[R] Fwd: regsubsets (Leaps)

[Bert Gunter]

Frank -- where are you?!

(To the OP: Your post leaves me simply breathless. You are embarked on
a fool's errand. Filoche's "help" will continue you down that path.
IMHO only of course.

Bottom line: You CANNOT do what you wish to do. Or to quote John Tukey:

"The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data. " )

-- Bert


---------- Forwarded message ----------
From: farmedgirl <ksteinmann at cdpr.ca.gov>
Date: Fri, Jun 1, 2012 at 8:19 AM
Subject: [R] regsubsets (Leaps)
To: r-help at r-project.org


Hi
i need to create a model from 250 + variables with high collinearity, and
only 17 data points (p = 250, n = 750). I would prefer to use Cp, AIC,
and/or BIC to narrow down the number of variables, and then use VIF to
choose a model without collinearity (if possible).  I realize that having a
huge p and small n is going to give me extreme linear dependency problems,
but I *think* these model selection criteria should still be useful?

I have currently been running regsubsets for over a week with no results. I
have no idea if R is still working, or if the computer is hung. I ran
regsubsets on a smaller portion of the data, also with linear dependency
problems, and got results. However, the hourglass continues its endless
spiraling with the full dataset.

I am running the following on Windows 7
library(leaps)
m_250<-regsubsets(Y~., data=model2, nbest=1, really.big=TRUE)

(NOTE: The ~ is a tilda, not a dash, in the regression statement above: Y~.)

Does anyone have any opinions on:
1) is R likely to still be running, even after a week, or should i just shut
it down?

2) am i doing something wrong with regsubsets?

3) is there a better option than regsubsets, that will still allow me to
narrow down parameters so i have explanatory power (ie i could develop a
model using PLS, and keep all the variables, but also keep all the
collinearity issues, and have good prediction but not explanatory power)

4) any other ideas?

I am pretty new to R, so any newbie detail would be much appreciated!

thanks in advance for any help!

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-- 

Bert Gunter
Genentech Nonclinical Biostatistics

Internal Contact Info:
Phone: 467-7374
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