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
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 eigenvalueplot(allEffects(m2))
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 eigenvalueplot(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 eigenvalueplot(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))
#Model 2 (the one with everything) appears to be best
Make incubating counts into proportions, redo best model (Model 2)
incubatingprop = (behaviorprop$incubating/behaviorprop$total_alive)behaviorprop<-cbind(behaviorprop,incubatingprop)m2.22<-glmer(formula = incubatingprop~temp+day_of_experiment+infected+total_alive+(1|parent), family ="binomial", data = behaviorprop)
Warning in eval(family$initialize, rho): non-integer #successes in a binomial
glm!
boundary (singular) fit: see help('isSingular')
m2.22
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: incubatingprop ~ temp + day_of_experiment + infected + total_alive +
(1 | parent)
Data: behaviorprop
AIC BIC logLik deviance df.resid
566.6928 597.6205 -277.3464 554.6928 1274
Random effects:
Groups Name Std.Dev.
parent (Intercept) 0
Number of obs: 1280, groups: parent, 6
Fixed Effects:
(Intercept) temp day_of_experiment infected
7.93764 -0.32277 0.06016 -1.30031
total_alive
-0.14376
optimizer (Nelder_Mead) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warnings
plot(allEffects(m2.22))
Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Vector of incubating yes/no
behaviorprop$notincubating <- behaviorprop$total_alive-behaviorprop$incubatingm2.5<-glmer(formula =cbind(incubating,notincubating)~temp+day_of_experiment+infected+total_alive+(1|parent), family ="binomial", data = behaviorprop)summary(m2.5)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: cbind(incubating, notincubating) ~ temp + day_of_experiment +
infected + total_alive + (1 | parent)
Data: behaviorprop
AIC BIC logLik deviance df.resid
4247.3 4278.2 -2117.6 4235.3 1274
Scaled residuals:
Min 1Q Median 3Q Max
-5.5903 -0.8304 -0.3939 0.7511 6.6508
Random effects:
Groups Name Variance Std.Dev.
parent (Intercept) 0.04905 0.2215
Number of obs: 1280, groups: parent, 6
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.235389 0.388888 5.748 9.02e-09 ***
temp -0.149077 0.004639 -32.138 < 2e-16 ***
day_of_experiment 0.030602 0.002309 13.253 < 2e-16 ***
infected -0.769842 0.060602 -12.703 < 2e-16 ***
total_alive 0.088870 0.033422 2.659 0.00784 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) temp dy_f_x infctd
temp -0.465
dy_f_xprmnt -0.477 0.009
infected -0.168 0.120 0.230
total_alive -0.900 0.131 0.490 0.059
