Dear list members,
I am running GLMMs with count data, Laplace approximation, poisson family,
using glmer {lme4}, the last version of R and R studio in windows platform.
When fitting my final models, I run an anova to look at the "significance"
of a term inclusion (random effect term), of course, this does not apply
for random effects, but at least it gives me differences in DFs and whether
the models are significantly different or not.
It basically tells me that the least parsimonious model is the one with
the lowest AIC value. As you can see below the difference in AIC values is
pretty big.
Data: db.e
Models:
gwi: abundance ~ census * avail.surface + (1 | tree) + (1 | spp)
g: abundance ~ census * avail.surface + (1 | tree) + (1 | spp) + (1 |
spp:tree)
Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
gwi 12 22342.0 22442.9 -11159.0
g 13 9702.5 9811.8 -4838.3 12641 1 < 2.2e-16 ***
Then I run a qqplot on residuals vs. fitted values for each model and I
can see that the more parsimonious model (*gwi*) is the one with the better
fit (the points are pretty well alined across a straight line); whereas in
the other model (*g*) the points are in a curved line.
Would this be because random crossed effects should not be included as an
interaction term (like in the last model [g]) (Johnson & Omland, 2004)? or
I am overfiting?
My intuition tells me I should go for the most parsimonious model, since
the graphical checking works. But, I wonder if there any advise you can
give me to improve the fit of this model?. There is still a lot of variance
unexplained there, the model with the random effect interaction term
"spp:tree" has a variance of 59.87 sd. 7.73, in comparison to the model
with only tree and spp (2.45 sd. 1.55 & 3.5 sd. 1.90).
Greetings and thanks in advance for your time,
Glenda Mendieta-Leiva
PhD candidate
University of Oldenburg
PS. Johnson, J. B. and Omland, K. S. 2004. Model selection in ecology and
evolution. Trends Ecol. Evol. 19: 101-108.