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

[Chris Heffner]

Hello Thierry,

Thanks for the input!  The || syntax is intentional; it gets rid of the
correlation parameters between the random effects in the model.  This was
done based on my reading of the Bates, Kliegl, Vasishth, and Baayen
manuscript ("Parsimonious mixed models") from earlier this year as a way to
tamp down on overcomplexity.  However, changing it to a single pipe, (1 +
FactorC | item), does not affect convergence, although the warnings I get
change:

1: In vcov.merMod(object, use.hessian = use.hessian) :
  variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov
estimated from RX
2: In vcov.merMod(object, correlation = correlation, sigm = sig) :
  variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov
estimated from RX1: In vcov.merMod(object, use.hessian = use.hessian) :
  variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov
estimated from RX
2: In vcov.merMod(object, correlation = correlation, sigm = sig) :
  variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov
estimated from RX

(1 | item) converges... but so does (1 + FactorB + FactorC | item), which
to me seems to argue against model complexity being the cause.

Thank you,

Chris

On Tue, Sep 22, 2015 at 3:34 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.
>
> 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
>
> To call in the statistician after the experiment is done may be no
> more than asking him to perform a post-mortem examination: he may be
> able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does
> not ensure that a reasonable answer can be extracted from a given body
> of data. ~ John Tukey
>
>
> 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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