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

[Duncan Murdoch]

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