ML vs. REML [🇷 for BE/BA]

posted by Helmut Homepage – Vienna, Austria, 2015-04-20 18:35 (3714 d 11:39 ago) – Posting: # 14717
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Hi ElMaestro,

❝ There is still something that really bothers me and that is the log Likelihood difference of R versus WNL. With REML I think optimisation switches back and forth be in terms of the estimates. […]



Maybe, maybe not. PHX’ manual only tells me:

The linear mixed effects model is:

y = Xβ + Zγ + ε,
V = Variance(y) =ZGZT + R.

Let θ be a vector consisting of the variance parameters in G and R. The full maximum likelihood procedure (ML) would simultaneously esti­mate both the fixed effects parameters β and the variance parameters θ by maximizing the likelihood of the observations y with respect to these parameters. In contrast, restricted maximum likelihood esti­ma­tion (REML) maximizes a likelihood that is only a function of the vari­ance parameters θ and the observations y, and not a function of the fixed effects parameters. Hence for models that do not contain any fixed effects, REML would be the same as ML.


❝ ML would only be plain and simple covariance matrix fiddling, I think (?).


Well, we do have fixed effects, right? At least in PHX/WNL, only REML is imple­mented. IIRC, REML is recommended by Patterson/Jones somewhere.


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