# glm(.) / summary.glm(.); [over]dispersion and returning AIC..

**Martin Maechler
**
Martin Maechler <maechler@stat.math.ethz.ch>

*Wed, 4 Feb 1998 17:00:16 +0100*

>>>>>* "Jim" == Jim Lindsey <jlindsey@luc.ac.be> writes:
*
MM>> S has adopted the concept that the glm(.) model is always the same,
MM>> the dispersion being an orthogonal nuisance parameter,
MM>> which the user should specify in
MM>> summary(....) , i.e.,
MM>> summary.glm(object, dispersion = NULL, correlation=FALSE, ..)
MM>> ^^^^^^^^^^^^^^^^^
Jim> But in fact it is unity for binomial and poisson so some action must
Jim> be taken in summary. The orthogonality is a characteristic of
Jim> exponential dispersion models.
Sure; the above meant:
- dispersion *is* an optional parameter to summary.glm(.).
- it has a default depending on the model fitted (e.g. =1 for binomial)
- if the user specifies a different value, that one is used ...
MM>> [but wouldn't the dispersion also be used in predict.glm(..., se=TRUE) ?]
Jim> Dispersion does not affect predictions, only their precision.
Exactly, and the point is that ``predict( ... , se = TRUE )
^^^^^^^^^^
asks for 'standard errors' !
it is short for 'se.fit' and actually
not (yet) available in R, but in S-plus predict.lm & predict.glm.
The question remains (actually an S-plus only question for the current R):
How to get standard errors for predicted values
WITH user-specified / non-standard estimated dispersion value?
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !) To: r-devel-request@stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._