My data has 264 subjects with their serial measurements. Event is death and sataset has 67 dead, 197 alive observations.
I'm trying to model repeated x and y measurements along with age and gender characteristics via Mixed - Effects Logistic Regression Model. My R code is below:
mel <- glmer(status ~ x + y + age + gender + (1 | time)+ (1|id), data = mydata, family = binomial, control = glmerControl(optimizer = "bobyqa"))
When I use this code, I encounter overfitting (I'm not sure this word is the best one to explain the situation). I mean, before modelling I have 67 events, after modelling, I have again 67 events, as predicted probabilities suggest.
On the other hand, when I try to model these repeated values via this other code below, it gave me an error like this:
mel <- glmer(status ~ x + y+age+gender+ (1 | time), data = mydata, family = binomial, control = glmerControl(optimizer = "bobyqa"))
Warning message: In checkConv(attr(opt, "derivs"), optpar,ctrl=controlpar,ctrl=controlcheckConv, : Model is nearly unidentifiable: very large eigenvalue - Rescale variables?
I'd like to know, if I'm missing something or doing it wrong.