On Thu, 2003-09-04 at 08:34, Frank E Harrell Jr wrote: > On Thu, 04 Sep 2003 14:50:25 -0400 > "Paul, David A" <[EMAIL PROTECTED]> wrote: > > > I am one of only 5 or 6 people in my organization making the > > effort to include R/Splus as an analysis tool in everyday work - > > the rest of my colleagues use SAS exclusively. > > > > Today, one of them made the assertion that he believes the > > numerical algorithms in SAS are superior to those in Splus > > and R -- ie, optimization routines are faster in SAS, the SAS > > Institute has teams of excellent numerical analysts that > > ensure its superiority to anything freely available, PROC > > NLMIXED is more flexible than nlme( ) in the sense that it > > allows a much wider array of error structures than can be used > > in R/Splus, &etc. > > > > I obviously do not subscribe to these views and would like > > to refute them, but I am not a numerical analyst and am still > > a novice at R/Splus. Do there exist refereed papers comparing the > > numerical capabilities of these platforms? If not, are there > > other resources I might look up and pass along to my colleagues? > > > > > > > > Much thanks in advance, > > > > david paul > > I don't have papers comparing the numerical capabilities but I say > bunk to your colleagues. The last time I looked, SAS still relies on > the out of date Gauss-Jordan sweep operator in many key places, in > place of the QR decomposition that R and S-Plus use in regression. > And SAS being closed source makes it impossible to see how it really > does calculations in some cases. > > See http://hesweb1.med.virginia.edu/biostat/s/doc/splus.pdf Section > 1.6 for a comparison of S and SAS (though this doesn't address > numerical reliability). Overall, SAS is about 11 years behind R and > S-Plus in statistical capabilities (last year it was about 10 years > behind) in my estimation. > > Frank Harrell > SAS User, 1969-1991
In follow up to Frank's reply, allow me to point you to some additional papers and articles on numerical accuracy issues. I have not reviewed these in some time and they may be a bit dated relative to current versions. These do not cover R specifically, but do address S-Plus and SAS. This is not an exhaustive list by any means, but many of the papers do have other references that may be of value. 1. http://www.stat.uni-muenchen.de/~knuesel/elv/accuracy.html 2. http://www.amstat.org/publications/tas/mccull-1.pdf 3. http://www.amstat.org/publications/tas/mccull.pdf 4. http://www.npl.co.uk/ssfm/download/documents/cmsc06_00.pdf Another option is that NIST has reference datasets available for comparison at: http://www.itl.nist.gov/div898/strd/ These would allow you to conduct your own comparisons if you desire. HTH, Marc Schwartz (Also a former SAS user) ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
