[R] building a formula for glm() with 30,000 independent variables

[ripley@stats.ox.ac.uk]

Well, the theory of perceptrons says you will find perfect discrimination
with high probability even if there is no structure unless n is well in
excess of 2p.  So you do have 100,000 units?  If so you have many
gigabytes of data and no R implementation I know of will do this for you.
Also, the QR decomposition would take a very long time.

You could call glm.fit directly if you could form the design matrix
somehow but I doubt if this would run in an acceptable time.

On Sun, 10 Nov 2002, Ben Liblit wrote:

> I would like to use R to perform a logistic regression with about
> 30,000 independent variables.  That's right, thirty thousand.  Most
> will be irrelevant: the intent is to use the regression to identify
> the few that actually matter.
>
> Among other things, this calls for giving glm() a colossal "y ~ ..."
> formula with thirty thousand summed terms on its right hand side.  I
> build up the formula as a string and then call as.formula() to convert
> it.  Unfortunately, the conversion fails.  The parser reports that it
> has overflowed its stack.  :-(
>
> Is there any way to pull this off in R?  Can anyone suggest
> alternatives to glm() or to R itself that might be capable of handling
> a problem of this size?  Or am I insane to even be considering an
> analysis like this?

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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