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

[matteo dossena]

Thanks a lot Ben,

most probably, my doubt rises from a still superficial comprehension of the topic.
I guess, the correlation matrix is more important when is not simply symmetric and 
when the analysis is actually investigating the temporal dynamics.
I my case I'm interested in fitting a model that properly accounts for the experimental design.

Cheers
m.

Il giorno 7 Feb 2012, alle ore 16:56, Ben Bolker ha scritto:

> 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.
>> 
> 
> [snip snip]
> 
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