I am using the glmer function from the lme4 package to model nest attendance as a function of temperature, temporal variables, and experimental treatment. My model is relatively complicated — it contains multiple polynomial terms and interactions. The model is specified as:
FullModel<-glmer(Attendance~poly(TemperatureS, 2, raw=FALSE)+CoverType+poly(IncDayS,2, raw=FALSE)+poly(TimeS,2,raw=FALSE)+poly(TemperatureS,2,raw=false)*CoverType+poly(TemperatureS,2,raw=FALSE)*poly(IncDayS,2, raw=FALSE)+(1|ID), data=Attendance, family=binomial)
- Attendance is binary (on vs. off the nest), coded as 0/1
- Temperature is numeric
- Incday= day of incubation, numeric
- Time is numeric
- CoverType is categorical with two levels
- ID is the identity of the bird the observation is from, categorical with 14 levels
All of the numeric variables (so temperature, day of incubation, and time) have been scaled using the rescale function from the arm package, which centers and divides by 2 standard deviations, per Gelman 2007. However, when I run the model, I get the following warning message:
Warning message: Some predictor variables are on very different scales: consider rescaling
Why am I getting this warning if the numeric predictors have already been rescaled? Given that they have been scaled, is it okay to ignore this warning, or will this affect my model results?
All thoughts are greatly appreciated!