# [R] Logistic Regression using glm

From: Daniel Pick <mth_man_at_yahoo.com>
Date: Wed 12 Oct 2005 - 02:21:39 EST

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@yahoo.com

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