[R-lang] SPR experiment: using lmer, transforming data, collinearity, and using a covariable

[Roger Levy]

Claire Delle Luche wrote:
> Dear R-lang users,
> 
> I try to analyse a self paced reading experiment where I have two fixed variables (Relativiser, Attachment), two random variables (Participant, ItemNbr), and one covariable (BiasValue). The dependant variable is RT, reading time region by region.
> 
> My main aim is to remove the variance induced by BiasValue on Attachment, but I did not find any code for that.
> 
> My procedure is the following, elaborated from bits and tips: 
> - fit the data distribution, suggesting an inverse square root transform, rather than the classical log transform (on all RTs)
> - exclude deviant participants
> - calculate residual RTs (quite common in SPR experiments)
> - check for collinearity
> - then run the analysis region by region, with BiasValue as a covariate
> - obtain HPD intervals (it fails)

Dear Claire,

Here are some thoughts:

* I think it's more standard to calculate residual RTs by constructing 
subject-specific linear regressions rather than a mixed-effect linear 
regression pooling all the data.  Also, you usually want to use *all* 
the regions (not just the critical region/regions) in constructing this 
regression; maybe throw out the first and last regions of the sentence. 
I can't tell whether you're doing this.

* The problem with HPDinterval might be specific to the current state of 
lme4.  What error do you get?  Can you replicate it with a tiny toy 
dataset that you could post to the list?

* This may incite controversy, but I personally would suggest being 
careful about residualizing and analyzing transformed RTs.  The reason 
for this is that the transform changes the interpretation of the linear 
regression (used to calculate residuals) and of any interactions in your 
analysis.

* Is this a designed & balanced experiment?  If so, there shouldn't be 
problems with collinearity.

* You might consider having more random effects than intercepts in your 
mixed-effects regression.  I believe this is an open issue.

* I'm not sure what criteria you want to use to exclude deviant 
participants.  Could you explain in greater detail?

Hope this helps.

Best

Roger

-- 

Roger Levy                      Email: rlevy at ucsd.edu
Assistant Professor             Phone: 858-534-7219
Department of Linguistics       Fax:   858-534-4789
UC San Diego                    Web:   http://ling.ucsd.edu/~rlevy