Message-ID: <20021110141214.5509f831.fharrell@virginia.edu>
Date: 2002-11-10T19:12:14Z
From: Frank E Harrell Jr
Subject: building a formula for glm() with 30,000 independent variables
In-Reply-To: <3DCE6D23.7050104@eecs.berkeley.edu>
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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