[R-sig-ME] Convergence Error: 0 Fixed Correlations and More

[Chris Heffner]

Hello all,

Just wanted to update in case anyone else has a similar problem in the
future.  I got some input off the listserv to remove any items/participants
with a proportion of responses either uniformly at 0% or at 100% across all
conditions, which was true for a couple of my items.  I went ahead and did
that, and it fixed all of my convergence issues.  Ben's description of how
to suppress the correlations between random effects were right on; the
exact syntax was actually (0 + dummy(factorC,"S") | item), but they worked
like a charm.

Thanks all for all of your input,

Chris

On Tue, Sep 22, 2015 at 9:40 AM Ben Bolker <bbolker at gmail.com> wrote:

> On Tue, Sep 22, 2015 at 3:33 AM, Thierry Onkelinx
> <thierry.onkelinx at inbo.be> wrote:
> > Dear Chris,
> >
> > The correct syntax is (1 + FactorC | item) not (1 + FactorC || item).
> > Use a single |. I find the item.1 strange in the output. This might be
> > due to the syntax error.
>
>    Chris might be trying to suppress the correlations between
> random-effect component:
> the double-bar notation expands to (1|item) + (0 + FactorC | item),
> but there's a problem here: there's not *really* a way to do this with the
> double-bar syntax.  If FactorC has two levels (B and S), then the
> right (tedious)
> way to do this is
>
> ( 1|item)+(0+dummy(FactorC,"C")|item)
>
> or maybe (?)
>
> (0+dummy(FactorC,"C")|item)(0+dummy(FactorC,"C")|item)
>
>
>
> (I think the current model is overparameterized)
>
> >
> > The item random effect variances are quit high. You might have a
> > problem of quasi-complete separation. (1 + FactorC | item) might be
> > too complex for your data. Does (1 | item) converge?
> >
> > Best regards,
> >
> > ir. Thierry Onkelinx
> > Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> > and Forest
> > team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> > Kliniekstraat 25
> > 1070 Anderlecht
> > Belgium
>
>
>   [snip]
>
> >
> >
> > 2015-09-21 18:37 GMT+02:00 Chris Heffner <heffner at umd.edu>:
> >> Hi,
> >>
> >> I'm running a psychology experiment with a few fixed effects and random
> >> factors, but for some of the models that I'm comparing I get an output
> that
> >> looks something like this:
> >>
> >> Generalized linear mixed model fit by maximum likelihood (Laplace
> >> Approximation) ['glmerMod']
> >>  Family: binomial  ( logit )
> >> Formula: FW ~ FactorA + FactorB + FactorC + FactorA:FactorC +
> >> FactorB:FactorC +      (1 | participant) + (1 + FactorC || item)
> >>    Data: east.acc1.subset
> >> Control: glmerControl(optCtrl = list(maxfun = 30000))
> >>
> >>      AIC      BIC   logLik deviance df.resid
> >>   1001.5   1066.9   -487.7    975.5     1120
> >>
> >> Scaled residuals:
> >>     Min      1Q  Median      3Q     Max
> >> -3.8335 -0.3041  0.1416  0.3566  2.8851
> >>
> >> Random effects:
> >>  Groups      Name        Variance  Std.Dev.  Corr
> >>  item     FactorCB       5.454e+00 2.3352985
> >>              FactorCS       3.097e+00 1.7597629 -0.81
> >>  item.1   (Intercept) 5.437e+00 2.3316731
> >>  participant (Intercept) 2.595e-08 0.0001611
> >> Number of obs: 1133, groups:  item, 55; participant, 23
> >>
> >> (Intercept)            0.1928833  0.0006222   310.0   <2e-16 ***
> >> FactorAInitial        1.8077886  0.0006222  2905.5   <2e-16 ***
> >> FactorB150        -0.4506653  0.0006220  -724.5   <2e-16 ***
> >> FactorB200        -0.5485114  0.0006220  -881.9   <2e-16 ***
> >> FactorCS                 -0.3923921  0.0006221  -630.8   <2e-16 ***
> >> FactorAInitial:FactorCS -0.0889474  0.0006221  -143.0   <2e-16 ***
> >> FactorB150:FactorCS   0.1347207  0.0006221   216.6   <2e-16 ***
> >> FactorB200:FactorCS   0.0682518  0.0006221   109.7   <2e-16 ***
> >> ---
> >> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >>
> >> Correlation of Fixed Effects:
> >>             (Intr) FAIn FB150 FB200 FCS FAI:FCS FB150:FCS
> >> FAIntl 0.000
> >> FB150 0.000  0.000
> >> FB200 0.000  0.000  0.000
> >> FCS       0.000  0.000  0.000  0.000
> >> FaInt:FCS 0.000  0.000  0.000  0.000  0.000
> >> FB150:FCS 0.000  0.000  0.000  0.000  0.000 0.000
> >> FB200:FCS 0.000  0.000  0.000  0.000  0.000 0.000  0.000
> >>
> >> convergence code: 0
> >> Model failed to converge with max|grad| = 0.113738 (tol = 0.001,
> component
> >> 1)
> >> Model is nearly unidentifiable: very large eigenvalue
> >>  - Rescale variables?
> >>
> >> I've tried look through my data, as my first thought was that data was
> >> somehow miscoded, but I can't see anything that would be the matter.  A
> >> more complicated version of the model had the same problem until I got
> rid
> >> of a single participant (who seemed otherwise entirely unexceptional).
> The
> >> more complicated model now converges fine, but this simpler one now has
> >> these issues.  I have an almost identical dataset that I've been doing
> >> almost exactly the same models with that hasn't been giving me similar
> >> problems.
> >>
> >> Any thoughts?
> >>
> >> Thank you,
> >>
> >> Chris
> >>
> >>         [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
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> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

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