Re: [R] A comment about R:

From: Naji <>
Date: Thu 05 Jan 2006 - 22:59:40 EST

Hi all,

Roger thanks for the reproduction.
As a user of Stata & R, for common analysis I do use Stata and often, I have to adapt some computations or to do some complex hierarchical modeling and then I switch to R.
For me switching from Stata (or other statistical software, SO) to R (or other statistical language) requests a double effort: - Programming (laziness?) : writing and testing the code; considering the data as N array or any data frame in order to optimize performance - Statistical testing : I test the model over a simulated data set and validate that the statistical process is giving me back the adequate parameter estimates. An additional step one doesn't need when using an established SO.

For me using Stata (or any other SO), has the advantage of using a high quality code written & tested by an organization & their clients. Getting back to Roger replication, I find such replication very useful. Test whether the R-code is giving back adequate results. So it's a very good starting point before adapting the R-code to one's needs.

Stata advantage : one can download additional ado files ('package' like) and with the permission of the author, adapt them or translate them into R-code. Not only R &Stata are good products, they also show a valuable asset : the users community

Happy new year
Le 5/01/06 10:46, « Robert Chung » <> a écrit :

> Roger Bivand wrote:
>> Gabor Grothendieck wrote:
>>> For example, consider this introductory session in Stata:
>> Could I ask for comments on:
>> source(url(""), echo=TRUE)
>> as a reproduction of the Stata capabilities session?
> Roger, I think your reproduction of the Stata session is excellent.
> However, in a deeper sense, perhaps it's *too* faithful a replication. I
> don't normally do analyses exactly the same way in R and in Stata, so
> although it's possible to contort R into producing Stata-like output, why
> would anyone want to? For example, in the sample Stata session, they run a
> t-test before plotting any data. In R, I'd tend to plot early and test
> hypotheses after. Rather that print out the top and bottom 5 mileage cars,
> I might plot(weight,mpg,col=as.integer(foreign)) and identify() the
> bivariate oddities. Rather than start into linear models, I might do some
> lowess() lines. I'd probably do a splom() pretty early. Depending on what
> I was doing, maybe I'd do something like
> stars(auto[,-c(1,12)],labels=make).
> Stata and R are both fine products, but I sometimes wonder how the tools
> one chooses affect the analyses one does.
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> mailing list PLEASE do read the posting guide! Received on Thu Jan 05 23:02:35 2006

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