Kevin E. Thorpe [EMAIL PROTECTED] writes:
Hamilton, Cody 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
Hamilton, Cody wrote:
After a little digging, it turns out that fit.mult.impute will allow
fitter = glm, so previous suggestions regarding modeling cbind(y,n) as
an outcome will work fine. Thanks!
Also lrm can easily handle your setup using the weights argument.
Frank
Cody Hamilton,
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
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.
Hamilton, Cody 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
Thank you for the suggestions! I am interested in using lrm because I
am not sure that glm will interact with other functions from Hmisc (e.g.
aregImpute, fit.mult.impute, etc).
Cody Hamilton, Ph.D
Institute for Health Care Research and Improvement
Baylor Health Care System
(214) 265-3618
:[EMAIL PROTECTED] On Behalf Of Hamilton, Cody
Sent: Friday, June 16, 2006 1:32 PM
To: r-help@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
After a little digging, it turns out that fit.mult.impute will allow
fitter = glm, so previous suggestions regarding modeling cbind(y,n) as
an outcome will work fine. Thanks!
Cody Hamilton, Ph.D
Institute for Health Care Research and Improvement
Baylor Health Care System
(214) 265-3618
