R help - May 2012 - glm(weights) and standard errors

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)))))
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

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
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
West Hartford, CT