[R] How does glm(family='binomial') deal with perfect sucess?

[Mike Lawrence]

Hi all,

Trying to understand the logistic regression performed by glm (i.e. when 
family='binomial'), and I'm curious to know how it treats perfect 
success. That is, lets say I have the following summary data

	x=c(1,2,3,4,5,6)
	y=c(0,.04,.26,.76,.94,1)
	w=c(100,100,100,100,100,100)

where x is y is the probability of success at each value of x, 
calculated across w observations. When I use glm

	my.glm.obj=glm(y~x,family='binomial',weights=w)

the regression comes out fine, but if I try what I understand to be the 
equivalent lm procedure (i.e. fitting a straight line to the logit 
transformed y values):

	my.lm.obj=lm(qlogis(y)~x,weights=w)

I get an error because, of course, logit(1) = log(1/0) = log(Inf) = Inf
(similarly, logit(0) = log(0/1) = log(0) = -Inf).

I'd be very interested to see how glm deals with these extremes.

Cheers,

Mike


-- 
Mike Lawrence
http://artsweb.uwaterloo.ca/~m4lawren

"The road to wisdom? Well, it's plain and simple to express:
Err and err and err again, but less and less and less."
- Piet Hein