incubating models

library (tidyverse)

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library(ggplot2)
library(lme4)

Loading required package: Matrix

Attaching package: 'Matrix'

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    expand, pack, unpack

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

Incubating Model

m2 <- glmer(formula = incubating~temp+day_of_experiment+infected+total_alive+(1|parent), family = "poisson", data = behavior)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?

# Warning: Model is nearly unidentifiable: very large eigenvalue
plot(allEffects(m2))

vif(m2)

             temp day_of_experiment          infected       total_alive 
         1.008934          1.663081          1.072261          1.578918 

Model 2.1: everything except random effect

m2.1 <- glm(formula = incubating~temp+day_of_experiment+infected+total_alive, family = "poisson", data = behavior)
plot(allEffects(m2.1))

vif(m2.1)

             temp day_of_experiment          infected       total_alive 
         1.016913          1.554293          1.113577          1.429696 

Assess whether or not to keep random effect with dAIC

ICtab(m2,m2.1)

     dAIC df
m2    0.0 6 
m2.1 13.9 5 

# Keep random effect of parent colony, dAIC is lower 

Model 2.2: everything except total_alive

m2.2 <- glmer(formula = incubating~temp+day_of_experiment+infected+(1|parent), family = "poisson", data = behavior)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?

# Warning: Model is nearly unidentifiable: very large eigenvalue
plot(allEffects(m2.2))

Model 2.3: everything except infected

m2.3 <- glmer(formula = incubating~temp+day_of_experiment+total_alive+(1|parent), family = "poisson", data = behavior)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?

# Warning: Model is nearly unidentifiable: very large eigenvalue
plot(allEffects(m2.3))

Model 2.4: everything except day_of_experiment

m2.4 <- glmer(formula = incubating~temp+infected+total_alive+(1|parent), family = "poisson", data = behavior)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.00256368 (tol = 0.002, component 1)

#Warning: Model failed to converge with max|grad| = 0.00256368 (tol = 0.002, component 1)
plot(allEffects(m2.4))

Check these with performance

compare_performance(m2,m2.1,m2.2,m2.3,m2.4, 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
----------------------------------------------------------------------------------------------------------------------------
m2   | glmerMod | 1.632 | 1.000 |    -1.572 |           0.023 |       0.999 |        0.999 |       0.988 |            91.03%
m2.2 | glmerMod | 1.674 | 1.000 |    -1.602 |           0.023 |    7.05e-17 |     7.12e-17 |    9.18e-16 |            39.33%
m2.1 |      glm | 1.645 | 1.198 |    -1.586 |           0.023 |    9.44e-04 |     9.53e-04 |       0.012 |            30.05%
m2.4 | glmerMod | 1.691 | 1.000 |    -1.614 |           0.023 |    8.63e-24 |     8.71e-24 |    1.12e-22 |            24.78%
m2.3 | glmerMod | 1.707 | 1.000 |    -1.616 |           0.022 |    1.32e-25 |     1.33e-25 |    1.72e-24 |            14.29%

#Model 2 (the one with everything) appears to be best