Hi All,
Continuing and expanding the off-topic: BTW, it is COBOL (Common
Business Oriented Language).
I thought it silly that I had to take FORTRAN in college. In
retrospect, I view it as one of the best forced choices that I have
ever [sort of] made because I can always fall back on it when the
clunky [though easier] R program that I write frustrates me. Recall
that most of the errors that computers make are sitting in the chair
staring at the screen!
I think this is related to the original question, though. The answer
depends on what you wish to use this package for. R expands at an
alarming rate and tends to make it hard, though not impossible, to do
things that you do not understand [for free]. At a minimum, it takes
some effort! Stata has made this process ridiculously easy; indeed,
I remember reading something recently about internal debates within
Stata Corp. about the extent to which they should protect users from
themselves. R does not even try [BTW, someone earlier made a comment
about importing data to R. This is one of the easy things, try the
\texttt{foreign} package]! But, if you know what you are doing,
Stata makes many things quite fast and easy [at considerable cost for
both the software and the manuals to assist you in figuring it out].
Philosophically, I like the R idea because it creates incentive
compatibility in the development of data analytic techniques and
making such developments freely available.
My $.02 is to learn statistics; this will give you a decided
advantage! Once you know exactly what you are trying to do,
programming is the easy part [even in C, C++, or FORTRAN].
Best,
RWW
On Oct 5, 2006, at 3:28 PM, Dan Williams wrote:
> Fortran still exists? I thought it remained only in the scripting
> language
> of SAS (and, I suppose, SPSS). Next you will tell me that COBAL is
> still
> used for development. With small to medium size datasets you can do
> ANYTHING in a spreadsheet (I am talking thousands or even tens of
> thousands
> of observations these days). But the advantage of commercial or
> widely
> examined open source software is that you don't have to conduct a
> tedious
> audit of your math to be sure that your results are correct.
>
>
>
> -----Original Message-----
> Hi All,
>
> Somewhat off topic, but something I've been wondering about.
>
> What do you think of this:
>
> "Stats packages may come and go but you can always come home to
> Fortran(C)."
>
> It seems to me that entire computing environments change radically
> every decade or so, and particular optimized ways of interacting with
> data and instantiating current statistical practice also change
> around that fast. Since most of us hope to have multi-decade careers,
> I often wonder if it would be better for graduate students to learn
> two programming languages upon arriving at graduate school (or
> hopefully earlier): (1) one that can do most or all of what we
> currently think are best practices in data analysis (say, R or Stata
> now, or in earlier years, SPSS or SAS or CSA or OSIRIS (unfortunately
> I have never used OSIRIS although I hear it was great)) AND (2) a
> lower level programming language like fortran or C that will probably
> exist in some way or another for the entire data-analytic life of a
> given scholar.
>
> This way, when, say, us old guys don't want to install neural
> interfaces in order to analyze our data (say, our brains are not
> plastic enough for them to be effective or we don't want holes in our
> heads), and R is no longer supported and doesn't do what we need (not
> because of lack of flexibility but because in this future we only do
> enormous genetic and MCMC and permutation style estimation with
> billions of operations and R is at its core a slow memory hog) ----
> then we can roll our own using good old Fortran or C --- since the
> neural operating systems will still be written in versions of those
> languages.
>
> Anyway, this post does not answer the question of exactly which
> specialized package to learn. Jim Battista put my point of view more
> elegantly that I could have done: "You win the jackpot -- you get to
> learn them all." I do wonder, however, whether ensuring that folks
> know something more enduring and lower level would allow for greater
> adaptability and flexibility as we try to get work done while we
> watch the fads flow by.
>
> Best,
>
> Jake
>
>
> Jake Bowers
> currently:
> Robert Wood Johnson Health Policy Scholar, '05-'07
> Institute for Quantitative Social Science
> Harvard University
>
> on leave:
> Assistant Professor of Political Science
> Faculty Associate in the Center for Political Studies, ISR
> University of Michigan
>
> http://www.umich.edu/~jwbowers
>
>
>
>
> On Oct 5, 2006, at 10:04 AM, Walt Borges wrote:
>
>> Gosh, everyone's going to have a different opinion on this,
>> especially
>> on the relative value of preparing tables and graphs for publication
>> submissions.
>>
>>> From an analysis standpoint, and as someone who learned all these
>> programs over the last two years, I would list the pros and cons
>> thus:
>>
>> SPSS -- pro: Good point and click features
>> Relatively easy to work with data
>> con: Expensive on a students' budget
>> Program coding is not intuitively based
>>
>> STATA -- pro: Intuitive and simple program coding
>> Decent point and click
>> Simple, logical command structure
>> You can create "do-file" programs that
>> process
>> data and run analytical programs;
>> these
>> can be assembled into master do-files that
>> allow you to work off the
>> original
>> data set each session, rather than saving
>> modified data sets (which can quickly become
>> confusing, especially when several team members are
>> working with the data)
>> Decent graphics.
>> Relatively inexpensive for students
>>
>> con: Inevitably you will need to deal with a data or
>> graphics issue that will send you to
>> the
>> seven-volume manual. Someone in the
>> department better have one you can use,
>> because it's real expensive. The good news is
>> that you will probably find the instructions there
>> and understand them.
>> In-program help files are incomplete and
>> often
>> not helpful. Example: The STATA
>> typeface makes the percent sign,
>> which is used in graphics commands,
>> look like a capital N on screen. I
>> spent
>> four hours using the on-screen help in
>> trying unsuccessfully to format a graph
>> before going to the manual and discovering
>> this
>> annoying little problem.
>> The program for time-series analysis is not
>> fully developed, but the
>> alternative
>> software is far more expensive.
>>
>>
>> R -- pro: Price: freeware.
>> Exceptionally versatile: you do the
>> programming,
>> so your abilities are the limits of
>> its
>> capabilities. Library of add-on
>> components for
>> specialized analysis.
>> Easy access to outside modules (such as
>> WinBugs
>> for Bayesian analysis)
>> Excellent graphic capabilities.
>>
>> con: Non-intuitive, object-based programming
>> structure (however, if you are
>> familiar with computer programming
>> structures, this is not too much of a
>> problem).
>> Importing data sets (major pain in the butt).
>>
>> Personally, although I learned SPSS first, I prefer Stata for most
>> projects, although time series analysis is easier in E-Views and RATS
>> (both very expensive). The trick is learning how to format data in
>> sets
>> in Stata using do-files. Once you learn that, Stata is a cheap, quick
>> and easy way to conduct most basic analysis. For advanced analysis
>> and
>> Baysian, R is really the only choice.
>>
>> Walt Borges
>> Doctoral candidate
>> University of Texas-Dallas
>>
>>
>> -----Original Message-----
>> From: Political Methodology Society
>> [mailto:[log in to unmask]] On
>> Behalf Of Michael Plenty
>> Sent: Wednesday, October 04, 2006 4:42 PM
>> To: [log in to unmask]
>> Subject: [POLMETH] R vs. Stata vs. SPSS
>>
>> My name is Mike and I work as an intern for a major consulting
>> firm in
>> Washington,DC.
>>
>> My company uses SPSS, as most undergraduate programs and companies,
>> however,
>> now that I'm beginning the thinking process for graduate school, most
>> schools, in particular Northwestern, UChicago, and Yale, all
>> recommend I
>> become familiar with Stata and R.
>>
>> Are there any major differences between the three programs?
>>
>> If so, what? and what resources are out there to help me adjust?
>>
>> Michael Plenty
>>
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>
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Robert W. Walker
Assistant Professor
Department of Political Science
Program in Applied Statistics and Computation
Washington University in Saint Louis
Campus Box 1063
One Brookings Drive
Saint Louis, Missouri 63130-3899
rww at wustl.edu
http://rww.wustl.edu
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