[R] statistics question about a statement in julian faraway's "extending the linear model with R" text
Peter Dalgaard
p.dalgaard at biostat.ku.dk
Mon Jul 14 23:35:53 CEST 2008
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 ?
>
Note that he does say "roughly speaking". The intention is presumably
that if y is a vector of proportions and g is the logit function,
proportions can be zero or one, but then their logits would be minus or
plus infinity. (However, that's not the only thing that goes wrong; the
model for g(E(Y)) is linear, the expression for E(g(y)) in general is not.)
--
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