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

[John Trueswell]

Zhenguang,

If Experiment 1 and Experiment 2 are similar enough, you could combine
the data from the two experiments and model the entire set (keeping
Experiment as a predictor in the model, to see if that matters).

John Trueswell


On Thu, Jun 24, 2010 at 5:17 PM, Zhenguang Cai <s0782345@sms.ed.ac.uk> wrote:
> 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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