On Wed, 30 Oct 2002 20:30:35 -0700
Julia Linke <jlinke@ucalgary.ca> wrote:
> Dear List,
> I have been looking into various different regression model
> selection strategies, and have read Quinn and Keogh's (2002) advise to
> select the best model based on all possible combinations of the multiple
>
> predictors, rather through procedures such as backward, or
> forward stepwise selection. I generally use the AIC as a criteria to
> select the best model, but now I am looking for a command (such as
> stepAIC) that integrates model selection based on lowest AIC using all
> possible predictor combinations.
>
> Would you be able to suggest a S-Plus code that achieves
> running all possible predictor combinations (potentially including
> interactions) for a multiple regression model ?
>
> Any insight would be greatly appreciated.
> Regards,
> Julia
>
>
I haven't read the reference you cited but I hope they added that all of these
stepwise procedures can be disasters in terms of overfitting, non-preservation
of confidence interval coverage and type I error, biased estimates of
regression coefficients, inflated R^2, inflated adjusted R^2, etc., etc. See
more at http://www.pitt.edu/~wpilib/statfaq.html and
http://hesweb1.med.virginia.edu/biostat/rms .
--
Frank E Harrell Jr Prof. of Biostatistics & Statistics
Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
U. Virginia School of Medicine http://hesweb1.med.virginia.edu/biostat
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