[R] Stepwise

[Ben Bolker]

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