bill,

what kind of modeling are you talking about? based on your desc on SAS, you
are doing general linear model, aren't you?

Frank gave you a great suggestion of using shrinkage methods, such as lasso,
instead of stepwise/backforward/forward methods. But no matter shrinkage or
stepwise, both can be implemented in R and SAS. At this point, there is no
difference at all.

On 2/3/06, Bill Szkotnicki <bszk@uoguelph.ca> wrote:
>
> Hello,
>
> Recently I have been reading a lot of material about statistical modeling
> using R. There seems to be conflicting opinions about what the best
> approach
> is between the SAS community and the R community.
> 1) In R one might start with a model that has all possible effects of
> interest in it and then simplify by eliminating/adding insignificant
> effects
> using a stepwise procedure.
> 2) In SAS one may starts with a "reasonable" model and look at type 3 SS's
> to test hypotheses and report LSMEANS. This can be done in R too I think.
>
> Does anyone have current opinions about this? I know it's been discussed
> before but I would be very interested in hearing about the advantages and
> pitfalls of both approaches.
>
> Bill
>
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--
WenSui Liu
(http://statcompute.blogspot.com)
Senior Decision Support Analyst
Health Policy and Clinical Effectiveness
Cincinnati Children Hospital Medical Center

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