I'm new to the world of R and statistical modeling and struggling to find an appropriate way to visualize the results of a generalized linear mixed model with a negative binomial distribution (glmer.nb from the lme4 package). I'm using a dataset that looks something like this:
$ Year : Factor w/ 2 levels "2017","2018"
$ Reampled : Factor w/ 2 levels "No","Yes"
$ Habitat : Factor w/ 4 levels "MCF","MM","MMF","REGEN"
$ Site : Factor w/ 63 levels "MCF_001","MCF_002",...
$ Disturbed : Factor w/ 2 levels "Disturbed","Mature"
$ Species : Factor w/ 3 levels "MYLU","MYSE", "NoID"
$ Count : int 1 3 0 1 0 0 6 38 3 43 ...
$ Bat.Survey.Nights: int 4 4 4 5 5 5 6 6 6 4 ...
$ Avg.Snags : num -0.855 -0.855 -0.855 1.846 1.846 ...
$ Avg.Understory : num -0.00715 -0.00715 -0.00715 -0.94871 -0.94871 ...
$ Avg.Midstory : num -0.352 -0.352 -0.352 0.256 0.256 ...
$ Avg.Canopy : num -1.061 -1.061 -1.061 0.695 0.695 ...
$ Avg.Canopy.Cover : num -0.831 -0.831 -0.831 0.506 0.506 ...
$ Perc.Dec.Dom : num -0.493 -0.493 -0.493 -1.095 -1.095 ...
My model is based off of bat counts per site (with an offset to account for number of survey nights). Here, I am comparing vegetation between different bat species:
>nglmer.veg <- glmer.nb(Count ~ Avg.Snags + Avg.Understory*Species +
Avg.Midstory*Species + Avg.Canopy.Cover*Species + Perc.Dec.Dom +
offset(log(Bat.Survey.Nights)) + (1|Site),
data = insect.data)
>summary(glmer.veg)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: Negative Binomial(1.0954) ( log )
Formula: Count ~ Avg.Snags + Avg.Understory * Species + Avg.Midstory *
Species + Avg.Canopy.Cover * Species + Perc.Dec.Dom + offset(log(Bat.Survey.Nights)) + (1 | Site)
Data: insect.data
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
AIC BIC logLik deviance df.resid
1134.0 1187.4 -551.0 1102.0 191
Scaled residuals:
Min 1Q Median 3Q Max
-1.0407 -0.6660 -0.2981 0.4499 3.9381
Random effects:
Groups Name Variance Std.Dev.
Site (Intercept) 0.6728 0.8203
Number of obs: 207, groups: Site, 36
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.6367 0.2069 -3.077 0.002090 **
Avg.Snags 0.7330 0.2026 3.617 0.000298 ***
Avg.Understory 1.0821 0.2209 4.899 9.61e-07 ***
SpeciesMYSE -0.3875 0.2236 -1.733 0.083012 .
SpeciesNoID 1.4968 0.1998 7.490 6.90e-14 ***
Avg.Midstory 0.5031 0.2037 2.470 0.013522 *
Avg.Canopy.Cover -0.3813 0.2338 -1.631 0.102914
Perc.Dec.Dom 0.9980 0.2163 4.614 3.96e-06 ***
Avg.Understory:SpeciesMYSE -0.6613 0.2040 -3.241 0.001190 **
Avg.Understory:SpeciesNoID -0.5353 0.1992 -2.687 0.007205 **
SpeciesMYSE:Avg.Midstory -1.3015 0.2883 -4.514 6.36e-06 ***
SpeciesNoID:Avg.Midstory -0.4005 0.1864 -2.149 0.031660 *
SpeciesMYSE:Avg.Canopy.Cover 0.5597 0.2464 2.272 0.023114 *
SpeciesNoID:Avg.Canopy.Cover 0.5246 0.2196 2.389 0.016916 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
What is the best way to visualize these results?