On 18/07/2012 10:14 AM, Bert Gunter wrote:
checkforoutliers <- function(series)NULL
Cheers,
Bert
*Explanation: There is no such thing as a statistical outlier -- or,
rather,"outlier" is a fraudulent statistical concept, defined arbitrarily
and without scientific legitimacy. The typical unstated purpose of such
identification is to remove contaminating or irrelevant data, but such a
judgment can only be made by a subject matter expert with knowledge of the
context and, usually, the specific cause for the unusual data. Do not be
misled by the large body of statistical literature on this topic into
believing that statistical analysis alone can provide objective criteria to
do this. That is a path to scientific purgatory.
For the record:
1. I am a statistician
2. Lots of highly knowledgeable, smart statisticians will condemn what I
have just said as stupid ranting.
The perils of a mailing list.
I think you are assuming that Sajeeka will handle the outliers
incorrectly. It happens often enough, but I don't think it's polite to
make that assumption.
My answer to the question would have been to ask the question, "how do
you define outliers?" Certainly it's possible to define outliers in the
context of a model, and their presence is an indication of problems with
the model. The correct response might be to weaken the assumptions of
your model and use a robust procedure as Michael suggested (which might
mean throwing away the outliers), or it might be to change the model in
some other way. Your advice to consult a subject matter expert is good,
but in my experience, they often put more faith in their models than
they should, so as a statistician, I think you should point out
discrepancies like outliers. Which means it's good to have a function
to detect them.
Duncan Murdoch
-- Bert
On Wed, Jul 18, 2012 at 6:27 AM, Sajeeka Nanayakkara <nsaje...@yahoo.com>wrote:
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> What is the R code to check whether data series have outliers or not?
>
> Thanks,
>
> Sajeeka Nanayakkara
> [[alternative HTML version deleted]]
>
>
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