[R] Confidence Bounds on QQ Plots?

[Spencer Graves]

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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