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

[Cristiano Alessandro]

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 René <bimonosom using gmail.com> wrote:

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   This warning is harmless; it most likely comes from an outdated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