Model is nearly unidentifiable with lmer
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
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