Dear Sir,
Thank you Sir for the clarification.
Need to explore about this.
Thanking you
With Regards
M. Kavyashree
On Wed, Jun 8, 2011 at 2:35 PM, Tsjerk Wassenaar <tsje...@gmail.com> wrote:
> Hi Kavya,
>
> > Thanks sir. I will go through them. However I have referred -
> > "A Tutorial on Principle component Analysis" by Lindsay I Smith.
> > Which gave a good understanding about the concepts. Still I
> > have some doubts regarding eigen values, as you have told
> > I will think over them again.
>
> I know that one :) I'd advise others thinking of using PCA in MD to
> also read it... It also pays off to read a few more, though, to get
> slightly different viewing angles and to get used to different ways of
> telling the same story.
>
> > But one statement I was not clear from your previous mail that -
> > "An eigenvalue is an RMSF of the collective motion."
>
> I shouldn't have said RMSF, as it's not the root. The first eigenvalue
> is the variance or mean square fluctuation of the projection of your
> data onto the first eigenvector.
>
> >
> > These eigenvalues are the solutions for an Nth order equation
> > arising from N X N covar (sorry for using this term again) matrix
> > (considering only x component). If we consider this covar matrix
> > as a transformation matrix, eigen value would give the magnitude
> > and direction by which the eigenvector is transformed linearly.
> > Is it correct?
>
> No, the matrix of eigenvectors is a transformation (rotation) matrix.
> The eigenvectors in a sense give the directions of motion, and the
> eigenvalues the magnitudes.
>
> Cheers,
>
> Tsjerk
>
> --
> Tsjerk A. Wassenaar, Ph.D.
>
> post-doctoral researcher
> Molecular Dynamics Group
> * Groningen Institute for Biomolecular Research and Biotechnology
> * Zernike Institute for Advanced Materials
> University of Groningen
> The Netherlands
> --
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