Message-ID: <CA+hbrhXNSGJ2ik1aepJ7xPAUgmZvU2DZyFNQ+8ksNYwpxskJfw@mail.gmail.com>
Date: 2011-12-30T17:31:57Z
From: Peter Langfelder
Subject: good method of removing outliers?
In-Reply-To: <CAPNjSFad447-trBfEhYR5uZ4Z6kh4K8Mg60eNeeakPh0C0Bgqg@mail.gmail.com>
On Fri, Dec 30, 2011 at 9:03 AM, Michael <comtech.usa at gmail.com> wrote:
> Happy holidays all!
>
> I know it's very subjective to determine whether some data is outlier or
> not...
>
> But are there reasonally good and realistic methods of identifying outliers
> in R?
What kind of data do you have? For simple numeric data, there are
various methods for removing outliers developed for robust estimation
and I'm sure they are implemented in R. For example, this link
http://www.unt.edu/benchmarks/archives/2001/december01/rss.htm
describes how to calculate a robust measure of correlation that
includes a method to downweigh (or remove) outliers.
For identifying outlier samples in multivariate setting, the
possibilities are even more varied, from simple hierarchical
clustering and visual identification of outliers to network
connectivity methods etc.
HTH,
Peter