A number of people seem not to have caught my point about null
deviances.

My philosophy is that statistical software should only print out
information that is correct in any context in which it will be used.
If the software cannot differentiate among the contexts, it should
never freely print out those things; the user should have to ask for
the information and will do so only when the context is right.

glm in R prints out all sorts of garbage that is wrong or very
misleading in many of the contexts in which it is used. I use glms
primarily for log linear models (perhaps 80%). In that context, the
following are wrong (1-3) or misleading (number 4)
1. null deviance,
2. stars,
3. p-values,
4. no labels on binary factor variables.
I teach my students that software can produce a lot of wrong
information and give these as examples. I have severely penalized the
students for using any of them on the exam last month. Unfortunately,
the last (4) is more subtle. In spite of warning them (social science
students) repeatedly of this incoherence during the course, several
completely misinterpreted their results on the exam because of the
lack of category labels on binary factor variables.

Several people have contacted me asking about my experience teaching
with R. After almost two years, perhaps it is time for a summary. I
teach categorical data, stochastic processes, glm, and repeated
measures with R. Using such software in front of students is always a
big risk, as with the offset bug (and I still insist that it is a bug
because correct initial estimates are essential to the IWLS
approach) a while back. You have always to be prepared to improvise
when something goes wrong, as it always will with beta software.
(Yesterday, with 63.2 on Windows, changing the plot line type made
subesequent plots have dashed borders etc...)

For those who teach either glm or categorical data and have GLIM (or,
I assume, Genstat) available, I would strongly recommend that they use
them and not R. I shall do the same if and when an NT version of GLIM
appears (if I can find the money somewhere to pay for it) but for the
moment our computer people cannot get the DOS version to work on their
NT server. GLIM is pedagogically far superior simply because students
have to know what they want and explicitly ask for it. No irrelevant
garbage printed out.

On the other hand, for stochastic processes and repeated measures, R
is clearly much superior. However, I do not use any of the R modelling
functions, only my own libraries.

Yesterday I taught what is almost certainly my last glm class ever, as
next year I shall be giving a nonlinear modelling course. Because I
did that course using R, I did not have the Aitkin and Clayton GLIM
macros for censored survival data available a few weeks back. So I
used my gnlr function. The students immediately saw that it could do
all that glm could do and more. In subsequent homework, almost all
dropped glm and used gnlr for everything in spite of the facts that I
explicitly told them to use glm and that they had to give initial
estimates for the parameters! This surprised even me but says
something very clear about glm.

Teaching only involves toy examples. For consulting work, where data
are always unbalanced and messy, I would never use R glm but always
GLIM if it is strictly a glm-type problem. I do use R a lot for
consulting but only with my own data classes and library functions.

Perhaps it is useful to compare R to Linux. Linux strives to do better
than Unix. R blindly tries to clone S. My position for the past three
years on this list has been that R should aim to do as Linux. Besides
source code availability and fast bug fixes, it has at least two major
factors going for it: scoping rules and "TeX inside". Is it not time
that a project is undertaken to rewrite the high-level functions in R
rather than S-Plus style so that they are fast and not
memory-consuming, i.e. useful for real data problems?

I hope that these opinions are of some use. Jim
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