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
