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

[Zhenguang Cai]

Thanks for all the advice and comments. I think I can also follow John's 
advice by pooling the two experiments together (which I did) and see the 
factor I am interested in will improve the model fit when added second. 
  Is that OK?

Zhenguang

T. Florian Jaeger wrote:
> well, than you have one null result and one result =). It's not the 
> strongest case, but at least you can argue that one of your experiments 
> seems to suggests that it is indeed one of the predictors (rather than 
> the other) that's driving the effect.
> 
> crucially, for the result to be what you probably are looking for, the 
> factor you're interested in must be the one that if added /second 
> /still improves the model significantly. 
> 
> Florian
> 
> On Fri, Jun 25, 2010 at 5:19 PM, Zhenguang Cai <s0782345@sms.ed.ac.uk 
> <mailto:s0782345@sms.ed.ac.uk>> wrote:
> 
>     yes, I did two cross-language/dialect structural priming studies,
>     with the same design and the same materials. In one experiment,
>     Mandarin was the response language in one experiment and Cantonese
>     was the response language in the other(primes were either in
>     Mandarin and Cantonese in both experiments).
> 
> 
>     Zhenguang
> 
>     T. Florian Jaeger wrote:
> 
>         Dear Zhenguang,
> 
>         what do your mean by Experiment 1 and 2? You have two different
>         data sets?
> 
>         Florian
> 
>         On Fri, Jun 25, 2010 at 9:12 AM, Zhenguang Cai
>         <s0782345@sms.ed.ac.uk <mailto:s0782345@sms.ed.ac.uk>
>         <mailto:s0782345@sms.ed.ac.uk <mailto:s0782345@sms.ed.ac.uk>>>
>         wrote:
> 
>            Dear Professor Trueswell,
> 
>            Thanks for the advice. I did that and found that P2 can be
>         subsumed
>            by P1 but not the other way round. I think that means something.
> 
>            My further question is that we always at least have to
>         determine 1)
>            whether P2 can be subsumed by P1 (i.e., whether the addition
>         of P2
>            can significantly improve model fit) and 2) whether P2 can be
>            subsumed by P1 (i.e., whether the addition of P2 can
>         significantly
>            improve model fit). Is that correct?
> 
>            Zhenguang
> 
>            John Trueswell wrote:
> 
>                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 <mailto:s0782345@sms.ed.ac.uk>
>         <mailto:s0782345@sms.ed.ac.uk <mailto: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
> 
>                    --
>                    The University of Edinburgh is a charitable body,
>         registered in
>                    Scotland, with registration number SC005336.
> 
> 
> 
> 
>            --     The University of Edinburgh is a charitable body,
>         registered in
>            Scotland, with registration number SC005336.
> 
> 
> 
>     -- 
>     The University of Edinburgh is a charitable body, registered in
>     Scotland, with registration number SC005336.
> 
> 

-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.