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

[Martin Maechler]

>>>>> "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?
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