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 ______________________________________________ R-help@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.
