Message-ID: <200508151656.j7FGuSBT019203@upham.cgd.ucar.edu>
Date: 2005-08-15T16:56:28Z
From: Dennis Shea
Subject: PCA problem in R
[SNIP]>>
>>>On Sat, 13 Aug 2005, Alan Zhao wrote:
>>>
>>>>When I have more variables than units, say a 195*10896 matrix which has
>>>>10896 variables and 195 samples. prcomp will give only 195 principal
>>>>components. I checked in the help, but there is no explanation that why
>>>>this happen.
[SNIP]
>Sincerely,
>Zheng Zhao
>Aug-14-2005
>______________________________________________
Just yesterday I subscribed to r-help because I am planning
on learning the basics of R ... today. :-)
Thus, I am not sure about the history of this question.
The above situation, more variables than samples,
is commonly encounterd in the climate studies.
Consider annual mean temperatures for 195 years
on a coarse 72 [lat] x 144 [lon] grid [72*144=10368
spatial variables].
Let S be the number of grid points and T be the number
of years. I think there is a theorem (?Eckart-Young?)
which states that the maximum number of unique eigenvalues
is min(S,T). In your case 195 eigenvalues is correct.
I speculate that the underlying function transposes the
input data matrix and computes the the TxT [rather than SxS]
covariance matrix and solves for the eigenvalues/vectors.
It then uses a linear transformation to get the results
for the original input data matrix.
Computationally, the above is much faster and uses less memory.