Re: [R] Logistic Regression using glm

From: Thomas Lumley <>
Date: Wed 12 Oct 2005 - 03:13:13 EST

One of these is modelling the mean of the logit of p, the other is modelling the logit of the mean of p. They aren't the same.


On Tue, 11 Oct 2005, Daniel Pick wrote:

> Hello everyone,
> I am currently teaching an intermediate stats.
> course at UCSD Extension using R. We are using
> Venables and Ripley as the primary text for the
> course, with Freund & Wilson's Statistical Methods as
> a secondary reference.
> I recently gave a homework assignment on logistic
> regression, and I had a question about glm. Let n be
> the number of trials, p be the estimated sample
> proportion, and w be the standard binomial weights
> n*p*(1-p). If you perform
> output <- glm(p ~ x, family = binomial, weights = n)
> you get a different result than if you perform the
> logit transformation manually on p and perform
> output <- lm(logit(p) ~ x, weights = w),
> where logit(p) is either obtained from R with
> qlogis(p) or from a manual computation of ln(p/1-p).
> The difference seems to me to be too large to be
> roundoff error. The only thing I can guess is that
> the application of the weights in glm is different
> than in a manual computation. Can anyone explain the
> difference in results?
> Daniel Pick
> Principal
> Daniel Pick Scientific Software Consulting
> San Diego, CA
> E-Mail:
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Thomas Lumley			Assoc. Professor, Biostatistics	University of Washington, Seattle

______________________________________________ mailing list PLEASE do read the posting guide! Received on Wed Oct 12 03:17:17 2005

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