Peter Dalgaard <[EMAIL PROTECTED]> writes:
> Re. the smoothed residuals, you do need to be careful about the > smoother. Some of the "robust" ones will do precisely the wrong thing > in this context: You really are interested in the mean, not some > trimmed mean (which can easily amount to throwing away all your > cases...). Here's an idea: > > x <- runif(500) > y <- rbinom(500,size=1,p=plogis(x)) > xx <- predict(loess(resid(glm(y~x,binomial))~x),se=T) > matplot(x,cbind(xx$fit, 2*xx$se.fit, -2*xx$se.fit),pch=20) > > Not sure my money isn't still on the splines, though. Doh. You might also want to make sure that the residuals are of a type that can be _expected_ to have mean zero. Apparently, the default deviance residuals do not have that property, whereas response residuals do. I did check that loess (as opposed to lowess!) does a plain least-squares based fitting by default, but I didn't think to check what kind of residuals I was looking at. Serves me right for posting way beyond my bedtime... Anyways, you're probably better off believing Frank and not me in these matters. -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
