[R-sig-ME] Troubleshooting glmmTMB
m@tteo@@b@ @end|ng |rom gm@||@com
Sun Apr 19 15:59:12 CEST 2020
Hope to be clear enough since this is going to be my fist quest.
I was running 3 sets of models with glmer, each set of models had a
different response variable representing number of 3 different species of
birds counted during transects. No problem for the first 2 sets of models
with glmer, which I then compared with AICc to get the best model. For the
third species, by far the less counted during the transect, I found that
the model was underdispersed (~0.6). I decided to switch to genpois glmmTMB
model which can deal with underdispersed models. The response is not zero
inflated according to DHARMa testZeroInflation. I used the same approach
i.e. a set of model with different combinations of predictors which i would
then compare. The issue is that for few models i get the warning: extreme
or very small eigenvalues detected. Continuous predictors are already
scaled. I then tried to run the function I found in the glmmTMB
troubleshooting vignette (Example 3) to detect the parameters that
contribute to the small eigenvalues but i get a warning:
Error in diagnose_vcov(mod2) : can't analyze vcov
In addition: Warning message:
In diagnose_vcov(mod2) : analyzing Hessian, not vcov
Do you have any idea how I can overcome the problem?
I also have another question. In the models I have an observerID random
factor with 2 levels, and I know the random factors should have at least
5-6 levels. Do you think is ok to include them in the model even though it
creates a singular fit warning (in glmmTMB i get the non-positive-definite
Hessian matrix warning)?
Looking forward to hear from you.
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models