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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
