Thank you for your support, Roger - actually the problem vanished when
I used sum-coding (after installing the packages and reloading the
data, I accidentally skipped this step yesterday). So, in fact with
the old lme4.0 version, the model did converge. I pasted in all model
summaries and LRTs for dummy and sum coding below.
Would you recommend to always use the old version then?
*** with dummy coding:
(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