[R-sig-ME] Comparing models with different random effects

[Gang Chen]

Thanks for the help!

> It could make sense if you anova these two models, to find a better one.
> (1 | Subj) + (1 | Item)
> (1 | Subj) + (1 | Subj:Item)

These two models have the same number of parameters (and degrees of
freedom), thus likelihood ration test in anova() would not be
available. And that is why I was asking whether AIC/BIC is the only
way to compare the two model.

My second question is still open: I tend to believe that (1 | Subj) is
nested within (Task | Subj) since the first model has one parameter
(variance) which can be viewed as multiple variances in the second
model being constrained as equal, but I would still appreciate it if
somebody could confirm this.

Gang

> 2010/12/9 Gang Chen <gangchen6 at gmail.com>
>>
>> Suppose that there are multiple task types (Task) and each task type
>> is represented with a few questions (Item). And all subjects (Subj)
>> answer the same questions (Item).
>>
>> How do I compare a model with (1 | Subj) + (1 | Item) versus one with
>> (1 | Subj) + (1 | Subj:Item) in lmer()? Through AIC/BIC (assuming the
>> fixed effect remain the same)? Would it make more sense to consider (1
>> | Subj) + (1 | Subj:Item) + (1 | Item)?
>>
>> Is (1 | Subj) considered as nested within (Task | Subj)?
>>
>> Thanks,
>> Gang
>>
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>
>