# Interpreting fitted vs residual plots for binomial general linear mixed effect models?

I am carrying out analysis of a large seedlings dynamics dataset (20258 seedlings from 7467 plots) which I am unable to share for privacy reasons, as it is not my dataset. In this analysis I am trying to determine how several explanatory variables (stem length, functional group, conspecific and heterospecific density, and the interactions between functional groups and density) affect the probability of survival for a given seedling - using the glmer() function in lme4. Before using the model, I scaled all continuous variables using the scale() function and made sure all non-continuous variables were listed as factors, and played around with optimizer settings and iteration number to get rid of some standard optimizer warnings.

The model is structured like this:

themod <- glmer(survival ~ stemlength + group + conspecific density +
heterospecific density + group:conspecifics + group:heterospecifics
+group:stemlength + (1|plot) + (1 + conspecifics + heterospecifics|species),
family=binomial(link = "logit"), data = dataset,
control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e5)))


I am trying out ways of selecting these models beyond AIC values, dropterm analysis or R squared values. One residual vs fitted diagnostic plot i have found used for glmms is found at this link: