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.mes at cbs.dk Priv: PDalgd at gmail.com