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

[Ben Bolker]

lme4 always treats grouping variables (those on the right side of a
bar in a random-effects term such as (1|g) ) as factors, no matter
what their underlying type is.  This is particularly useful for models
such as  z ~ year + (1|year), which treats year as numeric (i.e.
fitting a linear regression line) in the fixed-effects part of the
model but as a categorical grouping variable (i.e. fitting year-level
deviations from the regression line) in the random-effects part of the
model.

  That said, if you have variables that are numeric in appearance but
are always going to be treated as categorical (e.g. subject IDs that
are arbitrary numeric codes), it's best practice to explicitly convert
them to factors early in your workflow.


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/
>
>