Hi, Berton:
thanks for getting back to me. 

I played around cor.rob().  Yes, I can get a robust correlation 
coefficient matrix based on mcd or mve outlier detection methods. 

I have two further questions:

1) How do I get a p value of the robust r?
2) What I mean by resampling is "leave one out" procedure, to get a 
confidence interval of r.  Do you know if there is any package in R to do 
it?  I suppose I could code it myself,  but it is nice if there is already 
one.

thanks.
Yang





"Berton Gunter" <gunter.berton@gene.com> 
25-Jan-2006 15:57
 
To
yang.x.qiu@gsk.com, r-help@stat.math.ethz.ch
cc

Subject
RE: [R] how to test robustness of correlation






check out cov.rob() in MASS (among others, I'm sure). The procedure is far
more sophisticated than "outlier removal" or resampling (??). References 
are
given in the docs.

-- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA
 
"The business of the statistician is to catalyze the scientific learning
process."  - George E. P. Box
 
 

> -----Original Message-----
> From: r-help-bounces@stat.math.ethz.ch 
> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of 
> yang.x.qiu@gsk.com
> Sent: Wednesday, January 25, 2006 12:37 PM
> To: r-help@stat.math.ethz.ch
> Subject: [R] how to test robustness of correlation
> 
> Hi, there:
> 
> As you all know, correlation is not a very robust procedure. 
> Sometimes 
> correlation could be driven by a few outliers. There are a 
> few ways to 
> improve the robustness of correlation (pearson correlation), 
> either by 
> outlier removal procedure, or resampling technique. 
> 
> I am wondering if there is any R package or R code that have 
> incorporated 
> outlier removal or resampling procedure in calculating correlation 
> coefficient. 
> 
> Your help is greatly appreciated. 
> 
> Thanks.
> Yang
> 
> Yang Qiu
> Integrated Data Analysis
> Cheminformatics@RTP
> GlaxoSmithKline
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