feeding models

library (tidyverse)

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library(ggplot2)
library(lme4)

Loading required package: Matrix

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library(glmmTMB)

Warning in checkDepPackageVersion(dep_pkg = "TMB"): Package version inconsistency detected.
glmmTMB was built with TMB version 1.9.1
Current TMB version is 1.9.2
Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)

library(effects)

Loading required package: carData
lattice theme set by effectsTheme()
See ?effectsTheme for details.

library(car)

Attaching package: 'car'

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library(bbmle)

Loading required package: stats4

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library(performance)

behavior_23Feb23 <- read.csv("behavior_23Feb23.csv")

behavior <- behavior_23Feb23

Feeding Model 1

m1 <- glmer(formula = feeding~temp+day_of_experiment+infected+total_alive+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m1)) 

vif(m1) #This checks for colinearity, we want the values below 2.5 

             temp day_of_experiment          infected       total_alive 
         1.038569          1.118786          1.029644          1.118236 

Model 1.1: everything except random effect

m1.1 <- glm(formula = feeding~temp+day_of_experiment+infected+total_alive, family = "poisson", data = behavior)
plot(allEffects(m1.1))

vif(m1.1)

             temp day_of_experiment          infected       total_alive 
         1.093894          1.178129          1.090494          1.095426 

Compare dAIC

ICtab(m1,m1.1)

     dAIC df
m1   0.0  6 
m1.1 4.9  5 

# keep random effect

Model 1.2: keep random effect, everything else except total_alive

m1.2 <- glmer(formula=feeding~temp+day_of_experiment+infected+(1|parent),family="poisson", data=behavior)
plot(allEffects(m1.2))

Model 1.3: keep everything except infected

m1.3 <- glmer(formula = feeding~temp+day_of_experiment+total_alive+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m1.3))

Model 1.4: keep everything except day of experiment

m1.4 <- glmer(formula = feeding~temp+infected+total_alive+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m1.4))

Check these with performance

#According to this m1.3 is the best overall, but m1.2 has the lowest AIC 
compare_performance(m1,m1.1,m1.2,m1.3,m1.4, rank=TRUE)

Warning: Following indices with missing values are not used for ranking:
  R2_conditional, R2_marginal, ICC, R2_Nagelkerke

# Comparison of Model Performance Indices

Name |    Model |  RMSE | Sigma | Score_log | Score_spherical | AIC weights | AICc weights | BIC weights | Performance-Score
----------------------------------------------------------------------------------------------------------------------------
m1.3 | glmerMod | 1.070 | 1.000 |    -1.233 |           0.023 |       0.385 |        0.387 |       0.772 |            86.76%
m1   | glmerMod | 1.066 | 1.000 |    -1.233 |           0.023 |       0.542 |        0.540 |       0.083 |            85.02%
m1.4 | glmerMod | 1.064 | 1.000 |    -1.236 |           0.023 |       0.024 |        0.024 |       0.048 |            49.58%
m1.2 | glmerMod | 1.083 | 1.000 |    -1.237 |           0.023 |       0.002 |        0.002 |       0.005 |            31.46%
m1.1 |      glm | 1.076 | 1.049 |    -1.241 |           0.023 |       0.046 |        0.046 |       0.092 |             9.14%