From: Peter Dalgaard <p.dalgaard_at_biostat.ku.dk>

Date: Mon, 14 Jul 2008 23:35:53 +0200

Date: Mon, 14 Jul 2008 23:35:53 +0200

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.)

-- O__ ---- Peter Dalgaard ุster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard_at_biostat.ku.dk) FAX: (+45) 35327907 ______________________________________________ R-help_at_r-project.org mailing list 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 - 21:54:51 GMT

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