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

[Alex Fine]

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/
<http://internal.psychology.illinois.edu/~abfine/AlexFineHome.html>

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