I'm trying to fit logistic regression with formula like this:
mod <- glmer(response ~ factor1+factor2+numeric1+numeric2+numeric3+numeric4 +(1|factor3), data=myDataset,family = binomial, control=glmerControl(optimizer="bobyqa"))
Factor1 and factor2 are categorical variables with (5 and 2 categories). Factor3 is id of subject. The rest of predictors are numeric variables - all of them scaled with scale().
I am getting following error/warning:
Warning messages: 1: Some predictor variables are on very different scales: consider rescaling 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.406353 (tol = 0.001, component 1) 3: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio - Rescale variables?
When I am trying to fit the model without factor1 and factor2 variables, model fits without complain, so I am assuming that problem is in my factor predictors. But I have no idea how to fix it. Should I recode my factor variables to dummy variables myself and then scale them? Will it help? Any idea will be very appreciated.