From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>

Date: Sat 26 Aug 2006 - 03:17:04 EST

}

ans$r.squared <- mss/(mss + rss)

Date: Sat 26 Aug 2006 - 03:17:04 EST

On Fri, 25 Aug 2006, Charles wrote:

> Hello all,

*> I am using the function lm to do my weighted least
**> square regression.
**>
**> model<-lm(Y~X1+X2, weight=w)
**>
**> What I am confused is the r.squared.
*

What r.squared? There is no r.squared in that object, but it is calculated by the summary method.

> It does not seem that the r.squared for the weighted

*> case is an ordinary 1-RSS/TSS.
**> What is that precisely?
*

Precisely that, with weights in the SS. The code is

r <- z$residuals f <- z$fitted w <- z$weights if (is.null(w)) { mss <- if (attr(z$terms, "intercept")) sum((f - mean(f))^2) else sum(f^2) rss <- sum(r^2) } else { mss <- if (attr(z$terms, "intercept")) { m <- sum(w * f/sum(w)) sum(w * (f - m)^2) } else sum(w * f^2) rss <- sum(w * r^2) r <- sqrt(w) * r

}

ans$r.squared <- mss/(mss + rss)

That's the great thing about R: you can answer your own question by reading the code for yourself.

> Is the r.squared measure comparable to that obtained

*> by the ordinary least square?
**>
**> <I also notice that
**> model$res is the unweighted residual while
**> summary(model)$res is the weighted residual>
*

Yes, as documented with added emphasis in ?summary.lm .

-- Brian D. Ripley, ripley@stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch 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.Received on Sat Aug 26 04:32:23 2006

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