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 <[EMAIL PROTECTED]> 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 > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > -- WenSui Liu (http://statcompute.blogspot.com) Senior Decision Support Analyst Health Policy and Clinical Effectiveness Cincinnati Children Hospital Medical Center [[alternative HTML version deleted]] ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
