Hello, I find your recommended procedure not very sensible but:
a) sum(fit$residuals^2)/fit|$d||f.residual | b) This is a statistical question but, for instance, in the case of repeated mesures. But you'd use the precision (1/variance) of the original data, computed by groups of regressor. Hope this helps, Rui Barradas Em 13-10-2012 20:07, Eiko Fried escreveu: > Hello. > > I'm am trying to follow a recommendation to deal with a dependent variable > in a linear regression. > > I read that, due to the positive trend in my dependent variable residual vs > mean function, I should > 1) run a linear regression to estimate the standard deviations from this > trend, and > 2) run a second linear regression and use 1 / variance as weight. > > These might be terribly stupid questions, but: > a) Where do I find the standard deviations from this trend in the lm > output? Except for coefficients I only see residual std error, DF, R^2, F > and p values. > b) Why exactly does one adjust a linear regression by a 1/variance weight, > and in what cases? > > Thanks > T > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. [[alternative HTML version deleted]] ______________________________________________ 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.
