On Apr 16, 2009, at 3:58 PM, Stas Kolenikov wrote:
See, we just jave different expectations of what is to be seen in the
help system, and are used to different formats. Yes, Stata thinks of
data as a rectangular array (although it stores it in memory, unlike
SAS). The inputs to -egen-, as well as the values produced, depend on
the particular function -fcn- and are described in subsections on
those individual functions. That is mentioned at the top of the page.
There is a pretty much standard syntax of most Stata commands (command
name followed by variables it is applied to or expression to be
computed followed by if conditions on observations followed by comma
options ), and -egen- more or less satisfies that syntax. A Stata user
equipped with the basic concepts of the assignment command -generate-
(which -egen- is said to extend) and variable lists (-varlist- here
and there in the help file) would be able to make sense of this all.
I would rather translate R's ave() to Stata's -by- expression. Not all
of the -egen- functionality can be implemented via ave().
R has a by function which is a convenience wrapper for tapply. It will
not necessarily produce an object with the same number of rows as the
input, which is what I thought that egen was doing.
Looks like terseness is a prerequisite to doing anything in R though.
If I am telling you I am a newbie, the book abbreviations although
standard to everybody on this list may not mean much to me. I could
figure out "Regression Modeling Strategies" (although I was not
thinking about it as a book on R -- I probably did not read it far
enough :) ), and V&R is Venables & Ripley. Right?
Yes, and Chambers and Hastie wrote "Statistical Models in S".
The VR bundle is the way to get the MASS package (and IIRC three
others).
The documentation and contributed pages are here:
http://cran.r-project.org/manuals.html
http://cran.r-project.org/other-docs.html
Harrell probably does not think of RMS as an R book either.
--
David Winsemius
On 4/16/09, David Winsemius <dwinsem...@comcast.net> wrote:
Terse is OK by me as long as I get told what goes in (allowable
data types,
argument names and effects) and what comes out. What seemed to be
lacking in
that Stata doc for egen was a description of the purpose or
behavior and
then could find no description of the values produced. Perhaps it
is because
Stata has an approach that everything is a rectangular array? Is
everything
assumed to create a new column of data as in SAS?
At any rate it looked to this casual non-user, reading that
document, that
egen creates a new variable aligned with its argument variables by
applying
various functions within groupings. That is pretty much what ave
does. "ave"
is not restricted to mean as a functional argument. As I said it
was a
guess.
The texts I used to get up to speed in R are several downloaded
from the
Contributed documents (including anything written by Venables), V&R
MASS v
2, Harrell's RMS, Sarkar's Lattice, Chambers&Hastie SMiS and
reading a lot
of Q&A on this list.
--
David Winsemius
On Apr 16, 2009, at 11:57 AM, Stas Kolenikov wrote:
http://www.stata.com/help.cgi?egen -- it creates new
variables dealing
with some special relatively non-standard tasks that don't boil down
to a one-line arithmetic expressions. For that reason, there will be
no equivalent to -egen- in general, as it has so many functions that
are so different. -rowtotal- is of course just a shorthand for
sum(),
except for treatment of missing values ( ifelse(is.na(x),0,x ). But
-anycount- is a moderately complicated double cycle over variables
and
list of values (40 lines of underlying Stata code, including parsing
and labeling the resulting variables)... which will probably
become a
triple R cycle including the cycle over observations, although the
latter can probably be avoided.
Yes, R documentation looks exteremely terse to me as a regular Stata
user. I am used to seeing the concpets explained well, even in the
help files, and certainly more so in the shelved books. As every
option and every part of the syntax is devoted at least three to
five
sentences, and the most common uses are exemplified, I can usually
figure out how to run a particular task relatively quickly. (The
data
management tricks, which is what Peter was asking about above, are
probably an exception: you either know them, or you don't. In this
example, I don't know the corresponding R tricks, although I can
probably brute force the solution if I needed to.) The fraction of
commands in R that I personally have been coming across that are
comparably well documented is about a quarter. For other, it is
either
a guesswork+CRANning+googling around or "Forget it, I'll just go
back
to Stata to do it" after a few futile attempts. May be I just don't
know where to look for the good stuff, but it is certainly outside R
as a package+its documentation.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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