[R-sig-ME] glmer.nb - interaction interpretation

[Cueva, Jorge]

Dear John thank you so much for your support. I got the chart, it looks nice and the interpretation is easier!

Greetings

Jorge Cueva Ortiz

-----Original Message-----
From: Fox, John <jfox using mcmaster.ca> 
Sent: Tuesday, July 30, 2019 22:16
To: Cueva, Jorge <ga59lij using mytum.de>; r-sig-mixed-models using r-project.org
Subject: RE: glmer.nb - interaction interpretation

Dear Jorge,

You might try the predictorEffects() function in the effects package to visualize the interaction. In particular, and as a start, plot(predictorEffects(mod)), where mod is the model you wish to examine.

I hope this helps,
 John

-----------------------------------------------------------------
John Fox
Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
Web: https://socialsciences.mcmaster.ca/jfox/



> -----Original Message-----
> From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces using r-
> project.org] On Behalf Of Cueva, Jorge
> Sent: Tuesday, July 30, 2019 10:34 AM
> To: r-sig-mixed-models using 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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