

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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