[R] Comparison of SAS & R/Splus

[Douglas Bates]

"Paul, David  A" <paulda at BATTELLE.ORG> writes:

> 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?

Although they are out of date, there are some comparisons of accuracy in

 McCullough, B. D. (1998), "Assessing the reliability of statistical
 software: Part I", The American Statistician, 52, 149-159.

 McCullough, B. D. (1999), "Assessing the reliability of statistical
 software: Part II", The American Statistician, 53, 358-366.

Regarding PROC NLMIXED versus nlme, there are a lot of differences
between them.  I don't think that PROC NLMIXED will handle nested
random effects while nlme does.  However, nlme assumes the underlying
noise is Gaussian while PROC NLMIXED allows Gaussian or binomial or
Poisson.  PROC NLMIXED uses adaptive Gaussian quadrature to evaluate
the marginal log-likelihood whereas nlme uses a less accurate
evaluation but better parameterizations of the variance of the random
effects.  I think it would be difficult to declare one to be superior
to the other.