[R] PCA
Spencer Graves
spencer.graves at pdf.com
Sat Apr 26 16:34:49 CEST 2003
I think you can get what you want with the "svd" or "La.svd". Consider
the following:
The singular value decomposition ("svd" or "La.svd" in R 1.6.2) is
something like the following: Any n x m matrix A can be written in the
following format:
A = P Lam Q,
where P and Q are orthogonal, and Lam ia diagonal. If n < m, then we
can consider P to be n x n, so P'P = PP' = I, Lam = n x n diagonal, and
Q = n x m with QQ' = I.
Now suppose A = your data matrix minus the column means. Then the
sample covariance matrix, Var.A, can be written as follows:
Var.A = AA'/(n-1) = P Lam^2 P' / (n-1).
P gives the principle components and Q the corresponding loadings or
vice versa, I forget which now, and Lam^2/(n-1) are their associated
variances.
If you have trouble with the details, please let us know.
hope this helps. spencer graves
Andrew C. Ward wrote:
> What about trying a sub-set of the data?
>
> Regards,
>
> Andrew C. Ward
>
> CAPE Centre
> Department of Chemical Engineering
> The University of Queensland
> Brisbane Qld 4072 Australia
> andreww at cheque.uq.edu.au
>
>
>
> On Saturday, April 26, 2003 10:44 AM, array chip
> [SMTP:arrayprofile at yahoo.com] wrote:
>
>>Hi, I have a dataset of dimensions 50 x 15000, and tried to use
>>princomp or prcomp on this dataset with 15000 columns as
>>variables, but it seems that the 2 functions can;t handle this
>>large number of columns, anyone has nay suggestions to get
>>around this? Thanks
>>
>>
>>---------------------------------
>>
>>
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