-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Daniel Pick
Sent: Tuesday, October 11, 2005 12:22 PM
To: r-help at stat.math.ethz.ch
Subject: [R] Logistic Regression using glm
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