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

[Chris Heffner]

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

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