[R] modeling logit(y/n) using lrm

[Rolf Turner]

Cody Hamilton, Ph.D, wrote:

> I have a dataset at a hospital level (as opposed to the patient
> level) that contains number of patients experiencing events (call
> this number y), and the number of patients eligible for such events
> (call this number n).  I am trying to model logit(y/n) = XBeta.  In
> SAS this can be done in PROC LOGISTIC or GENMOD with a model
> statement such as: model y/n = <predictors>;.  Can this be done using
> lrm from the Hmisc library without restructuring the dataset so that
> for each hospital there is one row with y = 1 and one row with y = 0
> and then using the weight option in lrm to weight these two responses
> by the number of 'successes' and 'failures' for that hospital,
> respectively?  I would like to avoid the restructuring, and I
> understand that the use of the weight function is not compatible with
> a lot of the validation functions available in Hmisc (validate,
> bootcov, etc.).

	Why do you need lrm()?  Is there something I'm missing?

	As far as I can tell you can simply do

	glm(cbind(y,n-y) ~ <predictors>,family=binomial,data=<data>)

	where ``<data>'' has columns named ``y'' ``n'' and whatever
	the predictors are called.

				cheers,

					Rolf Turner
					rolf at math.unb.ca