[R-sig-ME] repeated measure in partially crossed design

[Ben Bolker]

matteo dossena <m.dossena at ...> writes:

> this really make things clearer now, seems like (season|subject), 
> could be the appropriate structure.
> 
> However, a last doubt still trouble me.
> 
> Having (season|subject) fitted as random effect, 
> is it taking in consideration pseudoreplication
> (repeated measures on subject)?

  Yes.

> If I would do this analysis with lme() I would fit a model with the
>  argument correlation=CorCompSymm(form=~1|subject),
> and a model without correlation than compared the two 
> to assess wether or not  there is violation of the independence.
> Is this a sensible things to do?

  I have to admit I don't quite understand why people fit
CorCompSymm models so much since they are *almost* equivalent to
just including a random effect of the form ~1|subject (with the
difference, I guess, that negative within-cluster correlations
are possible, while random=~1|subject enforces positive correlations).

> 
> Since i'm working with lmer(), how can I check if correlation
>  has to be included in the model?

  This is partly a philosophical question.  I would say that if
subject blocking is part of your experimental/sampling design then
you should include it in the model in any case, unless it causes
severe technical difficulties with the fitting.

   CorCompSymm is not a possibility in lmer.  In principle 
you can do a likelihood ratio test, but lmer won't fit models
without any random effects.  You could try the RLRsim package.
See also advice in <http://glmm.wikidot.com/faq> about how
(and whether) to test random effects.

> 
> Cheers
> m.
> 

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