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

[Ben Bolker]

[cc'd to r-sig-mixed-models]

On Sun, Oct 18, 2015 at 1:06 PM, Chunyun Ma <mcypsy at gmail.com> wrote:
>
> 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 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?

  Yes.

> 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.

  See previous e-mail.

>
> 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]]
>>>> >
>>>> > _______________________________________________
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>>>>
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>>>
>>>
>>>
>>>
>>> --
>>> Alex Fine
>>> Ph. (336) 302-3251
>>> web:  http://internal.psychology.illinois.edu/~abfine/
>>
>>
>