Dear R Users,

I was hoping for some help with a recurrent error message in lmer. I am trying to model the effect of temperature on metabolic rate in animals (response = int.length) at different temperatures (mean.sst), with repeated measurements on the same individuals (random effect = female). Ideally I would make a random slope and intercept model where the rate can change differently with temperature for different individuals:

model<-lmer(int.length~mean.sst+(mean.sst|female))

However, I get the following warning message:

Warning message:
Estimated variance-covariance for factor 'female' is singular in: `LMEoptimize<-`(`*tmp*`, value = list(maxIter = 200L, tolerance = 1.49011611938477e-08,
summary(model)

Linear mixed-effects model fit by REML
Formula: int.length ~ mean.sst + (mean.sst | female)
   AIC   BIC logLik MLdeviance REMLdeviance
 155.4 164.5  -72.7      142.8        145.4
Random effects:
 Groups   Name        Variance   Std.Dev.   Corr
 female   (Intercept) 6.8459e-10 2.6165e-05
          mean.sst    6.8169e-10 2.6109e-05 -0.065
 Residual             1.3634e+00 1.1676e+00
number of obs: 46, groups: female, 18
Fixed effects:
            Estimate Std. Error t value
(Intercept)  48.8249     6.5895   7.409
mean.sst     -1.3609     0.2518  -5.406
Correlation of Fixed Effects:
         (Intr)
mean.sst -1.000





If I try and run just a random intercepts model I get similar problems:



model2<-lmer(int.length~mean.sst+(1|female))

Warning message: Estimated variance for factor 'female' is effectively zero in: `LMEoptimize<-`(`*tmp*`, value = list(maxIter = 200L, tolerance = 1.49011611938477e-08,



I have tried disabling PQL iterations  using control = list(usePQL = FALSE, msVerbose=TRUE), following Douglas Bates' recommendation on the mailing list archives but I still get a similar message. Does this mean that the variance among subjects is too close to zero for estimation of the random effects? I compared the random effects model to a linear model with just lm(int.length ~ mean.sst) using a likelihood ratio test and got p = 1.0 (which is always suspicious). It would actually make sense for there to be negligible variation among subjects in their response to temperature, however I am concerned that I am making a fundamental error somewhere along the line.



I would greatly appreciate any suggestions you may have.



Best regards



Sam Weber



University of Exeter, UK.




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