-----Original Message-----
From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-
project.org] On Behalf Of Cueva, Jorge
Sent: Tuesday, July 30, 2019 10:34 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] glmer.nb - interaction interpretation
Dear all
I hope to get support for interpreting a model. First, I am assessing
the natural regeneration in a dry forest. The design has 12 clusters
and each cluster includes 3 open and 3 fenced plots (a total of 36
open plots and 36 fenced plots), the open plots are separate from the excluded plots by only 20 meters.
I want to know if livestock grazing affects the abundance of
regeneration, for this we collected excrements of animals, but a
single sample of excrements affects both the open and the fence plot.
Of all the models tested, the best was:
glmer.nb(Ind ~ 1 + Equine * Treat + SPrec + Cattle + (1|Cluster), data
= BaseOb2, family=poisson, verbose=FALSE,
glmerControl(optimizer="bobyqa", optCtrl = list(maxfun = 2e5)))
Ind = number of individuals
Equine = weight of equines excrements (horses + donkeys) Treat =
treatment (open and exclusion plots) SPrec = seasonal precipitation
Cattle = weight of cattle excrements Cluster = cluster was used as
random predictor because the samples were nested in the cluster.
My issue is when I want to interpret the effect of the predictors.
Here are the results
Fixed effects:
Estimate Std. Error z value
Pr(>|z|)
(Intercept) 3.170153 0.246584 12.856 <
2e-16 ***
Equine 0.926521 0.233079 3.975
7.03e-05 ***
Treatopen -0.009898 0.068965 -0.144
0.885875
SPrec 0.390747 0.078133 5.001
5.70e-07 ***
Cattle -0.365988 0.184748 -1.981
0.047589 *
Equine:Treatopen -0.989678 0.274040 -3.611
0.000305 ***
It can be seen that the independent effect of Equine is significantly
positive and that of Treatopen non-significantly negative.
Interpretation of these would be easy, but my issue is the
Equine:Treatopen interaction. Why is the effect of Equine first
positive and then in the interaction negative? What does that mean?
Very grateful in advance.
Jorge Cueva Ortiz
PhD Candidate
Technical University of Munich
01631327886
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