I'm confused (I bet David is too). First and last models are "the same", what do SE's have to do with anything ?
naive <- glm(extra ~ group, data=sleep) imputWrong <- glm(extra ~ group, data=sleep10) imput <- glm(extra ~ group, data=sleep10,weights=rep(0.1,nrow(sleep10))) lapply(list(naive,imputWrong,imput),anova) sapply(list(naive,imuptWrong,imput),function(x) vcov(x)[1,1]/vcov(x)[2,2]) # or another way to see it (adjust for the DF) coef(summary(naive))[2,2] - sqrt(198)/sqrt(18) * coef(summary(imput))[2,2] coef(summary(naive))[2,2] - sqrt(198)/sqrt(18) * coef(summary(imputWrong))[2,2] Are you sure you are interpreting Wood et al. correctly ? (I haven't read it, this is not rhetorical) On Wed, May 23, 2012 at 7:49 PM, Steve Taylor <steve.tay...@aut.ac.nz> wrote: > Re: > coef(summary(glm(extra ~ group, data=sleep[ rep(1:nrow(sleep), 10L), ] ))) > > Your (corrected) suggestion is the same as one of mine, and doesn't do what > I'm looking for. > > > -----Original Message----- > From: David Winsemius [mailto:dwinsem...@comcast.net] > Sent: Tuesday, 22 May 2012 3:37p > To: Steve Taylor > Cc: r-help@r-project.org > Subject: Re: [R] 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. >> > > ______________________________________________ > 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. ______________________________________________ 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.