# [R] Confidence Bounds on QQ Plots?

Spencer Graves spencer.graves at pdf.com
Tue Jun 1 23:49:16 CEST 2004

```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: r-help-bounces at stat.math.ethz.ch
>>[mailto:r-help-bounces at stat.math.ethz.ch] 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
>>
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>>
>>
>>
>
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