Tjerk,
This is just a pseudo code outline of what you need to do:
M = matrix(0, number of variables, number of variables)
V = rep(0, number of variables)
N = 0
While (more observations to read) {
X <- next observation
V <- V + X
M <- M + outer(X,X)
N <- N+1
}
Compute covariance matrix from elements of V,M, and N
You just need to refer to the formula defining covariance.
Outlook seems to think all my variables should be upper case.
HTH
Rex
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Tsjerk Wassenaar
Sent: Monday, March 14, 2011 10:14 AM
To: R-help
Subject: [R] *Building* a covariance matrix efficiently
deaRs,
I want to build a covariance matrix out of the data from a binary
file, that I can read in chunk by chunk, with each chunk containing a
single observation vector X. I wonder how to do that most efficiently,
avoiding the calculation of the full symmetric matrices XX'. The
trivial non-optimal approach boils down to something like:
Q <- matrix(rnorm(100000),ncol=200)
M <- matrix(0,ncol=200,nrow=200)
for (i in 1:nrow(Q))
M <- M + tcrossprod(Q[i,])
I would appreciate pointers to help me fill this lacuna in my R skills :)
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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