[EMAIL PROTECTED] 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.)
--
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
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