-----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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