[R] help with lme()

[Pascal A. Niklaus]

As far as I understand it, the problem is that REML accounts for the 
degrees of freedom used up by fixed effects (e.g., treatments), whereas 
ML does not account for these. From that perspective, REML appears to be 
the "better" fitting method.

However, if you test for a fixed effect by comparing two models, one 
including the fixed effect and one lacking it but otherwise identical, 
then the model comparison anova(model1,model2) is invalid when you use 
REML (because there is a different number of df consumed by the fixed 
effects in model1 and model2), but it is valid if you use ML (because it 
does not account for the df used up by the fixed effects at all).

Pascal


Bill Shipley wrote:

>Hello. I am trying to determine whether I should be using ML or REML
>methods to estimate a linear mixed model.   In the book by Pinheiro &
>Bates (Mixed-effects models in S and S-PLUS, page 76) they state that
>one difference between REML and ML is that « LME models with different
>fixed-effects structures fit using REML cannot be compared on the basis
>of their restricted likelihoods.  In particular, likelihood ratio tests
>are not valid under these circumstances.”
>
>I am not sure what “fixed-effects structures” means.  Does it mean that,
>as long as the types of contrasts are the same between two models, they
>ARE comparable, but are NOT comparable if the types of contrasts are
>changes?  Or rather, does it simply mean that one should use t or F
>tests for the fixed effects, and restrict the likelihood ratio tests to
>the random effects only if using REML?
>
> 
>
>Bill Shipley
>
>Associate Editor, Ecology
>
>North American Editor, Annals of Botany
>
>Département de biologie, Université de Sherbrooke,
>
>Sherbrooke (Québec) J1K 2R1 CANADA
>
>Bill.Shipley at USherbrooke.ca
>
> <http://callisto.si.usherb.ca:8080/bshipley/>
>http://callisto.si.usherb.ca:8080/bshipley/
>
> 
>
>
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