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RE: st: R for Stata Users


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: R for Stata Users
Date   Fri, 26 Feb 2010 14:53:42 -0000

Joe Hilbe has been a member of this list. I do not know Robert Muenchen.
Clearly either may wish to comment. 

I think it's important to underline that Statalist is not based on the
idea that Stata is perfect and that all other software is too lousy to
consider. Many Stata users also happy use other software too. In
mentioning other software we should remain rational and keep our
language temperate. Doing anything else just will lead to a diminished
reputation for the community and this list. 

Nick 
n.j.cox@durham.ac.uk 

Airey, David C

Scott Merryman posted 1/14/10 with no replies:

> R for Stata Users
> Series: Statistics and Computing
> Muenchen, Robert A., Hilbe, Joseph M.
> 
> 2010, Approx. 565 p., Hardcover
> 
> ISBN: 978-1-4419-1317-3
> 
> Not yet published. Available: March 21, 2010
> 
> 
>
http://www.springer.com/statistics/computational/book/978-1-4419-1317-3

Apparently Stata has "jargon", and R has "formal" terminology. R also
has "publication quality graphs". Why does everyone keep saying this as
if these are somehow unavailable in other packages? It is easier to make
a crappy graph in R than Stata in my hands at least. Also, if I were a
professional Stata user, why the hell would I want to know how to do
basic statistics and basic graphs in R? There are now 900 books
describing such. Rather I might want to know how to use R to extend
Stata with packages unavailable in Stata and outside my expertise to do
so, period.

Maybe one of the authors can comment on the title.

--snip--
	* Read data from various types of text files and Stata data sets
	* Manage your data through transformations, recodes, and
combining data sets from both the add-cases and add-variables approaches
and restructuring data from wide to long formats and vice versa
	* Create publication quality graphs including bar, histogram,
pie, line, scatter, regression, box, error bar, and interaction plots
	* Perform the basic types of analyses to measure strength of
association and group differences and be able to know where to turn to
cover much more complex methods

Stata is the most flexible and extensible data analysis package
available from a commercial vendor. R is a similarly flexible free and
open source package for data analysis, with over 3,000 add-on packages
available. This book shows you how to extend the power of Stata through
the use of R. It introduces R using Stata terminology with which you are
already familiar. It steps through more than 30 programs written in both
languages, comparing and contrasting the two packages' different
approaches. When finished, you will be able to use R in conjunction with
Stata, or separately, to import data, manage and transform it, create
publication quality graphics, and perform basic statistical analyses.

A glossary defines over 50 R terms using Stata jargon and again using
more formal R terminology. The table of contents and index allow you to
find equivalent R functions by looking up Stata commands and vice versa.
The example programs and practice datasets for both R and Stata are
available for download.

Robert A. Muenchen is the author of the book, R for SAS and SPSS Users,
and is a consulting statistician with 29 years of experience. He has
served on the advisory boards of SAS Institute, SPSS Inc., and the
Statistical Graphics Corporation. He currently manages Research
Computing Support at The University of Tennessee.

Joseph M. Hilbe is Solar System Ambassador with NASA/Jet Propulsion
Laboratory, California Institute of Technology, an adjunct professor of
statistics at Arizona State, and emeritus professor at the University of
Hawaii. He is a Fellow of the American Statistical Association and
elected member of the International Statistical Institute. Hilbe was the
first editor of the Stata Technical Bulletin, (later named the Stata
Journal) and is author of a number of textbooks, including Logistic
Regression Models and Negative Binomial Regression.

Written for > Professional/practitioner
--snip--


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