Standardized Pearson residuals

Peter Dalgaard: 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, ...

This would remedy what I have long judged a deficiency in summary.glm().
The information is important for diagnostic purposes.  One should not have
to fit a model with a quasi error, or suss out how to calculate the Pearson
chi square from the glm model object, to discover that the information in the
model object is inconsistent with simple binomial or poisson assumptions.

John Maindonald             email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473    fax  : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.
http://www.maths.anu.edu.au/~johnm

On 15/03/2011, at 10:00 PM, r-devel-request at r-project.org wrote:

From: Brett Presnell <presnell at stat.ufl.edu>
Date: 15 March 2011 2:40:29 PM AEDT
To: peter dalgaard <pdalgd at gmail.com>
Cc: r-devel at r-project.org
Subject: Re: [Rd] Standardized Pearson residuals



Thanks Peter.  I have just a couple of minor comments, and another
possible feature request, although it's one that I don't think will be
implemented.

peter dalgaard <pdalgd at gmail.com> writes:

On Mar 14, 2011, at 22:25 , Brett Presnell wrote:

Is there any reason that rstandard.glm doesn't have a "pearson" option?
And if not, can it be added?

Probably... I have been wondering about that too. I'm even puzzled why
it isn't the default. Deviance residuals don't have quite the
properties that one might expect, e.g. in this situation, the absolute
residuals sum pairwise to zero, so you'd expect that the standardized
residuals be identical in absolute value

y <- 1:4
r <- c(0,0,1,1)
c <- c(0,1,0,1)
rstandard(glm(y~r+c,poisson))

       1          2          3          4 
-0.2901432  0.2767287  0.2784603 -0.2839995 

in comparison,

i <- influence(glm(y~r+c,poisson))
i$pear.res/sqrt(1-i$hat)

       1          2          3          4 
-0.2817181  0.2817181  0.2817181 -0.2817181 

The only thing is that I'm always wary of tampering with this stuff,
for fear of finding out the hard way why thing are the way they
are....

I'm sure that's wise, but it would be nice to get it in as an option,
even if it's not the default

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.

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, ...

BTW, as I go along I'm trying to collect a lot of the datasets from the
examples and exercises in the text into an R package ("icda").  It's far
from complete and what is there needed tidying up, but I hope to
eventually to round it into shape and put it on CRAN, assuming that
Agresti approves and that there are no copyright issues.

I think something along the following lines should do it:

rstandard.glm <-
function(model,
        infl=influence(model, do.coef=FALSE),
        type=c("deviance", "pearson"), ...)
{
type <- match.arg(type)
res <- switch(type, pearson = infl$pear.res, infl$dev.res)
res <- res/sqrt(1-infl$hat)
res[is.infinite(res)] <- NaN
res
}

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