[R-sig-ME] convergence issues on lme4 and incoherent error messages

[René]

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, René


Am Do., 13. Juni 2019 um 16:27 Uhr schrieb Cristiano Alessandro <
cri.alessandro using 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 using gmail.com> wrote:
>
> >   Details below
> >
> > On Wed, Jun 12, 2019 at 12:38 AM Cristiano Alessandro
> > <cri.alessandro using 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   This warning is harmless; it most likely comes from an outdated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:
> >
> >
> https://github.com/lme4/lme4/commit/9d5d433d40408222b290d2780ab6e9e4cec553b9
> > )
> >
> > > 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
> > >
> >
> https://stats.stackexchange.com/questions/408504/variance-covariance-matrix-with-negative-entries-on-mixed-model-fit
> > > , 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
> > >
> >
> https://stats.stackexchange.com/questions/412345/diagonal-var-cov-matrix-for-random-slope-in-lme4
> > > ).
> > >
> > > Any help is highly appreciated. Thanks!
> > >
> > > Cristiano
> > >
> > >         [[alternative HTML version deleted]]
> > >
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> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
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