Dear R users, I have a problem with multicollinearity in mixed models and I am using lmer in package lme4. From previous mailing list, I learn of a reply "http://www.mail-archive.com/r-h...@stat.math.ethz.ch/msg38537.html"; which states that if not for interpretation but just for prediction, multicollinearity does not matter much. However, I am using mixed model to interpret something, so I am wondering if there is a suitable method to deal with this problem in lmer. My model is: model2<-lmer(sur_prop~(kidc+I(kidc^2)+I(kidc^3))*(byear_c+I(byear_c^2) +I(byear_c^3)+I(byear_c^4))+(byear_c|Studyparish),family=binomial) This is the maximum model and I have not begun to simplify it. The model is used to interpret the pattern how a mother's cohort year and total number of children will affect average survival rate of her children. Kids and byear_c have been centered, so the problem of correlation between linear term and polynomial terms (quadratic, cubic et al) has been solved to some degree. A still serious problem with this model is that number of children is correlated with cohort year, as we know the fact that number of children declines with time. So, would you please give a suggestion to deal with collinearity between kids and byear? Thank you very much for helping! Best regards, -- View this message in context: http://www.nabble.com/How-to-deal-with-multicollinearity-in-mixed-models-%28with-lmer%29--tp24994095p24994095.html Sent from the R help mailing list archive at Nabble.com.
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
