I am running a GLMM in R in lme4 package, the outcome variable is binary and the 10 fixed effects are a mix of categorical and continuous variables. The models have three random-effects. I am using DHARMa to check for the GLMM assumptions.
simulateResiduals(fittedModel = cm5, asFactor=T, plot = T, quantreg=T,1000) It doesn't show that I have big misspecification problems however the residuals are not uniform, and the KS-test and the dispersion test is significant.
I recalculate the residuals at each random effect levels and gave the same issues. I ran the same model but this time I categorised all the continuous fixed effects, the DRAHMa output is much better and better meet the assumptions.
I am not a fan of categorising continuous variables and I don't want to lose information to meet the assumptions. But at the same time, I don't want biased estimates because of not meeting the assumptions. Please advise, which option to sacrifice. Thank you