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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