[R-lang] High collinearity in logit linear mixed effects modelling

[Zhenguang Cai]

Dear R-language people,

I realized that this is probably a question that has been frequently 
asked already, so sorry for spam to some people.

I found high correlation between two predictors (P1 and P2) (r  = .8). 
So following Florian's advice, I did model comparisons to try to exclude 
one of the predictors. However, I am not sure whether I did things in 
the right way.

Step 1 (to determine whether P2 can be subsumed by P1)

M0<- lmer(Data~1+(1|Subject)+(1|Item),family='binomial')
M1<- lmer(Data~P1+(1|Subject)+(1|Item),family='binomial')
M2 <- lmer(Data~P1+P2+(1|Subject)+(1|Item),family='binomial')

anova (M0, M1)
anova (M1, M2)


Step 1 (to determine whether P1 can be subsumed by P2)

M0<- lmer(Data~1+(1|Subject)+(1|Item),family='binomial')
M1<- lmer(Data~P2+(1|Subject)+(1|Item),family='binomial')
M2 <- lmer(Data~P2+P1+(1|Subject)+(1|Item),family='binomial')

anova (M0, M1)
anova (M1, M2)


In Experiment 1, I found P2 can be subsumed by P1 but not the other way 
round.

However, in Experiment 2, I found P1 and P2 can be subsumed by each 
other. How to resolve this?


Thanks,

Zhenguang

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