[R-sig-ME] likelihood-ratio tests in conflict with coefficiants in maximal random effect model

[Emilia Ellsiepen]

Thank you for your feedback and this interesting discussion.


>> First off, it is not clear that Emilia's specific problem is being caused
>> by over-parameterization.  Emilia, could you perhaps give more information
>> about the nature of the dataset that you're analyzing?  Is it a 2x2
>> within-subjects, within-sentence balanced design without a great deal of
>> missing data?  In my experience with the last few pre-1.0 versions, lme4 is
>> generally very good at converging to an optimum for these kinds of datasets
>> with the number of observations and groups your fitted model reports.  Have
>> you tried fitting the model with the nlminb optimizer, either by including
>>
>> optimizer="optimx",optCtrl=list(method="nlminb")
>>
>> in the list of arguments to lmerControl, or by using the last pre-1.0
>> version of lme4 (available as lme4.0 on R-Forge)?  Do you still get similar
>> problems with the nlminb optimizer?  (You should definitely not get the
>> result that the simpler model has a higher log-likelihood.)

The design was a balanced 2x2 with-in subjects and with-in sentences
design without any missing data from a magnitude estimation
experiment.
When I use the nlminb optimizer (by installing the lme4.0 version), I
do get the interaction using the likelihood-ratio test, but I also get
the following warning message:

Warning message:
In mer_finalize(ans) : singular convergence (7)

I suppose that I should not be interpreting this model either and thus
will try to simplify the random effect structure.
Would you otherwise recomment to use whatever optimizer works best for
the data set in question? Shouldn't the optimizer be reported as well
then?

Emilia
-----------------------------------------
Emilia Ellsiepen
Institut für Linguistik
Goethe-Universität Frankfurt