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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