Hi R users:
I want to know if any of you had used
Stata or Statgraphics.
What are the advantages and disadvantages with
respect to R on the following aspects?
1. Statistical functions or options for advanced
experimental design (fractional, mixed models,
greco-latin squares, split-plot, etc).
2. Bayesian approach to experimental design.
3. Experimental design planing options.
4. Manuals (theory included in the manuals).
5. Support (in this aspect there is no comparison with R,
the R list is the best known support).
6. Numerical stability.
7. Implementation of modern statistical approaches.
Thank you for your help.
Kenneth
--
I'll answer the ones I know of:> 4. Manuals (theory included in the manuals).Stata manuals are superb. The online help manuals are really minimal, but the complete set of manuals for sale is really good. Not only they discuss the Stata implementetation, but they give a concise theoretical discussion of what the statistical methods are actually doing. While they don't get to talk much about the inner workings of Stata, (as some of the R manuals do) I like them much better to the R ones. Many of the statistical procedures are illustrated with examples using the datasets included with the software> 5. Support (in this aspect there is no comparison with R, > the R list is the best known support).R list has a better support than Statalist, but still Statalist is quite active and helpful. Plus they are more polite... no RTFM or stuff like that. If you own a Stata license, you can get direct support from somebody at StataCorp (in addition to Statalist). This is specially relevant if you have questions on how Stata is estimating something, bugs, etc.> 6. Numerical stability.Quite stable. The only glitch I've observed is that after new releases their routines are not very reliable... meaning they sometimes change the way something is being computed and might they mess up something that previously was running fine. Right now Stata 9 is pretty stable, but if Stata 10 would come up in the market now, I would probably wait for a couple of months and make sure everything is well tested. One last thing, while I abandoned the Stata world to move to R (due to $$), I have to say that the only thing I really miss about it is its ability to handle large datasets. Stata comes with great Data management routines, and it can hold large amount of data in its memory. Here R is light years behind. This is particularly relevant if you have to "clean-up" large datasets before you actually start doing statistics. hope this helps robert On 11/21/06, Kenneth Cabrera <krcabrer at une.net.co> wrote:> Hi R users: > > I want to know if any of you had used > Stata or Statgraphics. > > What are the advantages and disadvantages with > respect to R on the following aspects? > > 1. Statistical functions or options for advanced > experimental design (fractional, mixed models, > greco-latin squares, split-plot, etc). > 2. Bayesian approach to experimental design. > 3. Experimental design planing options. > 4. Manuals (theory included in the manuals). > 5. Support (in this aspect there is no comparison with R, > the R list is the best known support). > 6. Numerical stability. > 7. Implementation of modern statistical approaches. > > Thank you for your help. > > Kenneth > > -- > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >
On Tue, 21 Nov 2006, Kenneth Cabrera wrote:> Hi R users: > > I want to know if any of you had used > Stata or Statgraphics.We use Stata for teaching courses aimed at graduate students in other departments, and also (as a consequence) on a lot of medical/public health research projects. It is easier to learn than R, and has good support for all the methods we teach in the service courses [unlike, eg, SPSS or Minitab]. Part of the reason it is easier to learn is that there is a very regular syntax. [There is also a GUI, now, but it isn't a very good one and we were using Stata for teaching before it had a GUI].> What are the advantages and disadvantages with > respect to R on the following aspects? > > 1. Statistical functions or options for advanced > experimental design (fractional, mixed models, > greco-latin squares, split-plot, etc).Stata is not very good at this sort of thing. Neither is R, yet, since lme() is really for longitudinal data and lmer() is still developing.> 2. Bayesian approach to experimental design.Not much here, either, in Stata> 3. Experimental design planing options.Or here.> 4. Manuals (theory included in the manuals).Stata is excellent. They usually give formulas as well as references (and sometimes algorithms and computational notes that are not in the references). The only problem is they keep growing and dividing, so the cost of a complete set goes up quite rapidly with each release (and the volume that you want is always on the other side of the room or lent out to someone). The online help is also good. It suffers relative to R from the examples not necessarily being directly executable.> 5. Support (in this aspect there is no comparison with R, > the R list is the best known support).The Stata list is pretty good, too. You can see it at http://www.hsph.harvard.edu/statalist/> 6. Numerical stability.For most purposes this is not really an issue and I haven't pushed Stata to the edge. I haven't seen any problems. Stata does have a smaller range of built-in optimizers, and they seem to have stopped at the Marquadt algorithm. This has only once been a problem for me (in fitting log-binomial generalized linear models), but could be a problem in implementing new methods.> 7. Implementation of modern statistical approaches. >It depends on the area. It's not bad at all in biostatistics and in some areas of econometrics. As with R there is also a lot of user-written code, some of it of excellent quality. The Stata language is better than it looks, but some things can be easily programmed in it and some can't. The last two versions of Stata have introduced language changes in order to be able to implement better graphics and linear mixed models, and you can also now call C code from Stata, so things are improving. Algorithms that are suited to a `one rectangular dataset' view of the world are often very fast in Stata, but the penalty for not vectorizing is even stiffer than in R. -thomas Thomas Lumley Assoc. Professor, Biostatistics tlumley at u.washington.edu University of Washington, Seattle