[R] Comparison of SAS & R/Splus

[Marc Schwartz]

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" <paulda at BATTELLE.ORG> 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 the out of date Gauss-Jordan sweep operator in many key places, in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)