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

[Ben Bolker]

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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