[R-sig-ME] mcmcsamp error message...

[Douglas Bates]

On Fri, Aug 14, 2009 at 1:15 PM, Petar Milin<pmilin at ff.uns.ac.rs> wrote:
>
>
> Douglas Bates wrote:
>>
>> On Wed, Aug 12, 2009 at 12:27 PM, Petar Milin<pmilin at ff.uns.ac.rs> wrote:
>>>
>>> Hello!
>>> I am puzzled with an error message that mcmcsamp for models with random
>>> correlation parameters is not implemented yet. However, this would be the
>>> case only with the model:
>>> lmer(rt ~ FACTOR1 + COVARIATE1 + COVARIATE2 + COVARIATE3 +
>>> (1+FACTOR1|subject) + (1|item) + (0+COVARIATE3|item), data=dat)
>>> And same with:
>>> lmer(rt ~ FACTOR1 + COVARIATE1 + COVARIATE2 + COVARIATE3 +
>>> (1|subject) + (0+FACTOR1|subject) + (1|item) + (0+COVARIATE3|item),
>>> data=dat)
>>
>>> If I run just:
>>> lmer(rt ~ FACTOR1 + COVARIATE1 + COVARIATE2 + COVARIATE3 +
>>> (1|subject) + (1|item) + (0+COVARIATE3|item), data=dat)
>>> mcmcsamp ends fine.
>>
>>> I guess that the problem is in the fact that subjects were assigned
>>> (randomly) to only one level of the FACTOR1. Am I right?
>>
>> I'm not sure what you mean by "the problem".  If FACTOR1 is a
>> non-trivial factor (i.e. it has more than one level) then the
>> random-effects terms (1 + FACTOR1|subject) and (0+FACTOR1|subject)
>> generate correlated random effects and currently mcmcsamp does not
>> handle models with correlated random effects.
>>
>> If, as you say, each subject is assigned to only one level of FACTOR1
>> then neither of the terms above make sense.  You can't expect to
>> estimate an interaction of FACTOR1 and subject when FACTOR1:subject is
>> equivalent to subject.
>
> Sorry, I meant SOME subjects, but not all of them. And FACTOR1 has two
> levels, exactly. Hence, my question could be rephrased: if only subsample
> repeated levels of FACTOR1, could that be treated as a case of correlated
> random effects, in principle?

Yes.

> Thus, in future, with the implementation
> solved, that could be handled as a regular/proper case?

Yes.

>>> I think, previously, mcmcsamp handled this kind of nesting, but I might
>>> be
>>> wrong.
>
> It is weird structure, anyway. (I am just trying to help a colleague.)
>
> Thanks for the answer.
> Best,
> PM
>