mcmcsamp error message...
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