This is a simple and sensible question that does not have a simple answer. It's a research issue, and you should go to the literature to see what approaches seem appropriate.
HOWEVER, one simple descriptive approach -- which, however, may have important statistical flaws -- is to run loess on the absolute values (or,perhaps, their sqare roots)of the raw residuals. I think this might be asymptotically reasonable with a fixed smoothing window, but small sample behavior could well be awful. Carefully done simulations might help determine this. As I said, no simple answer. Perhaps others with real expertise might comment/correct -- maybe offlist, as this seems to be wandering from R. -- Bert On Thu, Nov 11, 2010 at 5:08 AM, Oliver Frings <oliverfri...@googlemail.com> wrote: > Hi Josh, > > many thank's for your reply. I tried to read up on this more and to be frank > I got a bit confused about the exact definition of residual standardization. > It occurs to me that different people have different definitions and that it > can be done with and without the leverage of each point. Anyhow, the way you > did it seems correct to me! My only problem is now that it assumes the same > standard error for each point. My data have definitely different standard > deviations at different points. So I was wondering if there is a way to do > it that accounts for the different standard deviations at different points? > > Many thanks! > > /Oliver > > On Wed, Nov 10, 2010 at 8:21 PM, Joshua Wiley <jwiley.ps...@gmail.com>wrote: > >> Hi Oliver, >> >> As a warning, I may be missing something too. I did not see something >> explicit in base R or MASS. In a quick scan of the fourth edition of >> the MASS book, I did not read anything that it is >> illogical/unreasonable to try to find standardized residuals (but my >> knowledge of local regression approaches nil). With that background, >> I proceeded to blithely scavenge from other functions until I came up >> with this: >> >> loess.stdres <- function(model) { >> res <- model$residuals >> s <- sqrt(sum(res^2)/(length(res) - model$enp)) >> stdres <- res/(sqrt(1 - hat(res)) * s) >> return(stdres) >> } >> >> ## now for a half-baked check >> >> ## fit linear model and local regression >> cars.lm <- lm(dist ~ speed, cars) >> cars.lo <- loess(dist ~ speed, cars) >> >> ## these seem somewhat similar >> rstandard(cars.lm) >> c(scale(residuals(cars.lm))) >> >> ## these seem somewhat similar too >> loess.stdres(cars.lo) >> c(scale(cars.lo$residuals)) >> >> >> Cheers, >> >> Josh >> >> >> >> On Wed, Nov 10, 2010 at 9:24 AM, Oliver Frings >> <oliverfri...@googlemail.com> wrote: >> > Hi all, >> > >> > I'm trying to apply loess regression to my data and then use the fitted >> > model to get the *standardized/studentized residuals. I understood that >> for >> > linear regression (lm) there are functions to do that:* >> > * >> > * >> > fit1 = lm(y~x) >> > stdres.fit1 = rstandard(fit1) >> > studres.fit1 = rstudent(fit1) >> > >> > I was wondering if there is an equally simple way to get >> > the standardized/studentized residuals for a loess model? BTW >> > my apologies if there is something here that I'm missing. >> > >> > All the best, >> > * >> > * >> > *Oliver * >> > >> > [[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. >> > >> >> >> >> -- >> Joshua Wiley >> Ph.D. Student, Health Psychology >> University of California, Los Angeles >> http://www.joshuawiley.com/ >> > > [[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. > -- Bert Gunter Genentech Nonclinical Biostatistics ______________________________________________ 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.