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

[René]

sorry... "then the model should [not] care about scaling "

Am Do., 13. Juni 2019 um 17:19 Uhr schrieb René <bimonosom using 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, 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   This warning is harmless; it most likely comes from an outdated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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>> >
>>
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