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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