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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.

Thank you.

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