I might just also note that what Eiko is doing is essentially forward stepwise selection and would recommend he do some searching on the perils of using stepwise (especially forward) variable selection methods.
Might also want to look into multiple imputation approaches relative to dealing with missing data. Regards, Marc Schwartz On Apr 24, 2012, at 9:14 AM, R. Michael Weylandt wrote: > Take a look at nobs() > > Michael > > On Tue, Apr 24, 2012 at 10:05 AM, Eiko Fried <tor...@gmail.com> wrote: >> I have a dataset with plenty of variables and lots of missing data. As far >> as I understand, R automatically removes subjects with missing values. >> >> I'm trying to fit a mixed effects model, adding covariate by covariate. I >> suspect that my sample gets smaller and smaller each time I add a >> covariate, because more and more lines get deleted. >> >> Is there a way of displaying hTow many subjects are "left" in each analysis? >> >> Thanks >> E ______________________________________________ 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.
