[R] Logistic Regression using glm

[Daniel Pick]

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