From: Duncan Murdoch <murdoch_at_stats.uwo.ca>

Date: Mon, 14 Jul 2008 17:24:24 -0400

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 14 Jul 2008 - 21:32:13 GMT

Date: Mon, 14 Jul 2008 17:24:24 -0400

markleeds_at_verizon.net wrote:

> In Julian Faraway's text on pgs 117-119, he gives a very nice, pretty

*> simple description of how a glm can be thought of as linear model
**> with non constant variance. I just didn't understand one of his
**> statements on the top of 118. To quote :
**>
**> "We can use a similar idea to fit a GLM. Roughly speaking, we want to
**> regress g(y) on X with weights inversely proportional
**> to var(g(y). However, g(y) might not make sense in some cases - for
**> example in the binomial GLM. So we linearize g(y)
**> as follows: Let eta = g(mu) and mu = E(Y). Now do a one step expanation
**> , blah, blah, blah.
**>
**> Could someone explain ( briefly is fine ) what he means by g(y) might
**> not make sense in some cases - for example in the binomial
**> GLM ?
**>
*

I don't know that text, but I'd guess he's talking about the fact that
the expected value of a binomial must lie between 0 and N (or the
expected value of X/N, where

X is binomial from N trials, must lie between 0 and 1).

Similarly, the expected value of a gamma or Poisson must be positive, etc.

Duncan Murdoch

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 14 Jul 2008 - 21:32:13 GMT

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