[R] things that are difficult/impossible to do in SAS or SPSS butsimple in R

Doran, Harold HDoran at air.org
Tue Jan 15 20:54:19 CET 2008

SAS cannot deal with multiple levels of random effects in a generalized
linear mixed model whereas the lmer function can handle multiple levels.
The SAS proc can only deal with 1 level of clustering and it is still
extremely s l o w .. 

> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of Matthew Keller
> Sent: Tuesday, January 15, 2008 2:45 PM
> To: R Help
> Subject: [R] things that are difficult/impossible to do in 
> SAS or SPSS butsimple in R
> Hi all,
> I'm giving a talk in a few days to a group of psychology 
> faculty and grad students re the R statistical language. Most 
> people in my dept.
> use SAS or SPSS. It occurred to me that it would be nice to 
> have a few concrete examples of things that are fairly 
> straightforward to do in R but that are difficult or 
> impossible to do in SAS or SPSS. However, it has been so long 
> since I have used either of those commercial products that I 
> am drawing a blank. I've searched the forums and web for a 
> list and came up with just Bob Muenchen's comparison of 
> general procedures and Patrick Burns' overview of the three. 
> Neither of these give concrete examples of statistical 
> problems that are easily solved in R but not the commercial packages.
> Can anyone more familiar with SAS or SPSS think of some 
> examples of problems that they couldn't do in one of those 
> packages but that could be done easily in R? Similarly, if 
> there are any examples of the converse I would also be 
> interested to know.
> Best,
> Matt
> --
> Matthew C Keller
> Asst. Professor of Psychology
> University of Colorado at Boulder
> www.matthewckeller.com
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