David Firth wrote (in response to a question from Paul Johnson):
On the more general point: yes, if all that students need to know is
OLS, Poisson rate models and logistic regression, then GLM is overkill.
I couldn't agree less. The glm (not GLM!) framework gives a
Well, I really did _intend_ GLM when I wrote GLM, meaning the sort of theoretical thing that Paul described, involving the presentation to (political-science etc) students of the general (linear) exponential family. All that stuff is not needed for a proper understanding of the three models mentioned, and certainly those three models are all meaningful without it. Your second paragraph below is one that I wholeheartedly agree with though (except that it should be linear function of the parameters, not the predictors), and it seems to agree also with what I wrote in the second part of the paragraph which you quote above (the part that you cut) from my earlier reply.
David
coherence to the structure and changes a collection of ad hoc (and thereby essentially meaningless cook-book) techniques into a single meaningful technique:
A parameter (the mean) of a distribution is a transformation of a linear function of some predictors. One seeks to estimate the linear coefficients via maximum likelihood. In a broad array of circumstances the maximization can be carried out by the glm() function (using iteratively reweighted least squares). The process is quick and efficient and the notation is about as transparent as can be imagined.
cheers,
Rolf Turner [EMAIL PROTECTED]
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