[R] Logistic Regression using glm

[Thomas Lumley]

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.

 	-thomas

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: mth_man at yahoo.com
>
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Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle