From: Daniel Pick <mth_man_at_yahoo.com>

Date: Wed 12 Oct 2005 - 02:21:39 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Oct 12 02:24:30 2005

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

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Oct 12 02:24:30 2005

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