Re: [R] things that are difficult/impossible to do in SAS or SPSS but simple in R

From: Roland Rau <roland.rproject_at_gmail.com>
Date: Tue, 15 Jan 2008 16:26:26 -0500

Hi,

maybe I missed something while using SAS or SPSS. So please make sure that I am not talking nonsense here.

Now you can easily extract a vector of the probability of dying of 85 year-old men dying in the first year of observation in all countries by writing:
prop.dying.array[86,1,1,]
- I hope I am wrong on this one. But when I was using SPSS, I could not find any possibility to include left truncated data in survival analysis. Maybe I did not find this possibility or maybe it has been included since.
- The function outer()
- Data are not always rectangular data frames.

Those are just a few thoughts which came to my mind. I hope this helps,
Roland                                 

Matthew Keller wrote:
> 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
>



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