[R] all possible subsets, with AIC

[Nutter, Benjamin]

I've dabbled in this a little bit, and the result of my dabbling is
attached.  I'll give you fair warning, however.  The attached function
can take a long time to run, and if your model has 10 or more
predictors, you may be retired before it finishes running.

In any case, it will models for all possible subsets of predictors in
lm, glm, or coxph.  If requested, it will also plot the R-squared,
Adjusted R-squared, AIC, or BIC of those models (when the values are
applicable to the model).  It might give you a good starting point.

Benjamin

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of kcleary2
Sent: Friday, February 12, 2010 3:19 PM
To: r-help at r-project.org
Subject: [R] all possible subsets, with AIC



Hello, 

I have a question about doing ALL possible subsets regression with a
general linear model. My goal is to produce cumulative Akaike weights
for each of 7 predictor variables-to obtain this I need R to: 

1.
Show me ALL possible subsets, not just the best possible subsets 

2. Give
me an AIC value for each model (instead of a BIC value). 

I have tried to
do this in library(RcmdrPlugin.HH), and using the "leaps" code below.
With the leaps code my problem is that my response is not a vector, it's
a single value (density of a species) 

ANy help would be greatly
appreciated. Thanks a lot,
Kate 

ALL-SUBSETS
REGRESSIOM

DESCRIPTION

leaps() performs an exhaustive search for the best subsets of the
variables in x for predicting y in linear regression, using an efficient
branch-and-bound algorithm. It is a compatibility wrapper for regsubsets
[1] does the same thing better. 

Since the algorithm returns a
best model of each size, the results do not depend on a penalty model
for model size: it doesn't make any difference whether you want to use
AIC, BIC, CIC, DIC, ... 

USAGE

leaps(x=, y=, wt=rep(1, NROW(x)), int=TRUE, method=c("Cp", "adjr2",
"r2"), nbest=10, names=NULL, df=NROW(x),
strictly.compatible=TRUE)

ARGUMENTS

 		x
 		A matrix of predictors

 		y

		A response vector

 		wt
 		Optional weight vector

 		int
 		Add an
intercept to the model

 		method
 		Calculate Cp, adjusted R-squared or
R-squared

 		nbest
 		Number of subsets of each size to report

 		names

		vector of names for columns of x

 		df
 		Total degrees of freedom to
use instead of nrow(x) in calculating Cp and adjusted R-squared


		strictly.compatible
 		Implement misfeatures of leaps() in S

--
Kate
Cleary
MS Candidate
Department of Fish, Wildlife, and Conservation Biology Colorado State
University Fort Collins, CO
970-491-3535



Links:
------
[1]
https://webmail.warnercnr.colostate.edu/leaps/help/regsubsets

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