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 0.507Pity 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
smime.p7s
Description: S/MIME cryptographic signature
_______________________________________________ R-lang mailing list [email protected] http://pidgin.ucsd.edu/mailman/listinfo/r-lang
