stationary models

library (tidyverse)

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library(ggplot2)
library(lme4)

Loading required package: Matrix

Attaching 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

Attaching package: 'bbmle'

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library(performance)

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

behavior <- behavior_23Feb23

Stationary Model

m3 <- glmer(formula = stationary~temp+day_of_experiment+infected+total_alive+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m3)) 

vif(m3)

             temp day_of_experiment          infected       total_alive 
         1.038561          1.158002          1.048192          1.140449 

Model 3.1: everything except random effect

m3.1 <- glm(formula = stationary~temp+day_of_experiment+infected+total_alive, family = "poisson", data = behavior)
plot(allEffects(m3.1)) 

vif(m3.1)

             temp day_of_experiment          infected       total_alive 
         1.065553          1.166925          1.072500          1.111387 

Assess whether or not to keep random effect with dAIC

ICtab(m3,m3.1)

     dAIC  df
m3     0.0 6 
m3.1 137.4 5 

# keep it 

Model 3.2: everything except total_alive

m3.2 <- glmer(formula = stationary~temp+day_of_experiment+infected+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m3.2)) 

Model 3.3: everything except infected

m3.3 <- glmer(formula = stationary~temp+day_of_experiment+total_alive+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m3.3)) 

Model 3.4: everything except day_of_experiment

m3.4 <- glmer(formula = stationary~temp+infected+total_alive+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m3.4)) 

Performance check

compare_performance(m3,m3.1,m3.2,m3.3,m3.4, 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
----------------------------------------------------------------------------------------------------------------------------
m3   | glmerMod | 2.136 | 1.000 |    -2.088 |           0.023 |       1.000 |        1.000 |       1.000 |            88.78%
m3.2 | glmerMod | 2.153 | 1.000 |    -2.112 |           0.023 |    8.34e-14 |     8.42e-14 |    1.10e-12 |            41.71%
m3.3 | glmerMod | 2.163 | 1.000 |    -2.099 |           0.023 |    2.12e-06 |     2.14e-06 |    2.79e-05 |            37.19%
m3.4 | glmerMod | 2.237 | 1.000 |    -2.174 |           0.023 |    2.59e-48 |     2.61e-48 |    3.40e-47 |            28.57%
m3.1 |      glm | 2.215 | 1.389 |    -2.153 |           0.023 |    1.43e-30 |     1.45e-30 |    1.89e-29 |            12.78%

# Model 3 (original model with everything) works best