Convergence Error: 0 Fixed Correlations and More

Speaking about this factor variables coding to have uncorrelated
random effects... Following Thierry Onkelinx's advice [thanks a lot
for this advice, by the way, I think I didn't answered yet, sorry], I
wrote down the models for a similar case, and it turned out that when
having uncorrelated random effects for all levels of a covariate, then
the Y variables were also uncorrelated, but it allowed different
variances for each level.

I thought one advantage of random effects was to introduce
correlations between observations taken for the same patient, so I
wonder if this trick really does what one expects (assuming I didn't
made mistakes, and I am not the only one to have this idea about
consequences of random effects), and that specifying special random
effects covariance matrix structures is less error-prone with nlme?

Best regards,

? On Tue, Sep 22, 2015 at 3:33 AM, Thierry Onkelinx

? > 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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Emmanuel CURIS
                                emmanuel.curis at parisdescartes.fr

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