Peter Flom <peterf <at> brainscope.com> writes:
>
> Robin Williams wrote
> <<<<
> Is there any facility in R to perform a stepwise process on a model,
> which will remove any highly-correlated explanatory variables? I am told
> there is in SPSS. I have a large number of variables (some correlated),
> which I would like to just chuck in to a model and perform stepwise and
> see what comes out the other end, to give me an idea perhaps as to which
> variables I should focus on.
> Thanks for any help / suggestions.
> >>>
>
> Stepwise is a bad method of selecting variables. Far better methods are LASSO
and LAR (least angle
> regression), available in the LARS package and the LASSO2 package.
>
> However, while both these methods are good, neither is a substitute for
substantive knowledge.
>
> Also, the key thing is not so much whether variables are correlated, but
whether they are co-linear, which
> is different. If you have a great many variables, then you can have a high
degree of colinearity even with no
> high pairwise correlations. I've not done this in R, but
>
> RSiteSearch("collinearity", restrict = 'functions') yields 34 hits.
>
> HTH
>
> Peter
>
Another suggestion would be to do PCA on the predictor variables.
And to read Frank Harrell's book on _Regression modeling strategies_.
cheers
Ben Bolker
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