I am a biologist in the middle of their PhD journey and I'm constantly learning new things about statistics but there are some things that are difficult for me to understand.
Until now I had dealt with linear mixed models and although it required some time for me to understand how they worked, the models I have worked with were not very complex. But right now, I am dealing with completely new stuff, specifically with a logistic regression with a random factor (GLMM) and I must say that I find these types of models very very difficult to understand because they are indeed complex and I don't have a strong formation on this topic.
I asked a question recently in this webpage about logistic regression, but doubts keep appearing in my mind. I don't mean to sound like I haven't searched in the internet, in the R packages documentation because I really have! I am writing this question to know if I can get a straight answer to resolve my doubts.
So, I work with a colony of wild birds, where some individuals are floaters (they dont have territory) and where some individuals defend breeding resources (territory, partner, nest etc...). I've built a logistic regression model with a link logit and 3 explanatory variables: body condition, degree of spotted plumage and length of ornamental feathers. Our goal is to know if these phenotypic features can predict an individual becoming a floater or not.
logistic <- glmer(status ~ condition + plumage + feather_length+(1|capture_year), data = data, family = "binomial") summary(logistic) Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod'] Family: binomial ( logit ) Formula: status ~ condition + mot_suma + feather_length + (1 | capture_year) Data: data2 AIC BIC logLik deviance df.resid 239.4 255.2 -114.7 229.4 171 Scaled residuals: Min 1Q Median 3Q Max -2.2089 -1.0149 0.5999 0.7930 1.8357 Random effects: Groups Name Variance Std.Dev. capture_year (Intercept) 0.1558 0.3947 Number of obs: 176, groups: capture_year, 6 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.89966 2.01298 0.944 0.34532 condition 0.50889 0.18690 2.723 0.00647 ** mot_suma 0.46022 1.10952 0.415 0.67829 feather_length -0.04145 0.05569 -0.744 0.45667 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) condtn mot_sm condition 0.179 mot_suma -0.081 -0.057 fethr_lngth -0.991 -0.177 0.064
I checked the residuals with the package DHARMa and everything looks fine. But in this point I don't know how to continue.
I've read that the estimates that the summary gives are not really precise and that I have to perform a LRT test. But I get overwhelmed when looking for information to do it. I used the function
drop1(logistic, test="Chisq") but if I must be sincere, I am not really sure about what I am doing. For this reason I am asking for help to know which should be the next step I should follow.
My mentor has never done logistic regression in R so I can say that I am alone on this and that I really need help, please.