convergence issues on lme4 and incoherent error messages
This does not happen. However, there are combinations of predictor variables that *for some subjects* have no data on the response, as explained here: https://stats.stackexchange.com/questions/412703/zeroed-random-effects-after-fitting-a-mixed-effect-model?noredirect=1#comment770806_412703 Cristiano
On Thu, Jun 13, 2019 at 5:30 PM Ben Bolker <bbolker at gmail.com> wrote:
The only other think I can think of is that you may have a *complete separation* issue or near-complete separation, i.e. some combinations of predictor variables may have responses that are all-zero or all-one ... On 2019-06-13 1:02 p.m., Cristiano Alessandro wrote:
Thanks! I do have the call:
dataframe$predictor1<-as.factor(dataframe$predictor1)
dataframe$predictor2<-as.factor(dataframe$predictor2)
at the beginning of my code.
Cristiano
On Thu, Jun 13, 2019 at 10:20 AM Ren? <bimonosom at gmail.com
<mailto:bimonosom at gmail.com>> wrote:
sorry... "then the model should [not] care about scaling "
Am Do., 13. Juni 2019 um 17:19 Uhr schrieb Ren? <bimonosom at gmail.com
<mailto:bimonosom at gmail.com>>:
Hi, :)
"But the predictor variables are all categorical with "sum"
coding; I am not sure what it means to center and scale a
categorical
variable."
Sure means nothing :))
If your predictors are actually categorical, then the model
should care about scaling at all (and should not come up with
this).
The implies, that you might want to check, whether the
categorical predictors are actually factorized in the dataframe.
e.g.
dataframe$predictor1<-as.factor(dataframe$predictor1)
dataframe$predictor2<-as.factor(dataframe$predictor2)
or (the same but less affected by how the variable is coded
beforehand... )
dataframe$predictor1<-as.factor(as.character(dataframe$predictor1))
Best, Ren?
Am Do., 13. Juni 2019 um 16:27 Uhr schrieb Cristiano Alessandro
<cri.alessandro at gmail.com <mailto:cri.alessandro at gmail.com>>:
Thanks a lot for your help!
Regarding "centering and scaling". I am not familiar with
this; I will
check this out. But the predictor variables are all
categorical with "sum"
coding; I am not sure what it means to center and scale a
categorical
variable. Is there a theory behind this or a text I could
look at?
Best
Cristiano
On Wed, Jun 12, 2019 at 11:31 PM Ben Bolker
<bbolker at gmail.com <mailto:bbolker at gmail.com>> wrote:
> Details below
>
> On Wed, Jun 12, 2019 at 12:38 AM Cristiano Alessandro
> <cri.alessandro at gmail.com
<mailto:cri.alessandro at gmail.com>> wrote:
> >
> > Hi all,
> >
> > I am having trouble fitting a mixed effect model. I keep
getting the
> > following warning, independently on the optimizer that I
use (I tried
> > almost all of them):
> >
> > Warning messages:
> > 1: 'rBind' is deprecated.
> > Since R version 3.2.0, base's rbind() should work fine
with S4 objects
>
> This warning is harmless; it most likely comes from an
outdated
> version of lme4 (we fixed it in the devel branch 15 months
ago:
>
>
> )
>
> > 2: In optimx.check(par, optcfg$ufn, optcfg$ugr,
optcfg$uhess, lower, :
> > Parameters or bounds appear to have different scalings.
> > This can cause poor performance in optimization.
> > It is important for derivative free methods like
BOBYQA, UOBYQA,
> NEWUOA.
>
> Have you tried scaling & centering the predictor
variables?
>
> > 3: Model failed to converge with 5 negative eigenvalues:
-2.5e-01
> -5.8e-01
> > -8.2e+01 -9.5e+02 -1.8e+03
> >
> > This suggests that the optimization did not converge. On
the other hand,
> if
> > I call summary() of the "fitted" model, I receive (among
the other
> things)
> > a convergence code = 0, which according to the
documentation means that
> the
> > optimization has indeed converged. Did the optimization
converged or not?
> >
> > convergence code: 0
>
> These do look large/worrying, but could be the result
of bad
> scaling (see above). There are two levels of checking for
convergence
> in lme4: one at the level of the nonlinear optimizer
itself (L-BFGS-B,
> which gives a convergence code of zero) and a secondary
attempt to
> estimate the Hessian and scaled gradient at the reported
optimum
> (which is giving you the "model failed to converge"
warning).
> ?convergence gives much more detail on this subject ...
>
> > Parameters or bounds appear to have different scalings.
> > This can cause poor performance in optimization.
> > It is important for derivative free methods like
BOBYQA, UOBYQA,
> NEWUOA.
> >
> > Note that I used 'optimx' ("L-BFGS-B") for this specific
run of the
> > optimization
>
> I would *not* generally recommend this. We don't have
> analytically/symbolically computed gradients for the
mixed-model
> likelihood, so derivative-based optimizers like L-BFGS-B
will be using
> finite differencing to estimate the gradients, which is
generally slow
> and numerically imprecise. That's why the default choices
are
> derivative-free optimizers (BOBYQA, Nelder-Mead etc.).
>
> I see there's much more discussion at the SO question, I
may or may
> not have time to check that out.
>
> . I also get other weird stuff that I do not understand:
> > negative entries in the var-cov matrix, which I could
not get rid of even
> > if I simplify the model a lot (see
> >
>
> > , with data). I thought of further simplify the var-cov
matrix making it
> > diagonal, but I am still struggling on how to do that in
lme4 (see
> >
>
> > ).
> >
> > Any help is highly appreciated. Thanks!
> >
> > Cristiano
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org
<mailto:R-sig-mixed-models at r-project.org> mailing list
>
[[alternative HTML version deleted]]
_______________________________________________
R-sig-mixed-models at r-project.org
<mailto:R-sig-mixed-models at r-project.org> mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models