stepwise regression

An incompletely debugged revision of the stepAIC function called 
"stepAIC.c" is available for downloading from "prodsyse.com".  This 
version allows three modifications of "stepAIC":

	  1.  It offers three different 'hierarchy' options:  'include', 
'exclude' and 'ignore'.  The 'include' option will test A^2 as 'A+A^2' 
with 2 degrees of freedom if a the linear term is not already in the 
model, with comparable adjustments for interactions and for backwards 
deletion.

	  2.  The use of "AIC.c" for model selection, following  Burnham and 
Anderson (2002) Model Selection and Multimodel Inference, 2nd ed. 
(Springer).

	  3.  The output includes an attribute "models" summarizing all the 
models fit sorted by the posterior probability that each model is the 
best to use given the available data assuming a uniform prior over all 
models tested.  If you assume that the "best" model will likely have 
either a dominant main effect or parabolic or two-factor interaction, 
then this should be equivalent to a posterior over all models in the 
specified scope.

	  This has been lightly tested with lm in S-Plus 6.1 and R 1.6.2.  It 
carries the standard GNU warranty, which is nothing.  I am actively 
working to improve this, and would appreciate your comments, questions, 
and suggestions.  However, I also have other commitments, so I can't 
promise to respond any more than I respond to "r-help".

	  I hope some of you find this useful -- and maybe get excited enough 
to improve it yourself.

Best Wishes,
Spencer Graves

################################
stepAIC in MASS.

hope this helps.  spencer graves

> Probably you've already found the function  step()  in the base
 > package, and its sub-functions  add1(), drop1().  See the help
 > for these.
 >
 > -  tom blackwell  -  u michigan medical school  -  ann arbor  -
 >

>
 >
 >>Hello,
 >>Does anybody know where I can find an R function to carry out variable
 >>selection using stepwise similar (or even better) to the stepwise 
function
 >>available in S-Plus?. I have tried the mle.stepwise function available in
 >>the wle package but I am not getting accurate results.
 >>I have tried also the leaps package but it does not handle the options
 >>f-in and f-out like in either MINITAB or SAS.
 >>
 >>Thanks in advance
 >>
 >>Dr. Edgar Acuna
 >>University of Puerto Rico at Mayaguez
 >
 >
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 >