[R-sig-ME] Model is nearly unidentifiable with lmer

[Chunyun Ma]

Hi again dear Ben and Alex!

I scaled the continuous predictor (Xc) using scale(Xc, centre=T, scale=T)
and the warning did disappear! Also, the log likelihood remains the same.
As Ben suggested, this indicates the large eigenvalue was not actually a
problem in the first place, although I still feel hazy about why the
warning appeared previously (I need to refresh my memory of what
eigenvalues are).

I also converted the subject using factor(). I would love to better
understand when it is necessary to factor a variable. I did find a post
from stackoverflow
<http://stackoverflow.com/questions/21226069/when-are-factors-necessary-appropriate-in-r>
on a similar topic, but it did not mention the random factor in a lmer
formula.

Alex, I tried both dummy coding and sum coding as you suggested. I got the
same warning message with either coding scheme. I still need to carefully
read your full paper to understand what “maximal random-effect structure”
is.

To recap, my remaining questions are:

   - Can I ignore the eigenvalue warning and proceed with the raw variable
   (because the rescaling makes it hard to interpret) since the log likelihood
   does not change?
   - In using lmer for RM design, if the random factor is
   subject/participant, should I always makes sure subject has been converted
   to factor using factor()? Any further reference would be appreciated.

Many thanks!

Warmly, Chunyun

On Sun, Oct 18, 2015 at 11:46 AM, Chunyun Ma <mcypsy at gmail.com> wrote:

Hi dear Ben and Alex!
>
> Thank you very much for your help and guidance! I just started reading
> your references. As I was exploring the alternatives you have suggested,
> another question came up. This may sounds silly, but I haven't found a
> definitive answer online: in the lmer formula, is it necessary to convert
> the random factor into factor using factor()?  Given that I have a RM
> design, my random factor will always be subject, which is numerical unless
> I force it into factor...
>
> Thank you again!
>
> Warmly,  Chunyun
>
> On Sun, Oct 11, 2015 at 8:28 PM, Alex Fine <abfine at gmail.com> wrote:
>
>> You might also try using sum-coding rather than (the default) dummy
>> coding with the categorical predictors.  Assuming the design is roughly
>> balanced, this is like mean-centering the categorical variables.  This will
>> change the interpretation of the coefficients.
>>
>> Here is some further reading:  http://talklab.psy.gla.ac.uk/tvw/catpred/
>>
>> On Sun, Oct 11, 2015 at 8:18 PM, Ben Bolker <bbolker at gmail.com> wrote:
>>
>>> Short answer: try rescaling all of your continuous variables.  It
>>> can't hurt/will change only the interpretation.  If you get the same
>>> log-likelihood with the rescaled variables, that indicates that the
>>> large eigenvalue was not actually a problem in the first place.
>>>
>>>    I don't think the standard citation from the R citation file
>>> <https://cran.r-project.org/web/packages/lme4/citation.html>, or the
>>> book chapter I wrote recently (chapter 13 of Fox et al, Oxford
>>> University Press 2015 -- online supplements at
>>> <http://ms.mcmaster.ca/~bolker/R%/misc/foxchapter/bolker_chap.html>)
>>> cover rescaling in much detail. Schielzeth 2010
>>> doi:10.1111/j.2041-210X.2010.00012.x gives a coherent argument about
>>> the interpretive advantages of scaling.
>>>
>>>    Ben Bolker
>>>
>>>
>>> On Sun, Oct 11, 2015 at 6:37 PM, Chunyun Ma <mcypsy at gmail.com> wrote:
>>> > Dear all,
>>> >
>>> > This is my first post in the mailing list.
>>> > I have been running some model with lmer and came across this warning
>>> > message:
>>> >
>>> > In checkConv(attr(opt, “derivs”), opt$par, ctrl = control$checkConv, :
>>> > Model is nearly unidentifiable: very large eigenvalue
>>> >
>>> >    - Rescale variables?
>>> >
>>> > Here is the formula of my model (I substituted variables names with
>>> generic
>>> > names):
>>> >
>>> > y ~ Intercept + Xc + Xd1 + Xd2 + Xc:Xd1 + Xc:Xd2 + Zd + Zd:Xc + Zd:Xd1
>>> +
>>> > Zd:Xd2 + (1 + Xc + Xd1 + Xd2 | sub)
>>> >
>>> > Xc: continuous var
>>> > Xd: level-1 dummy variable(s)
>>> > Zd: level-2 dummy variable
>>> >
>>> > A snapshot of data. I can also provide the full dataset if necessary.
>>> > sub Xc Xd1 Xd2 Zd y 1 36 0 0 1 1346 1 45 0 1 1 1508 1 72 1 0 1 1246 1
>>> 12 1 0
>>> > 1 1164 1 24 1 0 1 1295 1 36 1 0 1 1403
>>> >
>>> > When I reduced the # of random effect to (1+Xc|sub), the warning
>>> message
>>> > disappeared, but the model fit became poorer.
>>> > My question is: which variable(s) should I rescale? I’d be happy to
>>> > better understand t
>>> > he
>>> >
>>> > warning message if anyone could
>>> > kindly
>>> > suggest
>>> > some
>>> >  reference paper/book.
>>> >
>>> > Thank you very for your help!!
>>> >
>>> > Chunyun
>>> >
>>> >
>>> >         [[alternative HTML version deleted]]
>>> >
>>> > _______________________________________________
>>> > R-sig-mixed-models at r-project.org mailing list
>>> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>>
>>
>> --
>> Alex Fine
>> Ph. (336) 302-3251
>> web:  http://internal.psychology.illinois.edu/~abfine/
>> <http://internal.psychology.illinois.edu/~abfine/AlexFineHome.html>
>>
>
> ​

	[[alternative HTML version deleted]]