[R-sig-ME] Interpretation of GLMM output in R
Yvonne Hiller
yvonne.hiller at hotmail.de
Fri Jul 31 15:42:21 CEST 2015
Dear Ben Bolker and list members,
I´am a PhD-student working on tropical plant-pollinator
interactions (the fig-fig wasp mutualism).
I have some difficulties with my analyses of my data
using lmer (family = Poisson). I have read a lot of threads and searched for
solutions in Zuur 2009, though it was not completely satisfying. I looked at
other papers using GLMM (espeacially for the fig-fig wasp mutualism), but there
were so many different ways in reporting and interpreting p-values, AICs etc.
Therefore, I would be very grateful, if you could go through my output and
answer my questions to hopefully fully understand GLMM. Thank you so much in
advance.
One of my questions is:
Have parasitoids (offspring) and the volume size an
effect on the number of pollinator offspring?
As pollinators are parasitized by parasitoids, I would
expect a negative impact on pollinator offspring. As the size of the fig fruits
might be an additionally factor (due to limited oviposition sites inside the fruit
or due to the ovipositor length of a parasitoid is to short to reach inner most
ovules galled by pollinators), I included it in the model.
I have poisson data (counts):
Pollinator
= pollinator offspring
Parasitoids = parasitoid offspring
volume = fig fruit size measurement
tree = Tree ID
I have data from trees in the forest and in the
farmland, as well as data of the rainy and dry season. I have chosen to perform
lmer for each season and habitat separately. For each tree, I have collected 10
fig fruits to count offspring of wasps at the trophic level (pollinator,
parasitoid) and to measure the size of the fruit (volume). As I have 10 fig
fruits per tree, I would use tree as a random effect, to account for unmeasured
variance between trees. I have found different approaches to that in
literature. For instance: We did not include‘ tree ’ and ‘
syconium ’ as random factors, because wasps were free to move between syconia
on the same tree, and between trees.
Therefore,
I am not sure if I have to use GLMM with trees as a random effect or if it’s
also possible to use GLM without the random effect.
The inclusion of
fig volume in this manner removed the need to use “fig” as an additional nested
factor within “tree”. Is that right?
Here, I
present the model with the random effect.
So, lets start with the model of the farmland during
the dry season:
fad <- lmer(Pollinator~Parasitoids+volume+(1|tree),family="poisson",verbose=TRUE)
summary (fad)
Generalized linear mixed model fit by the Laplace
approximation
Formula: Pollinator ~ Parasitoids + volume + (1 |
tree)
Data:
fad.data
AIC BIC logLik deviance
1166 1175
-578.9 1158
Random effects:
Groups
Name Variance Std.Dev.
tree (Intercept) 0.10634 0.3261
Number of obs: 80, groups: tree, 8
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept)
2.3167867 0.1632747 14.190
<2e-16 ***
Parasitoids -0.0046908
0.0021686 -2.163 0.0305 *
volume
0.0069525 0.0006108 11.383
<2e-16 ***
---
Signif.
codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05
‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) Prstds
Parasitoids -0.160
volume
-0.657 -0.107
1. Random effects: What does the Random
Effect table - the Variance, Std. Dev. and Intercept tells me?
So it says that the
between–tree variability is fairly large. But I don’t understand to what it
relates. Does it mean that pollinator offspring variance is high between
trees and might be overestimating the explanatory variables?
2. What can I
conclude from the model regarding the fixed effects and how to report about it
(with or without p-values, z-values, estimates)?
So of what I know is that
the p-value is only a guide and that it is rather a comparison of two models.
What are the two models and can I compare them.
4. What does the
correlation of fixed effects tells me?
5. Is it right
that the estimates tells me whether the relation of the fixed effects to
pollinator offspring is positive or negative?
6. Can I
calculate an effect size on each explanatory variable?
I would highly appreciate your feedback on this.
Thanks so much in advance.
Best wishes,
Yvonne Hiller
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models
mailing list