The CCA algorithm requires you to invert the covariance matrices of
X and Y. This will (should) bomb in any software package because if
p,q, > n the matrices are singular. Methods like CCA and factor analysis
won't work if you have more variables than observations.
(I post this to the group also in case I am out to lunch here for some
reason. If so, enlightenment appreciated, as always.)
_______________________________________________________________________
DON MCKENZIE
Research Ecologist
College of Forest Resources, Box 352100
University of Washington
Seattle, WA 98195
206.543.9138
206.616.4015
dmck@u.washington.edu
http://silvae.cfr.washington.edu/people/dmck/home.html
_______________________________________________________________________
-
On Tue, 19 Oct 1999, Alexander Gershunov wrote:
>
> Dearest Splusers:
>
> Does anyone have an efficient Splus CCA function that works for matrices with
> large numbers of variables? By large, I mean larger than the number of
> observations. The canned Splus version, cancor(), cannot(;) deal with
> matrices
> that have more variables than observations, i.e. ncol > nrow. Such matrices,
> say
> X, an n X p matrix and Y, an n X q matrix, where n is the number of
> observations
> (rows) and p and q are the numbers of variabes (columns) such that p,q > n,
> are
> dealt with in such a way by cancor() that the linear combinations of the
> original variables ($xcoef and $ycoef) that are supposed to maximize
> correlation
> between the two sets of variables X and Y and be vectors of length p and q,
> respectively, instead have length (n-1). I just thought to check with this
> illustrious group before attempting to write my own CCA function.
>
> Thankfully,
>
> Sasha
> -------------------------------------------
> Alexander Gershunov
> Climate Research Division
> Scripps Institution of Oceanography
> UC San Diego
> La Jolla, CA 92093-0224
>
> Phone: (858) 534-8418; Fax: (858) 534-8561; email: sasha@ucsd.edu
>
>
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