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

[Levy, Roger]

(cc-ing the list, which I forgot to do on the last response…)


On Mar 3, 2014, at 8:41 AM, Emilia Ellsiepen <emilia.ellsiepen at gmail.com> wrote:

> 
> 
> 2014-03-03 16:23 GMT+01:00 Levy, Roger <rlevy at ucsd.edu>:
>> 
>>> On Mar 3, 2014, at 3:09, "Emilia Ellsiepen" <emilia.ellsiepen at gmail.com> wrote:
>>> 
>>> 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)
>> 
>> Thanks for this follow-up, Emilia! Question: do you get this warning when you fit the more complex model (with the fixed-effects interaction), or the null-hypothesis model (without the fixed-effects interaction)?

> That was for the more complex model with interaction. The one without
> interaction converged without problems.

Thanks Emilia.  Could you please give us a bit more information — show us the fitted models (both the null and alternative-hypothesis models), and also the results of the call to anova()?

Best

Roger