[S] stepwise discriminant analysis

William M. Grove (William.M.Grove-1@tc.umn.edu)
Mon, 10 Aug 1998 11:18:37 -0500


I see that step() and stepAIC() work on objects of the "lm" class (e.g.,
returned from glm(), gam() ), according to the documentation. As such I
assume it won't work with an unaltered form of lda() or fda().

I don't want to write a whole stepwise discriminant function from scratch.
(I want to take the output of such a function and bootstrap it using Shao's
consistent bootstrapping model selection technique.)

I have considered writing a wrapper funciton for, say, fda(), which coerces
the class of the returned object to "lm" and sets "Deviance" to something
useful like the classification error rate (on the training sample).

Does anyone know if I can then use this function to trick step() or
stepAIC() into performing a stepwise discriminant analysis? (I realize I'd
need to get the scale parameter for AIC treated as zero to get that part of
the optimization criterion out of the picture.) Or will the whole thing
blow up in my face owing to something I don't understand about how step()
or stepAIC() is going to call the fda()-like function?

Any help would be appreciated.
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