[R] How to deal with multicollinearity in mixed models (with lmer)?

[willow1980]

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-help@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,
-- 
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