the statistician is to catalyze the scientific learning
process." - George E. P. Box
> -Original Message-
> From: [EMAIL PROTECTED]
> [mailto:[EMAIL PROTECTED] On Behalf Of Gabor
> Grothendieck
> Sent: Thursday, January 26, 2006 9:05 AM
> To: [EMAIL PROTECTED]
&g
The cor function can do spearman correlation using
method = "spearman" .
On 1/25/06, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote:
> 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
> impr
One more thing ...
> 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:
>
You might call it semantics, but I prefer "resistant estimation" to "outlier
detection methods." I rec
Below
>
> 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?
A p-valu
[EMAIL PROTECTED], 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
Ge
ician is to catalyze the scientific learning
process." - George E. P. Box
> -Original Message-
> From: [EMAIL PROTECTED]
> [mailto:[EMAIL PROTECTED] On Behalf Of
> [EMAIL PROTECTED]
> Sent: Wednesday, January 25, 2006 12:37 PM
> To: r-help@stat.math.ethz.
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 i
