Binary Matrices
1000x1000 is only indicative. I need to generate larger (adjacency) matrices using a variety of models. Most are sparse, with a high proportion of zeros and so SparseM sounds very promising. I will investigate. Thanks, Mark
"Liaw, Andy" <andy_liaw at merck.com> 13/04/2005 >>>
-----Original Message----- From: Uwe Ligges Mark Edmondson-Jones wrote:
I'm wanting to perform analysis (e.g. using eigen()) of
binary matrices - i.e. matrices comprising 0s and 1s.
For example: n<-1000 test.mat<-matrix(round(runif(n^2)),n,n) eigen(test.mat,only.values=T) Is there a more efficient way of setting up test.mat, as
each cell only requires a binary digit? I imagine R is setting up a structure which could contain n^2 floats. No. In principle you could use logicals,
... but that doesn't save any memory:
object.size(integer(1e6))
[1] 4000028
object.size(logical(1e6))
[1] 4000028
but that does not help for further calculations in eigen().
Besides, if the problem size is really 1000 x 1000, one matrix in double precision is only 8MB. As Reid said, if the matrix is sparse, there's probably a lot more saving in both memory and computation by using SparseM and Matrix packages. Cheers, Andy
Uwe Ligges
Thanks in advance for any help. Regards, Mark This message has been checked for viruses but the contents
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