[R] statistics question about a statement in julian faraway's "extending the linear model with R" text

From: <markleeds_at_verizon.net>
Date: Mon, 14 Jul 2008 15:47:44 -0500 (CDT)

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 ? Thanks.

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