John Sorkin wrote:
> Frank,
> I believe you are proving my point. The difference is not so much the
> language as the end users. I use SAS, R, and SPlus on a regular basis. For
> some analyses, SAS is easiest to use, for some R (or SPlus). I can be just as
> dangerous using SAS and I can be with R if I don't think about what I am
> doing and not only check the assumptions of my models, but also pay attention
> to the results of the checks. You see problems with SAS data sets because you
> know what to look for and take the trouble to look for problems. When R (or
> SPlus) becomes commonly used by the great unwashed public, the number of
> poorly done analyses in these languages will increase. The basic problem with
> statistical software is that by making analyses easy to do, they allow anyone
> to do analyses. When an unprepared person sets about doing a complex task
> that should demand proper training and experience bad things happen quickly,
> and with high probability.
>
> In any event, regardless of which side of the argument members of the R
> listserver might take, we are all deeply in your debt for the many
> contributions you have made not only to the R environment, but also to the R
> listserver. On behalf of the entire R community, thank you.
>
We'll have to have a friendly but strong disagreement about this. I've
watched statisticians work too many times to not believe that many will
take the expedient route (e.g., assume linearity) when using
non-flexible or non-powerful software (e.g., SAS). And I don't find
errors in the data usually because I know the data. I find errors
because I can say things like
library(Hmisc)
datadensity(mydata) # show all raw data in small rug plots
hist.data.frame(mydata) # postage-stamp size histograms of all
variables in dataset
latex(describe(mydata)) # like PROC UNIVARIATE but shows MUCH more
information in MUCH less space, including a high-resolution histogram
next to the tabular info for each variable
I do agree with your comment about making things easy to do.
> With greatest respect and thanks,
Thanks very much for the kind words John.
Cheers
Frank
> John
>
> John Sorkin M.D., Ph.D.
> Chief, Biostatistics and Informatics
> University of Maryland School of Medicine Division of Gerontology
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> (Phone) 410-605-7119
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>
>>>> Frank E Harrell Jr <[EMAIL PROTECTED]> 1/7/2008 6:41 PM >>>
> John Sorkin wrote:
>> I fear I risk being viewed as something of a curmudgeon, but the truth must
>> be stated. S-Plus, R, SAS, etc. are all similar in that they are all tools
>> to an end and not an end in themselves. Any one of the three can do most
>> statistical analyses one might want to do. I could point out the strengths
>> of any one of the programming environments, but to be fair I would then be
>> required to point out each platform's weaknesses. In the end, what matters
>> is the quality and abilities of the person who uses the tools, not the tools
>> themselves. I don't think you can make a fair statement that any one is
>> absolutely better than the other.
>> John
>
> John - I must respectfully disagree at least in part. I have noticed
> that SAS users are far more likely to assume linearity in doing
> regression modeling, because SAS makes it so difficult to specify that
> you want an unknown smooth function of a covariate in a model. SAS
> users are also less likely to bootstrap and to validate statistical
> models because it's such a pain to do those in SAS. Also when I get SAS
> datasets from companies that have paid a fortune to a SAS-based contract
> research organization, I can quickly spot major data errors using S
> graphics; these errors were missed by all the SAS users because of poor
> graphics.
>
> Frank
>
>> John Sorkin M.D., Ph.D.
>> Chief, Biostatistics and Informatics
>> University of Maryland School of Medicine Division of Gerontology
>> Baltimore VA Medical Center
>> 10 North Greene Street
>> GRECC (BT/18/GR)
>> Baltimore, MD 21201-1524
>> (Phone) 410-605-7119
>> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>>
>>>>> Jeffrey J. Hallman <[EMAIL PROTECTED]> 1/7/2008 4:09 PM >>>
>> SAS programming is easy if everything you want to do fits easily into the
>> row-at-a-time DATA step paradigm. If it doesn't, you have to write macros,
>> which are an abomination. DATA step statements and macros are entirely
>> different programming languages, with one doing evaluations at "compile"
>> time,
>> and the other at "run" time. Except that that's not really true, either,
>> witness the 'call symput()' construct.
>>
>> Then, if you want to interact at all with the user, you need to learn SCL, a
>> third language, with it's own rules. And to do anything sophisticated with a
>> user interface (which will still look like hell), you have to learn the SAS
>> A/F toolkit built on SCL. And of course, A/F requires you to think
>> differently yet again.
>>
>> So, to be a competent and versatile SAS programmer, you have to learn four
>> languages and four paradigms, and keep them all straight in your head while
>> programming. Of course, hardly anyone can do this, so you usually find
>> stacks
>> of reference documentation close at hand when you visit a SAS programmer's
>> office.
>>
>> R and Splus don't offer much in the way of GUI programming, but for problems
>> that don't require a lot of GUI, it's very nice. You learn one language,
>> it's
>> quite forgiving, it's interpreted and usually easy to debug, and the programs
>> you end up with are far more readable and maintainable than anything a SAS
>> programmer can turn out. Reading my own SAS code is bad, and reading someone
>> else's is torture.
>>
>> Do I sound like an R bigot? Actually, I'm a Smalltalk bigot, which is even
>> nicer than R. But R is quite usable for most things I do, and I use
>> Smalltalk
>> for GUI-intensive stuff. Speaking as a programmer, SAS is awful.
>>
>
>
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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