glm(weights) and standard errors
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