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Principal components analysis [message #14501]

Mon, 01 March 1999 00:00

Mark McGillion
Messages: 14
Registered: December 1997

Junior Member

Dear all,

I am computing the principal components and derived variables of a set
of power spectral data (using IDL v5.1). The power spectral data is a
matrix of high dimensionality [500,400], the power spectra has 500
points, and there are 400 examples of this spectra. The 500 derived
variables calculated from the principle components describe the data
completely (100% of the variance). In my case, I find that the first
two derived variables describe over 95% of the variance, therefore I can
easily visualise my original data in 2-dimensions. Great!

What I need to know is......what contribution is made to these derived
variables by each of the original 500 variables. Is it possible to
determine
which of the original 500 variables provided the greatest contribution
to each derived variable?

I would appreciate any help you can give (even if it is just a reference
book!).

Cheers, Mark

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Principal components analysis

By: Mark McGillion on Mon, 01 March 1999 00:00

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