fanning models

library (tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.3.0      ✔ stringr 1.5.0 
✔ readr   2.1.3      ✔ forcats 0.5.2 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(ggplot2)
library(lme4)

Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    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'

The following object is masked from 'package:dplyr':

    recode

The following object is masked from 'package:purrr':

    some

library(bbmle)

Loading required package: stats4

Attaching package: 'bbmle'

The following object is masked from 'package:dplyr':

    slice

library(performance)

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

behavior <- behavior_23Feb23

Fanning Model

m4 <- glmer(formula = fanning~temp+day_of_experiment+infected+total_alive+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m4))

vif(m4)

             temp day_of_experiment          infected       total_alive 
         1.016248          1.112137          1.018022          1.106488 

Model 4.1: everything except random effect

m4.1 <- glm(formula = fanning~temp+day_of_experiment+infected+total_alive, family = "poisson", data = behavior)
plot(allEffects(m4.1))

vif(m4.1)

             temp day_of_experiment          infected       total_alive 
         1.089218          1.142275          1.057377          1.081851 

Compare to see which is better with dAIC

ICtab(m4,m4.1)

     dAIC  df
m4     0.0 6 
m4.1 272.9 5 

# keep random effect 

Model 4.2: everything except total_alive

m4.2 <- glmer(formula = fanning~temp+day_of_experiment+infected+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m4.2))

Model 4.3: everything except infected

m4.3 <- glmer(formula = fanning~temp+day_of_experiment+total_alive+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m4.3))

Model 4.4: keep everything except day_of_experiment

m4.4 <- glmer(formula = fanning~temp+infected+total_alive+(1|parent), family = "poisson", data = behavior)
plot(allEffects(m4.4))

Compare model performance

compare_performance(m4,m4.1,m4.2,m4.3,m4.4, 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
----------------------------------------------------------------------------------------------------------------------------
m4   | glmerMod | 1.342 | 1.000 |    -1.226 |           0.023 |       0.945 |        0.944 |       0.564 |           100.00%
m4.3 | glmerMod | 1.343 | 1.000 |    -1.229 |           0.023 |       0.055 |        0.056 |       0.436 |            68.89%
m4.2 | glmerMod | 1.359 | 1.000 |    -1.242 |           0.023 |    5.84e-09 |     5.89e-09 |    4.59e-08 |            49.07%
m4.4 | glmerMod | 1.408 | 1.000 |    -1.269 |           0.023 |    1.91e-23 |     1.93e-23 |    1.50e-22 |            42.13%
m4.1 |      glm | 1.640 | 1.214 |    -1.346 |           0.023 |    5.23e-60 |     5.27e-60 |    4.11e-59 |             0.00%

# Original model is best- should I be skeptical of the performance score of 100%?