I have a data set which involves 30 binomial absence/presences totalled for a ratio out of 1, which is the total score of a test out of 30 marks. The data requires fitting one of my predictor variables as a random effect,
glmer1 <- glmer(formula= cbind(NumberPresent, NumberAbsent) ~ Year + Class + Gender + (1|School), data = Framework1, family = "binomial"(link="logit"))
AIC 4112.8 BIC 4135.9 logLik -2051.4 deviance 4102.8 df.resid 732
Obviously the data is still overdispersed after the inclusion of a random effect. My question is: Should i use a GLM with quasibinomial? Or stay with
glmer with the random effect? Or is there a better way to do it?
Also: I get this error returned when i try to run the glmer.
Model is nearly unidentifiable: very large eigenvalue - Rescale variables? Model is nearly unidentifiable: large eigenvalue ratio - Rescale variables?
Is this because of the nature of my variables?