Hello, I'm (eventually) attempting a singular value decomposition of a 3200 x 527829 matrix in R version 2.10.1. The script is as follows: ###---------Begin Script here-------### library(Matrix)
snps <- 527829 ## Number of SNPs N <- 3200 ## Sample size y <- rnorm(N, 100,1) ## simulated phenotype system.time( ## read in matrix 3200 x 527829 x <- scan("gedi7.raw", what=rep(0,snps), nmax=N*snps, skip=1)) system.time(x <- matrix(x,nrow=N,ncol=snps, byrow=TRUE)) print(object.size(x), units="Mb") ###--------End Script----------------#### The scan function finishes without a problem. "x" is in double precision floating point format and takes up 12886.5Mb of memory at the first object.size() statement. When I convert it to a matrix I get an error stating that I cannot allocate a vector of size 12.6Gb. I have requested 31Gb of memory on the server. 12.6+ 12.8 = 25.4Gb of used memory. Is it that R is using considerable memory for operations not directly related to storing the matrix objects here? Or is this perhaps a problem of contiguous memory? Any help is greatly appreciated. -Scott -- View this message in context: http://r.789695.n4.nabble.com/Working-with-massive-matrices-in-R-tp3458561p3458561.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.