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

[Frank E Harrell Jr]

On Sun, 10 Nov 2002 06:28:51 -0800
Ben Liblit <liblit at eecs.berkeley.edu> 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?
> 
> Thanks!
> 
> It would be worth doing a simulation first to see if ANY statistical properties of the resulting estimates or P-values work as advertised with your setup.  I would expect severe biases, lack of preservation of type I error, and low probability of selecting the "correct" variables.  The "few that actually matter" will likely be those whose estimates are made with the most error.
-- 
Frank E Harrell Jr              Prof. of Biostatistics & Statistics
Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
U. Virginia School of Medicine  http://hesweb1.med.virginia.edu/biostat
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