Francisco Torreira wrote: > Hello, > > I am fitting a mixed model that prompts the following warning messages: > > Warning messages: > 1: Estimated variance-covariance for factor 'spk' is singular > in: `LMEoptimize<-`(`*tmp*`, value = list(maxIter = 200L, tolerance = > 1.49011611938477e-08, > 2: nlminb returned message function evaluation limit reached without > convergence (9) > in: `LMEoptimize<-`(`*tmp*`, value = list(maxIter = 200L, tolerance = > 1.49011611938477e-08, > > Although the model is fitted, R does not let me run simulations on it > with mcmcamp(). This is the error message I get: > >> mcmcsamp(full, n=10000) > Error: inconsistent degrees of freedom and dimension > Error in t(.Call(mer_MCMCsamp, object, saveb, n, trans, verbose, deviance)) : > error in evaluating the argument 'x' in selecting a method for function > 't > > The model was: > full <- lmer(an ~ type + (1 + type | spk) - 1) > > My design included 5 speakers (spk) and 5 utterance types (type). For > each combination of speaker and utterance type there were > approximately 20 repetitions. If I fit a more reduced model with no > random effect for type within speakers, as in lmer(an~type+(1|spk)), > no warning appears. Here is the summary of my full model: > > Linear mixed-effects model fit by REML > Formula: an ~ 1 + type + (1 + type | spk) - 1 > AIC BIC logLik MLdeviance REMLdeviance > 4663 4747 -2311 4653 4623 > Random effects: > Groups Name Variance Std.Dev. Corr > spk (Intercept) 1568.3 39.601 > typee 1037.3 32.208 -0.745 > typeg 1303.7 36.107 -0.659 0.946 > typei 1780.9 42.200 -0.778 0.976 0.864 > typel 757.4 27.521 -0.725 0.839 0.826 0.865 > Residual 598.8 24.470 > number of obs: 498, groups: spk, 5 > > Fixed effects: > Estimate Std. Error t value > typea 78.87 17.88 4.410 > typee 18.49 12.13 1.524 > typeg 50.86 14.26 3.566 > typei 11.42 12.48 0.915 > typel 14.94 12.46 1.199 > > Correlation of Fixed Effects: > typea typee typeg typei > typee 0.570 > typeg 0.491 0.898 > typei 0.240 0.851 0.722 > typel 0.699 0.758 0.739 0.622 > > I wonder if the high correlations correlations between several > utterance types and the intercept in the random part of the model > aren't causing all this trouble. I would appreciate any comment on the > warnings.
Dear Francisco, I think many of us have experienced problems with singular estimated variance-covariance matrices with lmer. In some cases I am certain that the problem arises from near-perfect correlations between random effects, but I personally have not yet come to a deep understanding of what conditions can give rise to these near-perfect correlations. A couple of possibilities: first, you can remove the intercept/utterance-type correlation term from your model by respecifying it as an ~ type + (1 | spk) + (0 + type | spk) - 1 and see whether this eliminates the singularity. In your case, however, it seems like the strongest correlations are type pairs e&g and e&i. Perhaps you might try recoding utterance type (e.g., merge e & i since their coefficients seem similar anyway)? Best & let us know if this helps, Roger -- Roger Levy Email: [EMAIL PROTECTED] Assistant Professor Phone: 858-534-7219 Department of Linguistics Fax: 858-534-4789 UC San Diego Web: http://ling.ucsd.edu/~rlevy _______________________________________________ R-lang mailing list [email protected] https://ling.ucsd.edu/mailman/listinfo.cgi/r-lang
