On May 21, 2012, at 10:58 PM, Steve Taylor wrote:
Is there a way to tell glm() that rows in the data represent a
certain number of observations other than one? Perhaps even
fractional values?
Using the weights argument has no effect on the standard errors.
Compare the following; is there a way to get the first and last
models to produce the same results?
data(sleep)
coef(summary(glm(extra ~ group, data=sleep)))
coef(summary(glm(extra ~ group, data=sleep,
weights=rep(10L,nrow(sleep)))))
Here's a reasonably simple way to do it:
coef(summary(glm(extra ~ group, data=sleep[ rep(10L,nrow(sleep)), ] )))
--
David.
sleep10 = sleep[rep(1:nrow(sleep),10),]
coef(summary(glm(extra ~ group, data=sleep10)))
coef(summary(glm(extra ~ group, data=sleep10,
weights=rep(0.1,nrow(sleep10)))))
My reason for asking is so that I can fit a model to a stacked
multiple imputation data set, as suggested by:
Wood, A. M., White, I. R. and Royston, P. (2008), How should
variable selection be performed with multiply imputed data?.
Statist. Med., 27: 3227-3246. doi: 10.1002/sim.3177
Other suggestions would be most welcome.
_______________________________________________
Steve Taylor
Biostatistician
Pacific Islands Families Study
Faculty of Health and Environmental Sciences
AUT University
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David Winsemius, MD
West Hartford, CT
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
