Hello,
Newbie here, be gentle ;)
I have a reference book that discusses regression model selection using
several methods - what they call 'Forward Model Selection' i.e. add one
variable at a time and examining R, R^2, Mallow's C-p value, etc., 'Backward
Model Selection' i.e. starting out with all the variables included and then
remove them one at a time, and examining for fit, and finally a 'Best
Subsets' procedure, to find which combination (forward, backward, or other)
gives the best fit. Unfortunately everything is directed at use with
Minitab, so while I get the general concept behind what they are discussing,
I'm at somewhat of a loss as to how to do the same sort of thing in R. I
searched the R-project site and archives for 'regression model selection'
and got *too much* info... thousands of hits. Apparently its either a
*very* popular subject or my search-foo needs some work ;)
If someone could perhaps point me in the right direction, I'd greatly
appreciate it.
Thanks,
Monte
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