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

bogdan romocea br44114 at gmail.com
Fri Jun 16 20:39:00 CEST 2006

Not sure about your data set, but if you have some kind of
(weighted/stratified) sample of hospitals you need to pay special
attention. Survey data violates the assumptions of the classical
linear models (infinite population, identically distributed errors
etc) and needs to be analyzed differently. In SAS, it's wrong to throw
should be used instead. In R, take a look at the survey package. For
details check

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Hamilton, Cody
> Sent: Friday, June 16, 2006 1:32 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] modeling logit(y/n) using lrm
> 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.).
> Cody Hamilton, Ph.D
> Institute for Health Care Research and Improvement
> Baylor Health Care System
> (214) 265-3618
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