The size of the model matrix X can be estimated approximately. It depends on
the kind of data in the model matrix. For instance, floating points require
more memory than integers (which I think is 8 bits per cell). If your model
matrix is sparse, you can use some hidden functions in the matrix package for
sparse model matrices and save a lot of memory in doing so, though I am not
certain how to estimate memory requirements under such conditions.
________________________________________
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of
efreeman [efree...@blarg.net]
Sent: Monday, January 10, 2011 5:28 PM
To: r-help@r-project.org
Subject: [R] Memory Needed for Regression
I'm looking for a formula for memory usage in standard regression; that
is, if I have X rows with Y predictors, how much memory is needed? I'm
speccing out a system, and I'd like to be able to get enough memory
that we can do some fairly large regressions.
==Ed Freeman
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