Dear Spencer et al., The simulated envelopes in the car package are for studentized residuals from linear models, and the original reference is indeed to Atkinson's 1985 book. I don't see why the same approach -- really a parametric bootstrap -- shouldn't be applicable more generally, though shouldn't be necessary for independently sampled observations. Finally, the qq.plot() function in car calculates point-wise confidence envelopes based on the standard errors of order statistics for an independent sample from the reference distribution.
I hope that this helps, John > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Spencer Graves > Sent: Tuesday, June 01, 2004 4:49 PM > To: Pikounis, Bill > Cc: R Help > Subject: Re: [R] Confidence Bounds on QQ Plots? > > Thanks to Uwe Ligges, Andy Liaw, and Bill Pikounis for 3 > useful replies. I had seen the description in "S > Programming", but forgot where I had seen it. When I > couldn't find it in MASS, I got confused. > I will also check John Fox's work. > > Thanks again. > Best Wishes, > Spencer Graves > > Pikounis, Bill wrote: > > >Spencer, > >Venables & Ripley's S Programming (2000) book comprehensively covers > >"Simulation envelopes for normal scores plots" in Section 7.3, pages > >161 - 163. The Atkinson "Plots, Transformations, and Regression" > >(1985) book is cited. > > > >The V & R example and discussion, as usual, is very > informative on both > >the programming and data analysis fronts. > > > >Hope that helps, > >Bill > > > >---------------------------------------- > >Bill Pikounis, Ph.D. > > > >Biometrics Research Department > >Merck Research Laboratories > > > > > > > >>-----Original Message----- > >>From: [EMAIL PROTECTED] > >>[mailto:[EMAIL PROTECTED] On Behalf Of > Spencer Graves > >>Sent: Tuesday, June 01, 2004 2:36 PM > >>To: R Help > >>Subject: [R] Confidence Bounds on QQ Plots? > >> > >> > >> What's the current best wisdom on how to construct confidence > >>bounds on something like a normal probability plot? > >> > >> I recall having read a suggestion to Monte Carlo > something like > >>201 simulated lines with the same number of points, then sort the > >>order statistics, and plot the 6th and 196th of these. [I > use 201 not > >>200 because quantile(1:201, c(0.025, 0.975)) = 6 and 196 while > >>quantile(1:200, c(0.025, 0.975)) = 5.975 and 11.025.] I > think I know > >>how to do this, but before I code it, I'd like to ask two > questions on > >>this issue: > >> > >> 1. Where can I find this in the literature? I > didn't find it > >>where I thought it was, nor in anyplace else that seemed obvious to > >>me, but I don't think I made it up and I'd like to give > credit where > >>credit it due. > >> > >> 2. Are there better alternatives available, > especially if the > >>distribution is a compound mixture that is easily simulated > but not so > >>easily characterized analytically? > >> > >> Thanks, > >> spencer graves > >> > >>______________________________________________ > >>[EMAIL PROTECTED] mailing list > >>https://www.stat.math.ethz.ch/mailman/listinfo/r-help > >>PLEASE do read the posting guide! > >>http://www.R-project.org/posting-guide.html > >> > >> > >> > >> > > > >______________________________________________ > >[EMAIL PROTECTED] mailing list > >https://www.stat.math.ethz.ch/mailman/listinfo/r-help > >PLEASE do read the posting guide! > >http://www.R-project.org/posting-guide.html > > > > > > > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
