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

[Cristiano Alessandro]

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   This warning is harmless; it most likely comes from an outdated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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>

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