[R-lang] trouble with mixed-model

[Francisco Torreira]

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.

Thanks in advance,
Francisco Torreira

-- 
Francisco Torreira
PhD Candidate in Hispanic Linguistics
University of Illinois at Urbana-Champaign

https://netfiles.uiuc.edu/ftorrei2/www/index.html
tel: (+1) 217 - 778 8510