[R] PCA on high dimentional data

[Bert Gunter]

... and adding to what has already been said, PCA can be distorted by
non-ellipsoidal distributions or small numbers of unusual values.
Careful (chiefly graphical) examination of results is therefore
essential, and usually fairly easy to do. There are robust/resistant
versions of PCA in R, but they come with their own issues. As you have
already been told, you need to do some homework -- or get some local
advice.

Also, you need to post on some other list, e.g.
stats.stackexchange.com, as you have wandered outside the realm of R
issues.

-- Bert

On Sat, Dec 10, 2011 at 10:40 AM, Mark Difford <mark_difford at yahoo.co.uk> wrote:
> On Dec 10, 2011 at 5:56pm deb wrote:
>
>> My question is, is there any way I can map the PC1, PC2, PC3 to the
>> original conditions,
>> so that i can still have a reference to original condition labels after
>> PCA?
>
> deb,
>
> To add to what Stephen has said. Best to do read up on principal component
> analysis. Briefly, each PCA is composite variable, composed of different
> "amounts" of each and every one of your column variables, i.e. cond1, ...,
> cond1000.
>
> So the short answer to your question is no. There is no way to do this
> mapping, except as loadings on each principal component (PC).
>
> Regards, Mark.
>
> -----
> Mark Difford (Ph.D.)
> Research Associate
> Botany Department
> Nelson Mandela Metropolitan University
> Port Elizabeth, South Africa
> --
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>
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-- 

Bert Gunter
Genentech Nonclinical Biostatistics

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