Hi Peter and others,
If it helps, I wrote a small function glm.scoretest() for the statmod
package on CRAN to compute score tests from glm fits. The score test for
adding a covariate, or any set of covariates, can be extracted very neatly
from the standard glm output, although you probably already know that.
Regards
Gordon
---------------------------------------------
Professor Gordon K Smyth,
NHMRC Senior Research Fellow,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
sm...@wehi.edu.au
http://www.wehi.edu.au
http://www.statsci.org/smyth
Date: Tue, 15 Mar 2011 12:17:46 +0100
From: peter dalgaard <pda...@gmail.com>
To: Brett Presnell <presn...@stat.ufl.edu>
Cc: r-devel@r-project.org
Subject: Re: [Rd] Standardized Pearson residuals
On Mar 15, 2011, at 04:40 , Brett Presnell wrote:
Background: I'm currently teaching an undergrad/grad-service course
from Agresti's "Introduction to Categorical Data Analysis (2nd edn)"
and deviance residuals are not used in the text. For now I'll just
provide the students with a simple function to use, but I prefer to
use R's native capabilities whenever possible.
Incidentally, chisq.test will have a stdres component in 2.13.0 for
much the same reason.
Thank you. That's one more thing I won't have to provide code for
anymore. Coincidentally, Agresti mentioned this to me a week or two
ago as something that he felt was missing, so that's at least two
people who will be happy to see this added.
And of course, I was teaching a course based on Agresti & Franklin:
"Statistics, The Art and Science of Learning from Data", when I realized
that R was missing standardized residuals.
It would also be nice for teaching purposes if glm or summary.glm had a
"pearsonchisq" component and a corresponding extractor function, but I
can imagine that there might be arguments against it that haven't
occured to me. Plus, I doubt that anyone wants to touch glm unless
it's to repair a bug. If I'm wrong about all that though, ...
Hmm, how would that work? If there was one, I'd worry that people would
start subtracting them which is usually not the right thing to do. I do
miss having a test on the residual deviance occasionally (even though it
is only sometimes meaningful), having to fit a saturated model
explicitly can be a bit silly. E.g. in this case (homogeneity of birth
rates):
anova(glm(births~month,poisson,data=bb), test="Chisq")
...
Df Deviance Resid. Df Resid. Dev P(>|Chi|)
NULL 11 225.98
month 11 225.98 0 0.00 < 2.2e-16 ***
anova(glm(births~1,poisson,data=bb), test="Chisq")
...
Df Deviance Resid. Df Resid. Dev P(>|Chi|)
NULL 11 225.98
Notice that the latter version gives me the correct deviance but no
p-value.
A better support for generic score tests could be desirable too. I
suspect that this would actually be the Pearson Chi-square in the
interesting cases.
--
Peter Dalgaard
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd....@cbs.dk Priv: pda...@gmail.com
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