For the binomial the standard link function is the logit: g(y) = log( y/(1-y) )
In the binomial glm model the observed y values are 0, or 1 which give g(0) = -Inf and g(1) = Inf. Switching to g(mu) with 0 < mu < 1 results in finite values which are much easier for the computer to work with. Hope this helps, -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare [EMAIL PROTECTED] (801) 408-8111 > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of > [EMAIL PROTECTED] > Sent: Monday, July 14, 2008 2:48 PM > To: [EMAIL PROTECTED] > Subject: [R] statistics question about a statement in julian > faraway's "extending the linear model with R" text > > 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. > > ______________________________________________ > R-help@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. > ______________________________________________ R-help@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.
