问题
I am running several linear mixed models for an study about birds with the variable nest as a random variable. The thing is that in some of these models I get what is called 'singular fit': my nest random variable has a variance and st error of 0.00.
Some background: I am working with wild birds to see the effect of living in noisy environments on some oxidative stress parameters. For this, we took a blood sample for each of the nestlings of each nest to do the laboratory stuff. Because of the limited blood sample, some oxidative stress parameters couldn't be measured for every nestling.
model <- lmer(antioxidant_capacity~age+sex+clutch+zone+(1|nestID),
data=data, contrasts=list(sex=contr.sum, zon=contr.sum, clutch=contr.sum))
Then I get:
singular fit
This is the table:
REML criterion at convergence: 974.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.72237 -0.61737 0.06171 0.69429 2.88008
Random effects:
Groups Name Variance Std.Dev.
nestID (Intercept) 0 0.00
Residual 363 19.05
Number of obs: 114, groups: nido_mod, 46
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 294.5970 36.8036 109.0000 8.005 1.41e-12 ***
age -0.2959 3.0418 109.0000 -0.097 0.922685
clutch1 -0.5242 2.0940 109.0000 -0.250 0.802804
sex1 2.3167 1.8286 109.0000 1.267 0.207885
zone1 6.2274 1.7958 109.0000 3.468 0.000752 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) age clutch1 sex1
age -0.999
clutch1 0.474 -0.465
sex1 0.060 -0.054 -0.106
zone1 -0.057 0.061 -0.022 0.058
convergence code: 0
singular fit
I have read about singularity problems and if I have understood well, the singularity is related to overfitting. Could this be due to that for some response variables I have nests with only one nestling while there are nest with more nestlings? How can I solve this? Any recommendation?
Thank you, so much.
回答1:
In lmer, a singular fit could be caused by collinearity in fixed effects, as in any other linear model. That would need you to revise your model by removing terms. But in lmer, that (or a "boundary (singular) fit" warning) can also be also triggered in quite simple models when a random effect variance is estimated very near zero and (very loosely) the data is not sufficiently informative to drag the estimate away from the zero starting value.
The formal answer is broadly similar either way; drop terms that estimate as zero. And that remains sensible at least until you know which term is causing the problem. But there are times when a negligible variance is reasonably likely but you'd like to retain it in the model; for example because you're quite deliberately looking for intervals on possibly small variances or maybe doing multiple similar experiments and would prefer to extract all the variances consistently. If you're sure of what's going on, you can suppress these warnings via lmerControl, which can be set not to use the relevant tests. For example, you can include
control=lmerControl(check.conv.singular = .makeCC(action = "ignore", tol = 1e-4))
in your lmer call. That leaves in the default tolerance (which makeCC needs) but suppresses the singular fit test. (The default is action="warning", which runs the test and issues the warning).
回答2:
Are you actually interested in whether each of the fixed effects in your model has an effect? For example, age or sex may explain some of the variation, but perhaps you could include it as a random effect rather than a fixed effect. Changing it to a random effect (if that is rational) might address the over dispersion issue.
My interpretation of the singularity issue, which certainly could be incorrect, is that each of the combinations of your model only has one observation/measurement. Therefore, you may not have enough observations to include all of those variables as fixed effects.
来源：https://stackoverflow.com/questions/54597496/how-to-cope-with-a-singular-fit-in-a-linear-mixed-model-lme4