[R] singular variance-covariance warning in lmer

[Ista Zahn]

Hi Sam,
Just a stab in the dark here, but is your grouping variable really
female? What does

str(data.frame(mean.sst, female)

look like? How many levels does female have?

-Ista

On Thu, Oct 29, 2009 at 7:10 AM, Weber, Sam <Sam.Weber at exeter.ac.uk> wrote:
> 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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>
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-- 
Ista Zahn
Graduate student
University of Rochester
Department of Clinical and Social Psychology
http://yourpsyche.org