glm(weights) and standard errors
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.taylor at 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:dwinsemius at comcast.net] Sent: Tuesday, 22 May 2012 3:37p To: Steve Taylor Cc: r-help at 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.
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