On Feb 6, 2012, at 10:57 , Achim Zeileis wrote: > On Mon, 6 Feb 2012, James Annan wrote: > > > The summary() shows under "Residuals" the contributions to the objective > function, i.e. sqrt(1/w) (y - x'b) in the notation above. > > However, by using the residuals extractor function you can get the unweighted > residuals: > > residuals(lm(y~x,weights=c(.01,.01,.01,.01))) > >> The uncertainties on the parameter estimates, however, have *not* changed, >> which seems very odd to me. > > lm() interprets the weights as precision weights, not as case weights. > > Thus, the scaling in the variances is done by the number of (non-zero) > weights, not by the sum of weights. > >> The behaviour of IDL is rather different and intuitive to me: >> >> IDL> vec=linfit(x,y,sigma=sig,measure_errors=[1,1,1,1]) >> IDL> print,vec,sig >> -5.00000 5.00000 >> 1.22474 0.447214 >> >> IDL> vec=linfit(x,y,sigma=sig,measure_errors=[10,10,10,10]) >> IDL> print,vec,sig >> -5.00000 5.00000 >> 12.2474 4.47214 > > This appears to use sandwich standard errors.
Actually, I think the issue is slightly different: IDL assumes that the errors _are_ something (notice that setting measure_errors to 1 is not equvalent to omitting them), R assumes that they are _proportional_ to the inverse weights, and proportionality to c(.01,.01,.01,.01) is not different from proportionality to c(1,1,1,1)... There are a couple of ways to avoid the use of the estimated multiplicative dispersion parameter in R, one is to extract cov.unscaled from the summary, another is to use summary.glm with dispersion=1, but I'm not quite sure how they interact with weights (and I don't have the time to check just now.) -- Peter Dalgaard, Professor Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ 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.
