[R-lang] mixed logit models, coding the effects and understanding the parameters

[David Reitter]

Hi Maria,

good to hear from you. Just briefly for lack of time:

On Apr 7, 2009, at 5:28 AM, Maria Carminati wrote:
> Generalized linear mixed model fit using Laplace
> Formula: poresp ~ primec * nounrepc + (1 | subject) + (1 | item)
>   Data: verbdiff

> THERE WERE OVERALL 872 SUCCESSES AND 302 FAILURES IN THE EXPT, SO ODDS
> SHOULD BE 872/302=2.88 or (in probability space) .74/.26 = 2.85;
> THIS SHOULD GIVE A LOG OF ODDS OF APPROX 1.05, BUT THE INTERCEPT
> PREDICTED BY THE MODEL  IS MUCH HIGHER (1.66)

You have a random intercept for subjects (and one for items) fitted  
there...
I would fit a fixed effects model and check that first.  I'm not sure  
if, given the groups defined for your random terms, all data points  
are weighted equally (as they are in your max likelihood probability  
above).
(Finally, by coding your binary factors as -0.5,0.5, you don't  
necessarily center the means at 0 - unless your design is balanced,  
what I almost suspect.  If their means aren't 0, you wouldn't expect  
the fitted intercept to work out the way you're suggesting.)

Also, what happens if you take the non-significant terms out?

 > primec:nounrepc  -0.2138     0.3224  -0.663    0.507

Pity this one didn't work.  Where these low-frequency nouns?  Unless  
your design controlled their frequency, you could try adding terms for  
the noun log-frequency (from a corpus)...


Best
- David

--
Dr. David Reitter
Department of Psychology
Carnegie Mellon University
http://www.david-reitter.com

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