if it matters. I've
only seen them return materially different results in a couple of instances
when it wasn't a toy data set designed to produce differences. If you are
getting different p values for the deviance tests on random effects between
REML and ML then you should probably take that as a sign to be extra
paranoid. Most of my models are nonlinear so REML isn't strictly an option
in a lot of software anyway (it is possible but often not on offer). I say
do what you want as long as the deviance tests are still viable.

On Mon, Apr 23, 2018 at 5:38 PM, Maarten Jung <

ML vs. REML to find a parsimonious mixed model

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