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

[Chunyun Ma]

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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>> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
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>>
>
>
>
> --
> Alex Fine
> Ph. (336) 302-3251
> web:  http://internal.psychology.illinois.edu/~abfine/
> <http://internal.psychology.illinois.edu/~abfine/AlexFineHome.html>
>

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