[R-lang] Re: Investigating random slope variance

Titus von der Malsburg malsburg@posteo.de
Tue Apr 8 03:41:07 PDT 2014


On 2014-04-07 Mon 23:16, Levy, Roger <rlevy@ucsd.edu> wrote:
> On Apr 7, 2014, at 8:09 AM, Titus von der Malsburg <malsburg@posteo.de> wrote:
>>
>>    http://users.ox.ac.uk/~sjoh3968/R/effect_of_shrinkage2.png
> 
> [...] Though I’m still surprised that there’s so much more shrinkage
> in the y direction than in the x direction, despite the fact that the
> random slope standard deviation is so much smaller than that of the
> random intercept.

Could this be due to the manipulation being between-subject?

>> To remind you of the original question: I wanted to know which items are
>> read significantly faster or slower in the manipulated condition.  Based
>> on the BLUPs, these are items 25 and 36.  
>
> So, hold on: are you interested in (i) for which items can you
> conclude with (1-p)% confidence that the total effect of the
> manipulation is significantly non-zero in a particular direction
> [item-average slope + item-specific slope], or (ii) for which items
> can you conclude that their idiosyncratic sensitivity to the
> manipulation, above and beyond the population-average sensitivity, is
> significantly non-zero in a particular direction [only item-specific
> slope]?

Since there is no significant main effect of the manipulation, (i) and
(ii) are the same.  Think of the experiment as a corpus study with an
experimental manipulation.  This manipulation speeds up some items and
slows down others.  Overall there is no slow-down or speed-up.

> Your concern about the correlation coefficient in the random-effects
> covariance matrix seems reasonable to me.  I don’t know how the new
> lme4’s ranef() function extracts confidence intervals, but if it does
> so conditional on the point estimate of the random-effects covariance
> matrix, then the standard caveat applies that this kind of approach
> fails to take into account uncertainty in that covariance matrix.  For
> this reason you might want to consider Bayesian inferential
> methods.  My textbook-in-progress has some examples of how you can set
> these up in JAGS, I think Shravan has pedagogical materials now that
> show how to do this in Stan, and there are R packages that may be
> useful for this more directly (e.g., MCMCglmm).  That way you can
> inspect the posterior on the correlation parameter.

That sounds like they way to go.  (Well, actually I should collect more
data but that's unfortunately not easily possible in this project.)  So
it's finally time to learn JAGS or Stan.  At least I can called it
"work" now instead of procrastination.

Thank you all for your responses.  I learned something.

  Titus



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