R FAQ
Frequently Asked Questions on R
Version 1.3-20, 2001-08-31
Kurt Hornik


Table of Contents
*****************


R FAQ

1 Introduction
  1.1 Legalese
  1.2 Obtaining this document
  1.3 Citing this document
  1.4 Notation
  1.5 Feedback

2 R Basics
  2.1 What is R?
  2.2 What machines does R run on?
  2.3 What is the current version of R?
  2.4 How can R be obtained?
  2.5 How can R be installed?
    2.5.1 How can R be installed (Unix)
    2.5.2 How can R be installed (Windows)
    2.5.3 How can R be installed (Macintosh)
  2.6 Are there Unix binaries for R?
  2.7 What documentation exists for R?
  2.8 Citing R
  2.9 What mailing lists exist for R?
  2.10 What is CRAN?

3 R and S
  3.1 What is S?
  3.2 What is S-PLUS?
  3.3 What are the differences between R and S?
    3.3.1 Lexical scoping
    3.3.2 Models
    3.3.3 Others
  3.4 Is there anything R can do that S-PLUS cannot?
  3.5 What is R-plus?

4 R Web Interfaces

5 R Add-On Packages
  5.1 Which add-on packages exist for R?
  5.2 How can add-on packages be installed?
  5.3 How can add-on packages be used?
  5.4 How can add-on packages be removed?
  5.5 How can I create an R package?
  5.6 How can I contribute to R?

6 R and Emacs
  6.1 Is there Emacs support for R?
  6.2 Should I run R from within Emacs?
  6.3 Debugging R from within Emacs

7 R Miscellanea
  7.1 Why does R run out of memory?
  7.2 Why does sourcing a correct file fail?
  7.3 How can I set components of a list to NULL?
  7.4 How can I save my workspace?
  7.5 How can I clean up my workspace?
  7.6 How can I get eval() and D() to work?
  7.7 Why do my matrices lose dimensions?
  7.8 How does autoloading work?
  7.9 How should I set options?
  7.10 How do file names work in Windows?
  7.11 Why does plotting give a color allocation error?
  7.12 How do I convert factors to numeric?
  7.13 Are Trellis displays implemented in R?
  7.14 What are the enclosing and parent environments?
  7.15 How can I substitute into a plot label?
  7.16 What are valid names?
  7.17 Are GAMs implemented in R?
  7.18 Why is the output not printed when I source() a file?
  7.19 Why does outer() behave strangely with my function?

8 R Programming
  8.1 How should I write summary methods?
  8.2 How can I debug dynamically loaded code?
  8.3 How can I inspect R objects when debugging?
  8.4 How can I change compilation flags?

9 R Bugs
  9.1 What is a bug?
  9.2 How to report a bug

10 Acknowledgments


R FAQ
*****

1 Introduction
**************

   This document contains answers to some of the most frequently asked
questions about R.

1.1 Legalese
============

   This document is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the Free
Software Foundation; either version 2, or (at your option) any later
version.

   This document is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
for more details.

   A copy of the GNU General Public License is available via WWW at

     `http://www.gnu.org/copyleft/gpl.html'.

You can also obtain it by writing to the Free Software Foundation, Inc., 59
Temple Place -- Suite 330, Boston, MA 02111-1307, USA.

1.2 Obtaining this document
===========================

   The latest version of this document is always available from

     `http://www.ci.tuwien.ac.at/~hornik/R/'

   From there, you can obtain versions converted to plain ASCII text, DVI,
GNU info, HTML, PDF, PostScript as well as the Texinfo source used for
creating all these formats using the GNU Texinfo system
(http://texinfo.org/).

   You can also obtain the R FAQ from the `doc/FAQ' subdirectory of a CRAN
site (*note What is CRAN?::).

1.3 Citing this document
========================

   In publications, please refer to this FAQ as Hornik (2001), "The R FAQ"
and give the above, _official_ URL.

1.4 Notation
============

   Everything should be pretty standard.  `R>' is used for the R prompt,
and a `$' for the shell prompt (where applicable).

1.5 Feedback
============

   Feedback is of course most welcome.

   In particular, note that I do not have access to Windows or Mac systems.
Features specific to the Windows and Mac OS ports of R are described in the
"R for Windows FAQ" (http://www.stats.ox.ac.uk/pub/R/rw-FAQ.html) and the
"R for Macintosh FAQ/DOC" (http://www.eco-dip.unimi.it/R/rmac-FAQ.html).
If you have information on Mac or Windows systems that you think should be
added to this document, please let me know.

2 R Basics
**********

2.1 What is R?
==============

   R is a system for statistical computation and graphics.  It consists of
a language plus a run-time environment with graphics, a debugger, access to
certain system functions, and the ability to run programs stored in script
files.

   The design of R has been heavily influenced by two existing languages:
Becker, Chambers & Wilks' S (*note What is S?::) and Sussman's Scheme
(http://www.cs.indiana.edu/scheme-repository/home.html).  Whereas the
resulting language is very similar in appearance to S, the underlying
implementation and semantics are derived from Scheme.  *Note What are the
differences between R and S?::, for further details.

   The core of R is an interpreted computer language which allows branching
and looping as well as modular programming using functions.  Most of the
user-visible functions in R are written in R.  It is possible for the user
to interface to procedures written in the C, C++, or FORTRAN languages for
efficiency.  The R distribution contains functionality for a large number
of statistical procedures.  Among these are: linear and generalized linear
models, nonlinear regression models, time series analysis, classical
parametric and nonparametric tests, clustering and smoothing.  There is
also a large set of functions which provide a flexible graphical
environment for creating various kinds of data presentations.  Additional
modules ("add-on packages") are available for a variety of specific
purposes (*note R Add-On Packages::).

   R was initially written by Ross Ihaka <Ross.Ihaka@r-project.org> and
Robert Gentleman <Robert.Gentleman@r-project.org> at the Department of
Statistics of the University of Auckland in Auckland, New Zealand.  In
addition, a large group of individuals has contributed to R by sending code
and bug reports.

   Since mid-1997 there has been a core group (the "R Core Team") who can
modify the R source code CVS archive.  The group currently consists of Doug
Bates, John Chambers, Peter Dalgaard, Robert Gentleman, Kurt Hornik,
Stefano Iacus, Ross Ihaka, Friedrich Leisch, Thomas Lumley, Martin
Maechler, Guido Masarotto, Paul Murrell, Brian Ripley, Duncan Temple Lang,
and Luke Tierney.

   R has a home page at `http://www.r-project.org/'.  It is free software
distributed under a GNU-style copyleft, and an official part of the GNU
project ("GNU S").

2.2 What machines does R run on?
================================

   R is being developed for the Unix, Windows and Mac families of operating
systems.

   The current version of R will configure and build under a number of
common Unix platforms including i386-freebsd, i386-linux-gnu,
i386-sun-solaris, powerpc-linux-gnu, powerpc-apple-darwin, mips-sgi-irix,
alpha-linux-gnu, alpha-dec-osf4, rs6000-ibm-aix, hppa-hp-hpux,
sparc-linux-gnu, and sparc-sun-solaris.

   If you know about other platforms, please drop us a note.

2.3 What is the current version of R?
=====================================

   The current released version is 1.3.1.  Conforming to this
`major.minor.patchlevel' numbering scheme, there are three development
versions of R, working towards the next patch (`r-patched'), minor
(`r-devel'), and major (`r-ng') releases of R, respectively.  Version
r-patched is for bug fixes mostly.  New features are typically introduced
in r-devel.  Version r-ng will eventually become the next generation of R.

2.4 How can R be obtained?
==========================

   Sources, binaries and documentation for R can be obtained via CRAN, the
"Comprehensive R Archive Network" (see *Note What is CRAN?::).

   Sources are also available via anonymous rsync.  Use

     rsync -rC rsync.r-project.org::MODULE R

to create a copy of the source tree specified by MODULE in the subdirectory
`R' of the current directory, where MODULE specifies one of the four
existing flavors of the R sources, and can be one of `r-release' (current
released version), `r-patched' (patched released version), and `r-devel'
(development version, less stable), and `r-ng' (next generation, unstable).
The rsync trees are created directly from the master CVS archive and are
updated hourly.  The `-C' option in the `rsync' command is to cause it to
skip the CVS directories.  Further information on `rsync' is available at
`http://rsync.samba.org/rsync/'.

2.5 How can R be installed?
===========================

2.5.1 How can R be installed (Unix)
-----------------------------------

   If binaries are available for your platform (see *Note Are there Unix
binaries for R?::), you can use these, following the instructions that come
with them.

   Otherwise, you can compile and install R yourself, which can be done
very easily under a number of common Unix platforms (see *Note What
machines does R run on?::).  The file `INSTALL' that comes with the R
distribution contains a brief introduction, and the "R Installation and
Administration" guide (*note What documentation exists for R?::) has full
details.

   Note that you need a FORTRAN compiler or `f2c' in addition to a C
compiler to build R.  Also, you need Perl version 5 to build the R object
documentations.  (If this is not available on your system, you can obtain a
PDF version of the object reference manual via CRAN.)

   In the simplest case, untar the R source code, change to the directory
thus created, and issue the following commands (at the shell prompt):

     $ ./configure
     $ make

   If these commands execute successfully, the R binary and a shell script
font-end called `R' are created and copied to the `bin' directory.  You can
copy the script to a place where users can invoke it, for example to
`/usr/local/bin'.  In addition, plain text help pages as well as HTML and
LaTeX versions of the documentation are built.

   Use `make dvi' to create DVI versions of the R manuals, such as
`refman.dvi' (an R object reference index) and `R-exts.dvi', the "R
Extension Writers Guide", in the `doc/manual' subdirectory.  These files
can be previewed and printed using standard programs such as `xdvi' and
`dvips'.  You can also use `make pdf' to build PDF (Portable Document
Format) version of the manuals, and view these using e.g. Acrobat.  Manuals
written in the GNU Texinfo system can also be converted to info files
suitable for reading online with Emacs or stand-alone GNU Info; use `make
info' to create these versions (note that this requires `makeinfo' version
4).

   Finally, use `make check' to find out whether your R system works
correctly.

   You can also perform a "system-wide" installation using `make install'.
By default, this will install to the following directories:

`${prefix}/bin'
     the front-end shell script

`${prefix}/man/man1'
     the man page

`${prefix}/lib/R'
     all the rest (libraries, on-line help system, ...).  This is the "R
     Home Directory" (`R_HOME') of the installed system.

In the above, `prefix' is determined during configuration (typically
`/usr/local') and can be set by running `configure' with the option

     $ ./configure --prefix=/where/you/want/R/to/go

(E.g., the R executable will then be installed into
`/where/you/want/R/to/go/bin'.)

   To install DVI, info and PDF versions of the manuals, use `make
install-dvi', `make install-info' and `make install-pdf', respectively.

2.5.2 How can R be installed (Windows)
--------------------------------------

   The `bin/windows' directory of a CRAN site contains binaries for a base
distribution and a large number of add-on packages from CRAN to run on
Windows 95, 98, NT4, 2000 and ME (at least) on Intel and clones (but not on
other platforms).  The Windows version of R was created by Robert
Gentleman, and is now being developed and maintained by Guido Masarotto
<Guido.Masarotto@r-project.org> and Brian D. Ripley
<Brian.Ripley@r-project.org>.

   For most installations the installer `rwinst.exe' will be the easiest
tool to use.

   See the "R for Windows FAQ"
(http://www.stats.ox.ac.uk/pub/R/rw-FAQ.html) for more details.

2.5.3 How can R be installed (Macintosh)
----------------------------------------

   The `bin/macos' directory of a CRAN site contains bin-hexed (`hqx') and
stuffit (`sit') archives for a base distribution and a large number of
add-on packages to run under MacOS 8.6 to MacOS 9.1 or MacOS X natively.
The Mac version of R and the Mac binaries are maintained by Stefano Iacus
<Stefano.Iacus@r-project.org>.

   The "R for Macintosh FAQ/DOC"
(http://www.eco-dip.unimi.it/R/rmac-FAQ.html) has more details.

   Binaries of base distributions for MacOS X (Darwin) with X11 are made
available by Jan de Leeuw <deleeuw@stat.ucla.edu> in the `bin/macosx'
directory of a CRAN site.

2.6 Are there Unix binaries for R?
==================================

   The `bin/linux' directory of a CRAN site contains Debian 2.2/2.3
packages for the i386 platform (now part of the Debian distribution and
maintained by Doug Bates), Red Hat 6.x/7.x packages for the alpha and i386
platforms (maintained by Naoki Takebayashi and Martyn Plummer,
respectively), SuSE 7.2 i386 packages by Albrecht Gebhardt, Mandrake 8.0
and 7.2 i386 packages by Michele Alzetta and Maximino Ameneiro Gomez, and
LinuxPPC 5.0 RPMs by Alex Buerkle.

   The Debian packages can be accessed through APT, the Debian package
maintenance tool.  Simply add the line

     deb http://cran.r-project.org/bin/linux/debian DISTRIBUTION main

(where DISTRIBUTION is either `stable' or `unstable'; feel free to use a
CRAN mirror instead of the master) to the file `/etc/apt/sources.list'.
Once you have added that line the programs `apt-get', `apt-cache', and
`dselect' (using the apt access method) will automatically detect and
install updates of the R packages.

   The `bin/osf' directory of a CRAN site contains RPMs by Albrecht
Gebhardt for alpha systems running Alpha Unix (OSF/Tru64).

   No other binary distributions have thus far been made publically
available.

2.7 What documentation exists for R?
====================================

   Online documentation for most of the functions and variables in R
exists, and can be printed on-screen by typing `help(NAME)' (or `?NAME') at
the R prompt, where NAME is the name of the topic help is sought for.  (In
the case of unary and binary operators and control-flow special forms, the
name may need to be be quoted.)

   This documentation can also be made available as one reference manual
for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX, see
*Note How can R be installed?::.  An up-to-date HTML version is always
available for web browsing at `http://stat.ethz.ch/R/manual/'.

   The R distribution also comes with the following manuals.

   * "An Introduction to R" (`R-intro') includes information on data types,
     programming elements, statistical modeling and graphics.  This
     document is based on the "Notes on S-PLUS" by Bill Venables and David
     Smith.

   * "Writing R Extensions" (`R-exts') currently describes the process of
     creating R add-on packages, writing R documentation, R's system and
     foreign language interfaces, and the R API.

   * "R Data Import/Export" (`R-data') is a guide to importing and
     exporting data to and from R.

   * "The R Language Definition" (`R-lang'), a first version of the
     "Kernighan & Ritchie of R", explains evaluation, parsing, object
     oriented programming, computing on the language, and so forth.

   * "R Installation and Administration" (`R-admin').

   In addition to material written specifically for R, documentation for
S/S-PLUS (see *Note R and S::) can be used in combination with this FAQ
(*note What are the differences between R and S?::).  We recommend

     W. N. Venables and B. D. Ripley (1999), "Modern Applied Statistics with
     S-PLUS.  Third Edition".  Springer, ISBN 0-387-98825-4.

This has a home page at `http://www.stats.ox.ac.uk/pub/MASS3/' providing
additional material, in particular "R Complements" which describe how to
use the book with R.  These complements contain both descriptions of some
of the differences between R and S-PLUS, and the modifications needed to
run the examples in the book.  Its companion is

     W. N. Venables and B. D. Ripley (2000), "S Programming".  Springer,
     ISBN 0-387-98966-8.

This provides an in-depth guide to writing software in the S language which
forms the basis of both the commercial S-PLUS and the Open Source R data
analysis software systems.  See
`http://www.stats.ox.ac.uk/pub/MASS3/Sprog/' for more information.

   More introductory books are

     P. Spector (1994), "An introduction to S and S-PLUS", Duxbury Press.

     A. Krause and M. Olsen (1997), "The Basics of S and S-PLUS", Springer.

   The book

     J. C. Pinheiro and D. M. Bates (2000), "Mixed-Effects Models in S and
     S-PLUS", Springer, ISBN 0-387-98957-0

provides a comprehensive guide to the use of the *nlme* package for linear
and nonlinear mixed-effects models.  This has a home page at
`http://nlme.stat.wisc.edu/MEMSS/'.

   As an example of how R can be used in teaching an advanced introductory
statistics course, see

     D. Nolan and T. Speed (2000), "Stat Labs: Mathematical Statistics
     Through Applications", Springer Texts in Statistics, ISBN 0-387-98974-9

This integrates theory of statistics with the practice of statistics
through a collection of case studies ("labs"), and uses R to analyze the
data.  More information can be found at
`http://www.stat.Berkeley.EDU/users/statlabs/'.

   Last, but not least, Ross' and Robert's experience in designing and
implementing R is described in Ihaka & Gentleman (1996), "R: A Language for
Data Analysis and Graphics", _Journal of Computational and Graphical
Statistics_, *5*, 299-314.  *Note Citing R::.

   An annotated bibliography (BibTeX format) of R-related publications
which includes most of the above references can be found at

     `http://www.r-project.org/doc/bib/R.bib'

2.8 Citing R
============

   To cite R in publications, use

     @article{,
       author =    {Ross Ihaka and Robert Gentleman},
       title =     {R: A Language for Data Analysis and Graphics},
       journal =   {Journal of Computational and Graphical Statistics},
       year =      1996,
       volume =    5,
       number =    3,
       pages =     {299--314}
     }

2.9 What mailing lists exist for R?
===================================

   Thanks to Martin Maechler <Martin.Maechler@r-project.org>, there are
three mailing lists devoted to R.

`r-announce'
     This list is for announcements about the development of R and the
     availability of new code.

`r-devel'
     This list is for discussions about the future of R and pre-testing of
     new versions.  It is meant for those who maintain an active position in
     the development of R.

`r-help'
     The `main' R mailing list, for announcements about the development of R
     and the availability of new code, questions and answers about problems
     and solutions using R, enhancements and patches to the source code and
     documentation of R, comparison and compatibility with S and S-PLUS,
     and for the posting of nice examples and benchmarks.

Note that the r-announce list is gatewayed into r-help, so you don't need
to subscribe to both of them.

   Send email to <r-help@lists.r-project.org> to reach everyone on the
r-help mailing list.  To subscribe (or unsubscribe) to this list send
`subscribe' (or `unsubscribe') in the _body_ of the message (not in the
subject!) to <r-help-request@lists.r-project.org>.  Information about the
list can be obtained by sending an email with `info' as its contents to
<r-help-request@lists.r-project.org>.

   Subscription and posting to the other lists is done analogously, with
`r-help' replaced by `r-announce' and `r-devel', respectively.

   It is recommended that you send mail to r-help rather than only to the R
Core developers (who are also subscribed to the list, of course).  This may
save them precious time they can use for constantly improving R, and will
typically also result in much quicker feedback for yourself.

   Of course, in the case of bug reports it would be very helpful to have
code which reliably reproduces the problem.  Also, make sure that you
include information on the system and version of R being used.  See *Note R
Bugs:: for more details.

   Archives of the above three mailing lists are made available on the net
in a monthly schedule via the `doc/html/mail.html' file in CRAN.
Searchable archives of the lists are available via
`http://www.ens.gu.edu.au/robertk/R/'.

   The R Core Team can be reached at <r-core@lists.r-project.org> for
comments and reports.

2.10 What is CRAN?
==================

   The "Comprehensive R Archive Network" (CRAN) is a collection of sites
which carry identical material, consisting of the R distribution(s), the
contributed extensions, documentation for R, and binaries.

   The CRAN master site at TU Wien, Austria, can be found at the URL

     `http://cran.r-project.org/'

and is currently being mirrored daily at

     `http://cran.at.r-project.org/'(TU Wien, Austria)
     `http://cran.au.r-project.org/'(PlanetMirror, Australia)
     `http://cran.ch.r-project.org/'(ETH Zu"rich, Switzerland)
     `http://cran.dk.r-project.org/'(SunSITE, Denmark)
     `http://cran.hu.r-project.org/'(Semmelweis U, Hungary)
     `http://cran.uk.r-project.org/'(U of Bristol, United
                                  Kingdom)
     `http://cran.us.r-project.org/'(U of Wisconsin, USA)
     `http://cran.za.r-project.org/'(Rhodes U, South Africa)

Please use the CRAN site closest to you to reduce network load.

   From CRAN, you can obtain the latest official release of R, daily
snapshots of R (copies of the current CVS trees), as gzipped and bzipped
tar files, a wealth of additional contributed code, as well as prebuilt
binaries for various operating systems (Linux, Digital Unix, and MS
Windows).  CRAN also provides access to documentation on R, existing
mailing lists and the R Bug Tracking system.

   To "submit" to CRAN, simply upload to
`ftp://cran.r-project.org/incoming/' and send an email to
<wwwadmin@cran.r-project.org>.

     *Note:*  It is very important that you indicate the copyright
     (license) information (GPL, BSD, Artistic, ...)  in your submission.

   Please always use the URL of the master site when referring to CRAN.

3 R and S
*********

3.1 What is S?
==============

   S is a very high level language and an environment for data analysis and
graphics.  In 1998, the Association for Computing Machinery (ACM) presented
its Software System Award to John M. Chambers, the principal designer of S,
for

     the S system, which has forever altered the way people analyze,
     visualize, and manipulate data ...

     S is an elegant, widely accepted, and enduring software system, with
     conceptual integrity, thanks to the insight, taste, and effort of John
     Chambers.

   The evolution of the S language is characterized by four books by John
Chambers and coauthors, which are also the primary references for S.

   * Richard A. Becker and John M. Chambers (1984), "S.  An Interactive
     Environment for Data Analysis and Graphics," Monterey: Wadsworth and
     Brooks/Cole.

     This is also referred to as the "_Brown Book_", and of historical
     interest only.

   * Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), "The New
     S Language," London: Chapman & Hall.

     This book is often called the "_Blue Book_", and introduced what is
     now known as S version 2.

   * John M. Chambers and Trevor J. Hastie (1992), "Statistical Models in
     S,"  London: Chapman & Hall.

     This is also called the "_White Book_", and introduced S version 3,
     which added structures to facilitate statistical modeling in S.

   * John M. Chambers (1998), "Programming with Data," New York: Springer,
     ISBN 0-387-98503-4
     (<http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/>).

     This "_Green Book_" describes version 4 of S, a major revision of S
     designed by John Chambers to improve its usefulness at every stage of
     the programming process.

   See `http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html' for
further information on "Stages in the Evolution of S".

   There is a huge amount of user-contributed code for S, available at the
S Repository (http://lib.stat.cmu.edu/S/) at CMU.

3.2 What is S-PLUS?
===================

   S-PLUS is a value-added version of S sold by Insightful Corporation.
Based on the S language, S-PLUS provides functionality in a wide variety of
areas, including robust regression, modern non-parametric regression, time
series, survival analysis, multivariate analysis, classical statistical
tests, quality control, and graphics drivers.  Add-on modules add
additional capabilities for wavelet analysis, spatial statistics, GARCH
models, and design of experiments.

   See the Insightful S-PLUS page
(http://www.insightful.com/products/splus/) for further information.

3.3 What are the differences between R and S?
=============================================

   We can regard S as a language with three current implementations or
"engines", the "old S engine" (S version 3; S-PLUS 3.x and 4.x), the "new S
engine" (S version 4; S-PLUS 5.x and above), and R.  Given this
understanding, asking for "the differences between R and S" really amounts
to asking for the specifics of the R implementation of the S language,
i.e., the difference between the R and S engines.

   For the remainder of this section, "S" refers to the S engines and not
the S language.

3.3.1 Lexical scoping
---------------------

   Contrary to other implementations of the S language, R has adopted the
evaluation model of Scheme.

   This difference becomes manifest when _free_ variables occur in a
function.  Free variables are those which are neither formal parameters
(occurring in the argument list of the function) nor local variables
(created by assigning to them in the body of the function).  Whereas S
(like C) by default uses _static_ scoping, R (like Scheme) has adopted
_lexical_ scoping.  This means the values of free variables are determined
by a set of global variables in S, but in R by the bindings that were in
effect at the time the function was created.

   Consider the following function:

     cube <- function(n) {
       sq <- function() n * n
       n * sq()
     }

   Under S, `sq()' does not "know" about the variable `n' unless it is
defined globally:

     S> cube(2)
     Error in sq():  Object "n" not found
     Dumped
     S> n <- 3
     S> cube(2)
     [1] 18

   In R, the "environment" created when `cube()' was invoked is also looked
in:

     R> cube(2)
     [1] 8

   As a more "interesting" real-world problem, suppose you want to write a
function which returns the density function of the r-th order statistic
from a sample of size n from a (continuous) distribution.  For simplicity,
we shall use both the cdf and pdf of the distribution as explicit
arguments.  (Example compiled from various postings by Luke Tierney.)

   The S-PLUS documentation for `call()' basically suggests the following:

     dorder <- function(n, r, pfun, dfun) {
       f <- function(x) NULL
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       PF <- call(substitute(pfun), as.name("x"))
       DF <- call(substitute(dfun), as.name("x"))
       f[[length(f)]] <-
         call("*", con,
              call("*", call("^", PF, r - 1),
                   call("*", call("^", call("-", 1, PF), n - r),
                        DF)))
       f
     }

Rather tricky, isn't it?  The code uses the fact that in S, functions are
just lists of special mode with the function body as the last argument, and
hence does not work in R (one could make the idea work, though).

   A version which makes heavy use of `substitute()' and seems to work
under both S and R is

     dorder <- function(n, r, pfun, dfun) {
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       eval(substitute(function(x) K * PF(x)^a * (1 - PF(x))^b * DF(x),
                       list(PF = substitute(pfun), DF = substitute(dfun),
                            a = r - 1, b = n - r, K = con)))
     }

(the `eval()' is not needed in S).

   However, in R there is a much easier solution:

     dorder <- function(n, r, pfun, dfun) {
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       function(x) {
         con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
       }
     }

This seems to be the "natural" implementation, and it works because the
free variables in the returned function can be looked up in the defining
environment (this is lexical scope).

   Note that what you really need is the function _closure_, i.e., the body
along with all variable bindings needed for evaluating it.  Since in the
above version, the free variables in the value function are not modified,
you can actually use it in S as well if you abstract out the closure
operation into a function `MC()' (for "make closure"):

     dorder <- function(n, r, pfun, dfun) {
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       MC(function(x) {
            con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
          },
          list(con = con, pfun = pfun, dfun = dfun, r = r, n = n))
     }

   Given the appropriate definitions of the closure operator, this works in
both R and S, and is much "cleaner" than a substitute/eval solution (or one
which overrules the default scoping rules by using explicit access to
evaluation frames, as is of course possible in both R and S).

   For R, `MC()' simply is

     MC <- function(f, env) f

(lexical scope!), a version for S is

     MC <- function(f, env = NULL) {
       env <- as.list(env)
       if (mode(f) != "function")
         stop(paste("not a function:", f))
       if (length(env) > 0 && any(names(env) == ""))
         stop(paste("not all arguments are named:", env))
       fargs <- if(length(f) > 1) f[1:(length(f) - 1)] else NULL
       fargs <- c(fargs, env)
       if (any(duplicated(names(fargs))))
         stop(paste("duplicated arguments:", paste(names(fargs)),
              collapse = ", "))
       fbody <- f[length(f)]
       cf <- c(fargs, fbody)
       mode(cf) <- "function"
       return(cf)
     }

   Similarly, most optimization (or zero-finding) routines need some
arguments to be optimized over and have other parameters that depend on the
data but are fixed with respect to optimization.  With R scoping rules,
this is a trivial problem; simply make up the function with the required
definitions in the same environment and scoping takes care of it.  With S,
one solution is to add an extra parameter to the function and to the
optimizer to pass in these extras, which however can only work if the
optimizer supports this.

   Lexical scoping allows using function closures and maintaining local
state.  A simple example (taken from Abelson and Sussman) is obtained by
typing `demo(scoping)' at the R prompt.  Further information is provided in
the standard R reference "R: A Language for Data Analysis and Graphics"
(*note What documentation exists for R?::) and in Robert Gentleman and Ross
Ihaka (2000), "Lexical Scope and Statistical Computing", _Journal of
Computational and Graphical Statistics_, *9*, 491-508.

   Lexical scoping also implies a further major difference.  Whereas S
stores all objects as separate files in a directory somewhere (usually
`.Data' under the current directory), R does not.  All objects in R are
stored internally.  When R is started up it grabs a very large piece of
memory and uses it to store the objects.  R performs its own memory
management of this piece of memory.  Having everything in memory is
necessary because it is not really possible to externally maintain all
relevant "environments" of symbol/value pairs.  This difference also seems
to make R _faster_ than S.

   The down side is that if R crashes you will lose all the work for the
current session.  Saving and restoring the memory "images" (the functions
and data stored in R's internal memory at any time) can be a bit slow,
especially if they are big.  In S this does not happen, because everything
is saved in disk files and if you crash nothing is likely to happen to
them.  (In fact, one might conjecture that the S developers felt that the
price of changing their approach to persistent storage just to accommodate
lexical scope was far too expensive.)  Hence, when doing important work,
you might consider saving often (see *Note How can I save my workspace?::)
to safeguard against possible crashes.  Other possibilities are logging
your sessions, or have your R commands stored in text files which can be
read in using `source()'.

     *Note:*  If you run R from within Emacs (see *Note R and Emacs::), you
     can save the contents of the interaction buffer to a file and
     conveniently manipulate it using `ess-transcript-mode', as well as
     save source copies of all functions and data used.

3.3.2 Models
------------

   There are some differences in the modeling code, such as

   * Whereas in S, you would use `lm(y ~ x^3)' to regress `y' on `x^3', in
     R, you have to insulate powers of numeric vectors (using `I()'), i.e.,
     you have to use `lm(y ~ I(x^3))'.

   * The glm family objects are implemented differently in R and S.  The
     same functionality is available but the components have different
     names.

   * Option `na.action' is set to `"na.omit"' by default in R, but not set
     in S.

   * Terms objects are stored differently.  In S a terms object is an
     expression with attributes, in R it is a formula with attributes.  The
     attributes have the same names but are mostly stored differently.  The
     major difference in functionality is that a terms object is
     subscriptable in S but not in R.  If you can't imagine why this would
     matter then you don't need to know.

   * Finally, in R `y~x+0' is an alternative to `y~x-1' for specifying a
     model with no intercept.  Models with no parameters at all can be
     specified by `y~0'.

3.3.3 Others
------------

   Apart from lexical scoping and its implications, R follows the S
language definition in the Blue and White Books as much as possible, and
hence really is an "implementation" of S.  There are some intentional
differences where the behavior of S is considered "not clean".  In general,
the rationale is that R should help you detect programming errors, while at
the same time being as compatible as possible with S.

   Some known differences are the following.

   * In R, if `x' is a list, then `x[i] <- NULL' and `x[[i]] <- NULL'
     remove the specified elements from `x'.  The first of these is
     incompatible with S, where it is a no-op.  (Note that you can set
     elements to `NULL' using `x[i] <- list(NULL)'.)

   * In S, the functions named `.First' and `.Last' in the `.Data'
     directory can be used for customizing, as they are executed at the
     very beginning and end of a session, respectively.

     In R, the startup mechanism is as follows.  R first sources the system
     startup file ``$R_HOME'/library/base/R/Rprofile'.  Then, it searches
     for a site-wide startup profile unless the command line option
     `--no-site-file' was given.  The name of this file is taken from the
     value of the `R_PROFILE' environment variable.  If that variable is
     unset, the default is ``$R_HOME'/etc/Rprofile'
     (``$R_HOME'/etc/Rprofile.site' in R 1.4.0).   </FIXME> This code is
     loaded in package *base*.  Then, unless `--no-init-file' was given, R
     searches for a file called `.Rprofile' in the current directory or in
     the user's home directory (in that order) and sources it into the user
     workspace.  It then loads a saved image of the user workspace from
     `.RData'  in case there is one (unless `--no-restore' was specified).
     If needed, the functions `.First()' and `.Last()' should be defined in
     the appropriate startup profiles.

   * In R, `T' and `F' are just variables being set to `TRUE' and `FALSE',
     respectively, but are not reserved words as in S and hence can be
     overwritten by the user.  (This helps e.g. when you have factors with
     levels `"T"' or `"F"'.)  Hence, when writing code you should always
     use `TRUE' and `FALSE'.

   * In R, `dyn.load()' can only load _shared libraries_, as created for
     example by `R CMD SHLIB'.

   * In R, `attach()' currently only works for lists and data frames, but
     not for directories.  (In fact, `attach()' also works for R data files
     created with `save()', which is analogous to attaching directories in
     S.)  Also, you cannot attach at position 1.

   * Categories do not exist in R, and never will as they are deprecated now
     in S.  Use factors instead.

   * In R, `For()' loops are not necessary and hence not supported.

   * In R, `assign()' uses the argument `envir=' rather than `where=' as in
     S.

   * The random number generators are different, and the seeds have
     different length.

   * R passes integer objects to C as `int *' rather than `long *' as in S.

   * R has no single precision storage mode.  However, as of version 0.65.1,
     there is a single precision interface to C/FORTRAN subroutines.

   * By default, `ls()' returns the names of the objects in the current
     (under R) and global (under S) environment, respectively.  For example,
     given

          x <- 1; fun <- function() {y <- 1; ls()}

     then `fun()' returns `"y"' in R and `"x"' (together with the rest of
     the global environment) in S.

   * R allows for zero-extent matrices (and arrays, i.e., some elements of
     the `dim' attribute vector can be 0).  This has been determined a
     useful feature as it helps reducing the need for special-case tests for
     empty subsets.  For example, if `x' is a matrix, `x[, FALSE]' is not
     `NULL' but a "matrix" with 0 columns.  Hence, such objects need to be
     tested for by checking whether their `length()' is zero (which works
     in both R and S), and not using `is.null()'.

   * Named vectors are considered vectors in R but not in S (e.g.,
     `is.vector(c(a = 1:3))' returns `FALSE' in S and `TRUE' in R).

   * Data frames are not considered as matrices in R (i.e., if `DF' is a
     data frame, then `is.matrix(DF)' returns `FALSE' in R and `TRUE' in S).

   * R by default uses treatment contrasts in the unordered case, whereas S
     uses the Helmert ones.  This is a deliberate difference reflecting the
     opinion that treatment contrasts are more natural.

   * In R, the last argument (which corresponds to the right hand side) of
     an assignment function must be named `value'.  E.g., `fun(a) <- b' is
     evaluated as `(fun<-)(a, value = b)'.

   * In S, `substitute()' searches for names for substitution in the given
     expression in three places: the actual and the default arguments of
     the matching call, and the local frame (in that order).  R looks in
     the local frame only, with the special rule to use a "promise" if a
     variable is not evaluated.  Since the local frame is initialized with
     the actual arguments or the default expressions, this is usually
     equivalent to S, until assignment takes place.

   * In R, `eval(EXPR, sys.parent())' does not work.  Instead, one should
     use either `eval(EXPR, sys.frame(sys.parent())),' which also works in
     S, or `eval(EXPR, parent.frame())', which is more efficient but does
     not work in S.

   * In S, the index variable in a `for()' loop is local to the inside of
     the loop.  In R it is local to the environment where the `for()'
     statement is executed.

   * In S, `tapply(simplify=TRUE)' returns a vector where R returns a
     one-dimensional array (which can have named dimnames).

   * In S(-PLUS) the C locale is used, whereas in R the current operating
     system locale is used for determining which characters are
     alphanumeric and how they are sorted.  This affects the set of valid
     names for R objects (for example accented chars may be allowed in R)
     and ordering in sorts and comparisons (such as whether `"aA" < "Bb"' is
     true or false).  From version 1.2.0 the locale can be (re-)set in R by
     the `Sys.setlocale()' function.

   * In S, `missing(ARG)' remains `TRUE' if ARG is subsequently modified;
     in R it doesn't.

   * From R version 1.3.0, `data.frame' strips `I()' when creating (column)
     names.


   There are also differences which are not intentional, and result from
missing or incorrect code in R.  The developers would appreciate hearing
about any deficiencies you may find (in a written report fully documenting
the difference as you see it).  Of course, it would be useful if you were
to implement the change yourself and make sure it works.

3.4 Is there anything R can do that S-PLUS cannot?
==================================================

   Since almost anything you can do in R has source code that you could
port to S-PLUS with little effort there will never be much you can do in R
that you couldn't do in S-PLUS if you wanted to.  (Note that using lexical
scoping may simplify matters considerably, though.)

   R offers several graphics features that S-PLUS does not, such as finer
handling of line types, more convenient color handling (via palettes),
gamma correction for color, and, most importantly, mathematical annotation
in plot texts, via input expressions reminiscent of TeX constructs.  See
the help page for `plotmath', which features an impressive on-line example.
More details can be found in Paul Murrell and Ross Ihaka (2000), "An
Approach to Providing Mathematical Annotation in Plots", _Journal of
Computational and Graphical Statistics_, *9*, 582-599.

3.5 What is R-plus?
===================

   There is no such thing.

4 R Web Interfaces
******************

   *Rcgi* is a CGI WWW interface to R by Mark J. Ray
<mjr@stats.mth.uea.ac.uk>.  Recent versions have the ability to use
"embedded code": you can mix user input and code, allowing the HTML author
to do anything from load in data sets to enter most of the commands for
users without writing CGI scripts.  Graphical output is possible in
PostScript or GIF formats and the executed code is presented to the user
for revision.

   Demo and download are available from
`http://www.mth.uea.ac.uk/~h089/Rcgi/'.

   *Rweb* is developed and maintained by Jeff Banfield
<jeff@math.montana.edu>.  The Rweb Home Page
(http://www.math.montana.edu/Rweb/) provides access to all three versions
of Rweb--a simple text entry form that returns output and graphs, a more
sophisticated Javascript version that provides a multiple window
environment, and a set of point and click modules that are useful for
introductory statistics courses and require no knowledge of the R language.
All of the Rweb versions can analyze Web accessible datasets if a URL is
provided.

   The paper "Rweb: Web-based Statistical Analysis", providing a detailed
explanation of the different versions of Rweb and an overview of how Rweb
works, was published in the Journal of Statistical Software
(`http://www.stat.ucla.edu/journals/jss/v04/i01/').

5 R Add-On Packages
*******************

5.1 Which add-on packages exist for R?
======================================

   The R distribution comes with the following extra packages:

*ctest*
     A collection of Classical TESTs, including the Ansari-Bradley,
     Bartlett, chi-squared, Fisher, Kruskal-Wallis, Kolmogorov-Smirnov, t,
     and Wilcoxon tests.

*eda*
     Exploratory Data Analysis.  Currently only contains functions for
     robust line fitting, and median polish and smoothing.

*lqs*
     Resistant regression and covariance estimation.

*methods*
     Formally defined methods and classes for R objects, plus other
     programming tools, as described in the Green Book.

*modreg*
     MODern REGression: smoothing and local methods.

*mva*
     MultiVariate Analysis.  Currently contains code for principal
     components, canonical correlations, metric multidimensional scaling,
     factor analysis, and hierarchical and k-means clustering.

*nls*
     Nonlinear regression routines.

*splines*
     Regression spline functions and classes.

*stepfun*
     Code for dealing with STEP FUNctions, including empirical cumulative
     distribution functions.

*tcltk*
     Interface and language bindings to Tcl/Tk GUI elements.

*ts*
     Time Series.

   The following packages are available from the CRAN `src/contrib' area.

*AnalyzeFMRI*
     Functions for I/O, visualisation and analysis of functional Magnetic
     Resonance Imaging (fMRI) datasets stored in the ANALYZE format.

*CoCoAn*
     Constrained Correspondence Analysis.

*Devore5*
     Data sets and sample analyses from "Probability and Statistics for
     Engineering and the Sciences (5th ed)" by Jay L. Devore, 2000, Duxbury.

*GLMMGibbs*
     Generalised Linear Mixed Models by Gibbs sampling.

*GeneSOM*
     Clustering genes using Self-Organizing Maps (SOMs).

*KernSmooth*
     Functions for kernel smoothing (and density estimation) corresponding
     to the book "Kernel Smoothing" by M. P. Wand and M. C. Jones, 1995.

*MASS*
     Functions and datasets from the main package of Venables and Ripley,
     "Modern Applied Statistics with S-PLUS".  Contained in the `VR' bundle.

*Matrix*
     A Matrix package.

*NISTnls*
     A set of test nonlinear least squares examples from NIST, the U.S.
     National Institute for Standards and Technology.

*Oarray*
     Arrays with arbitrary offsets.

*PHYLOGR*
     Manipulation and analysis of phylogenetically simulated data sets (as
     obtained from PDSIMUL in package PDAP) and phylogenetically-based
     analyses using GLS.

*PTAk*
     A multiway method to decompose a tensor (array) of any order, as a
     generalisation of SVD also supporting non-identity metrics and
     penalisations.  Also includes some other multiway methods.

*RArcInfo*
     Functions to import Arc/Info V7.x coverages and data.

*RandomFields*
     Creating random fields using various methods.

*RMySQL*
     An interface between R and the MySQL database system.

*RODBC*
     An ODBC database interface.

*RPgSQL*
     Provides methods for accessing data stored in PostgreSQL tables.

*RmSQL*
     An interface between R and the mSQL database system.

*Rstreams*
     Binary file stream support functions.

*Rwave*
     An environment for the time-frequency analysis of 1-D signals (and
     especially for the wavelet and Gabor transforms of noisy signals),
     based on the book "Practical Time-Frequency Analysis: Gabor and Wavelet
     Transforms with an Implementation in S" by Rene Carmona, Wen L. Hwang
     and Bruno Torresani, 1998, Academic Press.

*SASmixed*
     Data sets and sample linear mixed effects analyses corresponding to the
     examples in "SAS System for Mixed Models" by R. C. Littell, G. A.
     Milliken, W. W. Stroup and R. D. Wolfinger, 1996, SAS Institute.

*SuppDists*
     Ten distributions supplementing those built into R (Inverse Gauss,
     Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho,
     maximum F ratio, the Pearson product moment correlation coefficiant,
     Johnson distributions, normal scores and generalized hypergeometric
     distributions).

*XML*
     Facilities for reading XML documents and DTDs.

*acepack*
     ACE (Alternating Conditional Expectations) and AVAS (Additivity and
     VAriance Stabilization for regression) methods for selecting regression
     transformations.

*adapt*
     Adaptive quadrature in up to 20 dimensions.

*akima*
     Linear or cubic spline interpolation for irregularly gridded data.

*ash*
     David Scott's ASH routines for 1D and 2D density estimation.

*bindata*
     Generation of correlated artificial binary data.

*blighty*
     Function for drawing the coastline of the United Kingdom.

*boot*
     Functions and datasets for bootstrapping from the book "Bootstrap
     Methods and Their Applications" by A. C. Davison and D. V. Hinkley,
     1997, Cambridge University Press.

*bootstrap*
     Software (bootstrap, cross-validation, jackknife), data and errata for
     the book "An Introduction to the Bootstrap" by B. Efron and R.
     Tibshirani, 1993, Chapman and Hall.

*bqtl*
     QTL mapping toolkit for inbred crosses and recombinant inbred lines.
     Includes maximum likelihood and Bayesian tools.

*car*
     Companion to Applied Regression, containing functions for applied
     regession, linear models, and generalized linear models, with an
     emphasis on regression diagnostics, particularly graphical diagnostic
     methods.

*cclust*
     Convex clustering methods, including k-means algorithm, on-line update
     algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft
     Competitive Learning) and calculation of several indexes for finding
     the number of clusters in a data set.

*cfa*
     Analysis of configuration frequencies.

*chron*
     A package for working with chronological objects (times and dates).

*class*
     Functions for classification (k-nearest neighbor and LVQ).  Contained
     in the `VR' bundle.

*cluster*
     Functions for cluster analysis.

*cmprsk*
     Estimation, testing and regression modeling of subdistribution
     functions in competing risks.

*coda*
     Output analysis and diagnostics for Markov Chain Monte Carlo (MCMC)
     simulations.

*conf.design*
     A series of simple tools for constructing and manipulating confounded
     and fractional factorial designs.

*cramer*
     Routine for the multivariate nonparametric Cramer test.

*date*
     Functions for dealing with dates.  The most useful of them accepts a
     vector of input dates in any of the forms `8/30/53', `30Aug53', `30
     August 1953', ..., `August 30 53', or any mixture of these.

*diamonds*
     Functions for illustrating aperture-4 diamond partitions in the plane,
     or on the surface of an octahedron or icosahedron, for use as analysis
     or sampling grids.

*dse*
     Dynamic System Estimation, a multivariate time series package.
     Contains *dse1* (DSE kernel plus ARMA and state space models), *dse2*
     (DSE extensions), *syskern* (functions for writing code that is
     operating system and R/S independent), and *tframe* (functions for
     writing code that is independent of the representation of time).

*e1071*
     Miscellaneous functions used at the Department of Statistics at TU Wien
     (E1071), including moments, short-time Fourier transforms, Independent
     Component Analysis, Latent Class Analysis, reading and writing of
     images in portable anymap format, simulation of a Wiener process,
     support vector machines, and fuzzy clustering.

*ellipse*
     Package for drawing ellipses and ellipse-like confidence regions.

*event.chart*
     Package for creating event charts.

*exactRankTests*
     Computes exact p-values and quantiles using an implementation of the
     Streitberg/Roehmel shift algorithm.

*fastICA*
     Implementation of FastICA algorithm to perform Independent Component
     Analysis (ICA) and Projection Pursuit.

*fdim*
     Functions for calculating fractal dimension.

*fields*
     A collection of programs for curve and function fitting with an
     emphasis on spatial data.  The major methods implemented include cubic
     and thin plate splines, universal Kriging and Kriging for large data
     sets.  The main feature is that any covariance function implemented in
     R can be used for spatial prediction.

*foreign*
     Functions for reading and writing data stored by statistical software
     like Minitab, SAS, SPSS, Stata, etc.

*fracdiff*
     Maximum likelihood estimation of the parameters of a fractionally
     differenced ARIMA(p,d,q) model (Haslett and Raftery, Applied
     Statistics, 1989).

*gafit*
     Genetic algorithm for curve fitting.

*gee*
     An implementation of the Liang/Zeger generalized estimating equation
     approach to GLMs for dependent data.

*geoR*
     Functions to perform geostatistical data analysis including model-based
     methods.

*gld*
     Basic functions for the generalised (Tukey) lambda distribution.

*gregmisc*
     Miscellaneous functions written/maintained by Gregory R. Warnes.

*gss*
     A comprehensive package for structural multivariate function estimation
     using smoothing splines.

*ineq*
     Inequality, concentration and poverty measures, and Lorenz curves
     (empirical and theoretic).

*leaps*
     A package which performs an exhaustive search for the best subsets of a
     given set of potential regressors, using a branch-and-bound algorithm,
     and also performs searches using a number of less time-consuming
     techniques.

*lgtdl*
     A set of methods for longitudinal data objects.

*lmtest*
     A collection of tests on the assumptions of linear regression models
     from the book "The linear regression model under test" by W. Kraemer
     and H. Sonnberger, 1986, Physica.

*locfit*
     Local Regression, likelihood and density estimation.

*logspline*
     Logspline density estimation.

*lokern*
     Kernel regression smoothing with adaptive local or global plug-in
     bandwidth selection.

*lpridge*
     Local polynomial (ridge) regression.

*maptree*
     Functions with example data for graphing and mapping models from
     hierarchical clustering and classification and regression trees.

*maxstat*
     Maximally selected rank and Gauss statistics with several p-value
     approximations.

*mclust*
     Model-based cluster analysis.

*mda*
     Code for mixture discriminant analysis (MDA), flexible discriminant
     analysis (FDA), penalized discriminant analysis (PDA), multivariate
     additive regression splines (MARS), adaptive back-fitting splines
     (BRUTO), and penalized regression.

*meanscore*
     Mean Score method for missing covariate data in logistic regression
     models.

*mgcv*
     Routines for GAMs and other genralized ridge regression problems with
     multiple smoothing parameter selection by GCV or UBRE.

*mlbench*
     A collection of artificial and real-world machine learning benchmark
     problems, including the Boston housing data.

*muhaz*
     Hazard function estimation in survival analysis.

*multiv*
     Functions for hierarchical clustering, partitioning, bond energy
     algorithm, Sammon mapping, PCA and correspondence analysis.

*mvnmle*
     ML estimation for multivariate normal data with missing values.

*mvtnorm*
     Multivariate normal and t distributions.

*netCDF*
     Read data from netCDF files.

*nlme*
     Fit and compare Gaussian linear and nonlinear mixed-effects models.

*nlrq*
     Nonlinear quantile regression.

*nnet*
     Software for single hidden layer perceptrons ("feed-forward neural
     networks"), and for multinomial log-linear models.  Contained in the
     `VR' bundle.

*norm*
     Analysis of multivariate normal datasets with missing values.

*odesolve*
     An interface for the Ordinary Differential Equation (ODE) solver lsoda.
     ODEs are expressed as R functions.

*oz*
     Functions for plotting Australia's coastline and state boundaries.

*panel*
     Functions and datasets for fitting models to Panel data.

*pcurve*
     Fits a principal curve to a numeric multivariate dataset in arbitrary
     dimensions.  Produces diagnostic plots.  Also calculates Bray-Curtis
     and other distance matrices and performs multi-dimensional scaling and
     principal component analyses.

*permax*
     Functions intended to facilitate certain basic analyses of DNA array
     data, especially with regard to comparing expression levels between two
     types of tissue.

*pinktoe*
     Converts S trees to HTML/Perl files for interactive tree traversal.

*polymars*
     Polychotomous regression based on Multivariate Adaptive Regression
     Splines.

*polynom*
     A collection of functions to implement a class for univariate
     polynomial manipulations.

*princurve*
     Fits a principal curve to a matrix of points in arbitrary dimension.

*pspline*
     Smoothing splines with penalties on order m derivatives.

*quadprog*
     For solving quadratic programming problems.

*quantreg*
     Quantile  regression and related methods.

*rmeta*
     Functions for simple fixed and random effects meta-analysis for
     two-sample comparison of binary outcomes.

*rpart*
     Recursive PARTitioning and regression trees.

*scatterplot3d*
     Plots a three dimensional (3D) point cloud perspectively.

*sem*
     Functions for fitting general linear Structural Equation Models (with
     observed and unobserved variables) by the method of maximum likelihood
     using the RAM approach.

*sgeostat*
     An object-oriented framework for geostatistical modeling.

*sm*
     Software linked to the book "Applied Smoothing Techniques for Data
     Analysis:  The Kernel Approach with S-PLUS Illustrations" by A. W.
     Bowman and A. Azzalini (1997), Oxford University Press.

*sma*
     Functions for exploratory (statistical) microarray analysis.

*sn*
     Functions for manipulating skew-normal probability distributions and
     for fitting them to data, in the scalar and the multivariate case.

*sna*
     A range of tools for social network analysis, including node and
     graph-level indices, structural distance and covariance methods,
     structural equivalence detection, p* modeling, and network
     visualization.

*spatial*
     Functions for kriging and point pattern analysis from "Modern Applied
     Statistics with S-PLUS" by W. Venables and B. Ripley.  Contained in
     the `VR' bundle.

*splancs*
     Spatial and space-time point pattern analysis functions.

*strucchange*
     Various tests on structural change in linear regression models.

*survival*
     Functions for survival analysis, including penalised likelihood.

*tensor*
     Tensor product of arrays.

*tree*
     Classification and regression trees.

*tripack*
     A constrained two-dimensional Delaunay triangulation package.

*tseries*
     Package for time series analysis with emphasis on non-linear modelling.

*twostage*
     Functions for optimal design of two-stage-studies using the Mean Score
     method.

*wavethresh*
     Software to perform 1-d and 2-d wavelet statistics and transforms.

*wle*
     Robust statistical inference via a weighted likelihood approach.

*xgobi*
     Interface to the XGobi and XGvis programs for graphical data analysis.

*xtable*
     Export data to LaTeX and HTML tables.

*zmatrix*
     Matrices with numeric indices starting at zero rather than one.

See CRAN `src/contrib/PACKAGES' for more information.

   There is also a CRAN `src/contrib/Devel' directory which contains
packages still "under development" or depending on features only present in
the current development versions of R.  Volunteers are invited to give
these a try, of course.  This area of CRAN currently contains

*GRASS*
     Interface between the GRASS geographical information system and R,
     based on starting R from within the GRASS environment and chosen
     LOCATION and MAPSET.  Wrapper and helper functions are provided for a
     range of R functions to match the interface metadata structures.

*GenKern*
     Functions for generating and manipulating generalised binned kernel
     density estimates.

*R2HTML*
     Functions for exporting R objects & graphics in an HTML document.

*StatDataML*
     Read and write StatDataML.

*cxx*
     A small C++ test package.

*dopt*
     Finding D-optimal experimental designs.

*dseplus*
     Extensions to *dse*, the Dynamic Systems Estimation multivariate time
     series package.  Contains PADI, juice and monitoring extensions.

*ensemble*
     Ensembles of tree classifiers.

*grid*
     The Grid graphics package, a rewrite of the graphics layout
     capabilities, plus some support for interaction.

*hdf5*
     Interface to the NCSA HDF5 library.

*hpower*
     A suite of functions to compute power and sample size for tests of the
     general linear hypothesis.

*lattice*
     Lattice graphics, an implementation of Trellis Graphics functions.

*multidim*
     Code for correspondence analysis and other multidimensional descriptive
     statistics.

*multilm*
     A basic method for fitting and testing multivariate linear models,
     including stabilized test procedures by Laeuter et. al.

*npConfRatio*
     Nonparametric confidence intervals for the ratios of medians.

*pls*
     Univariate Partial Least Squares Regression.

*regexp*
     Simple regular expression interface.

*write.snns*
     Function for writing a SNNS pattern file from a data frame or matrix.

   Directory `src/contrib/Omegahat' contains yet unreleased packages from
the Omegahat Project for Statistical Computing (http://www.omegahat.org/).
Currently, there are

*CORBA*
     Dynamic CORBA client/server facilities for R.  Connects to other
     CORBA-aware applications developed in arbitrary languages, on different
     machines and allows R functionality to be exported in the same way to
     other applications.

*OOP*
     OOP style classes and methods for R and S-PLUS.  Object references and
     class-based method definition are supported in the style of languages
     such as Java and C++.

*REmbeddedPostgres*
     Allows R functions and objects to be used to implement SQL functions --
     per-record, aggregate and trigger functions.

*RGnumeric*
     A plugin for the Gnumeric spreadsheet that allows R functions to be
     called from cells within the sheet, automatic recalculation, etc.

*RJavaDevice*
     A graphics device for R that uses Java components and graphics APIs.

*RSMethods*
     An implementation of S version 4 methods and classes for R, consistent
     with the basic material in "Programming with data" by John M.
     Chambers, 1998, Springer NY.

*RSPerl*
     An interface from R to an embedded, persistent Perl interpreter,
     allowing one to call arbitrary Perl subroutines, classes and methods.

*RSPython*
     Allows Python programs to invoke S functions, methods, etc., and S code
     to call Python functionality.

*SASXML*
     Example for reading XML files in SAS 8.2 manner.

*SJava*
     An interface from R to Java to create and call Java objects and
     methods.

*SLanguage*
     Functions and C support utilities to support S language programming
     that can work in both R and S-PLUS.

*SNetscape*
     Plugin for Netscape and JavaScript.

*SXalan*
     Process XML documents using XSL functions implemented in R and
     dynamically substituting output from R.

*Slcc*
     Parses C source code, allowing one to analyze and automatically
     generate interfaces from S to that code, including the table of
     S-accessible native symbols, parameter count and type information, S
     constructors from C objects, call graphs, etc.

*Sxslt*
     An extension module for libxslt, the XML-XSL document translator, that
     allows XSL functions to be implemented via R functions.

   Jim Lindsey <jlindsey@luc.ac.be> has written a collection of R packages
for nonlinear regression and repeated measurements, consisting of *event*
(event history procedures and models), *gnlm* (generalized nonlinear
regression models), *growth* (multivariate normal and
elliptically-contoured repeated measurements models), *repeated*
(non-normal repeated measurements models), *rmutil* (utilities for
nonlinear regression and repeated measurements), and *stable* (probability
functions and generalized regression models for stable distributions).  All
analyses in the new edition of his book "Models for Repeated Measurements"
(1999, Oxford University Press) were carried out using these packages.  Jim
has also started *dna*, a package with procedures for the analysis of DNA
sequences.  Jim's packages can be obtained from
`http://www.luc.ac.be/~jlindsey/rcode.html'.

   More code has been posted to the r-help mailing list, and can be
obtained from the mailing list archive.

5.2 How can add-on packages be installed?
=========================================

   (Unix only.)  The add-on packages on CRAN come as gzipped tar files
named `PKG_VERSION.tar.gz', which may in fact be "bundles" containing more
than one package.  Provided that `tar' and `gzip' are available on your
system, type

     $ R CMD INSTALL /path/to/PKG_VERSION.tar.gz

at the shell prompt to install to the library tree rooted at the first
directory given in `R_LIBS' (see below) if this is set and non-null, and to
the default library (the `library' subdirectory of `R_HOME') otherwise.
(Versions of R prior to 1.3.0 installed to the default library by default.)

   To install to another tree (e.g., your private one), use

     $ R CMD INSTALL -l LIB /path/to/PKG_VERSION.tar.gz

where LIB gives the path to the library tree to install to.

   Even more conveniently, you can install and automatically update
packages from within R if you have access to CRAN.  See the help page for
`CRAN.packages()' for more information.

   You can use several library trees of add-on packages.  The easiest way
to tell R to use these is via the environment variable `R_LIBS' which
should be a colon-separated list of directories at which R library trees
are rooted.  You do not have to specify the default tree in `R_LIBS'.
E.g., to use a private tree in `$HOME/lib/R' and a public site-wide tree in
`/usr/local/lib/R-contrib', put

     R_LIBS="$HOME/lib/R:/usr/local/lib/R-contrib"; export R_LIBS

into your (Bourne) shell profile or even preferably, add the line

     R_LIBS="$HOME/lib/R:/usr/local/lib/R-contrib"

your `~/.Renviron' file.  (Note that no `export' statement is needed or
allowed in this file; see the on-line help for `Startup' for more
information.)

5.3 How can add-on packages be used?
====================================

   To find out which additional packages are available on your system, type

     library()

at the R prompt.

   This produces something like

     Packages in `/home/me/lib/R':
     
     mystuff       My own R functions, nicely packaged but not documented
     
     Packages in `/usr/local/lib/R/library':
     
     KernSmooth    Functions for kernel smoothing for Wand & Jones (1995)
     MASS          Main Library of Venables and Ripley's MASS
     base          The R base package
     boot          Bootstrap R (S-Plus) Functions (Canty)
     class         Functions for classification
     cluster       Functions for clustering (by Rousseeuw et al.)
     ctest         Classical Tests
     eda           Exploratory Data Analysis
     foreign       Read data stored by Minitab, S, SAS, SPSS, Stata, ...
     lqs           Resistant Regression and Covariance Estimation
     mgcv          Multiple smoothing parameter estimation and GAMs by GCV
     modreg        Modern Regression: Smoothing and Local Methods
     mva           Classical Multivariate Analysis
     nlme          Linear and nonlinear mixed effects models
     nls           Nonlinear regression
     nnet          Feed-forward neural networks and multinomial log-linear
                   models
     rpart         Recursive partitioning
     spatial       functions for kriging and point pattern analysis
     splines       Regression Spline Functions and Classes
     stepfun       Step Functions, including Empirical Distributions
     survival      Survival analysis, including penalised likelihood
     tcltk         Interface to Tcl/Tk
     ts            Time series functions

   You can "load" the installed package PKG by

     library(PKG)

   You can then find out which functions it provides by typing one of

     library(help = PKG)
     help(package = PKG)

   You can unload the loaded package PKG by

     detach("package:PKG")

5.4 How can add-on packages be removed?
=======================================

   Use

     $ R CMD REMOVE PKG_1 ... PKG_N

to remove the packages PKG_1, ..., PKG_N from the library tree rooted at
the first directory given in `R_LIBS' if this is set and non-null, and from
the default library otherwise.  (Versions of R prior to 1.3.0 removed from
the default library by default.)

   To remove from library LIB, do

     $ R CMD REMOVE -l LIB PKG_1 ... PKG_N

5.5 How can I create an R package?
==================================

   A package consists of a subdirectory containing the files `DESCRIPTION'
and `INDEX', and the subdirectories `R', `data', `demo', `exec', `inst',
`man', `src', and `tests' (some of which can be missing).  Optionally the
package can also contain script files `configure' and `cleanup' which are
executed before and after installation.

   See section "Creating R packages" in `Writing R Extensions', for details.
This manual is included in the R distribution, *note What documentation
exists for R?::, and gives information on package structure, the configure
and cleanup mechanisms, and on automated package checking and building.

   R version 1.3.0 has added the function `package.skeleton()' which will
set up directories, save data and code, and create skeleton help files for
a set of R functions and datasets.

   *Note What is CRAN?::, for information on uploading a package to CRAN.

5.6 How can I contribute to R?
==============================

   R is in active development and there is always a risk of bugs creeping
in.  Also, the developers do not have access to all possible machines
capable of running R.  So, simply using it and communicating problems is
certainly of great value.

   One place where functionality is still missing is the modeling software
as described in "Statistical Models in S" (see *Note What is S?::);
Generalized Additive Models (*note Are GAMs implemented in R?::) and some
of the nonlinear modeling code are not there yet.

   The R Developer Page (http://developer.r-project.org/) acts as an
intermediate repository for more or less finalized ideas and plans for the
R statistical system.  It contains (pointers to) TODO lists, RFCs, various
other writeups, ideas lists, and CVS miscellanea.

   Many (more) of the packages available at the Statlib S Repository might
be worth porting to R.

   If you are interested in working on any of these projects, please notify
Kurt Hornik <Kurt.Hornik@r-project.org>.

6 R and Emacs
*************

6.1 Is there Emacs support for R?
=================================

   There is an Emacs package called ESS ("Emacs Speaks Statistics") which
provides a standard interface between statistical programs and statistical
processes.  It is intended to provide assistance for interactive
statistical programming and data analysis.  Languages supported include: S
dialects (S 3/4, S-PLUS 3.x/4.x/5.x, and R), LispStat dialects (XLispStat,
ViSta), SAS, Stata, SPSS dialects (SPSS, Fiasco) and SCA.

   ESS grew out of the need for bug fixes and extensions to S-mode 4.8
(which was a GNU Emacs interface to S/S-PLUS version 3 only).  The current
set of developers desired support for XEmacs, R, S4, and MS Windows.  In
addition, with new modes being developed for R, Stata, and SAS, it was felt
that a unifying interface and framework for the user interface would
benefit both the user and the developer, by helping both groups conform to
standard Emacs usage.  The end result is an increase in efficiency for
statistical programming and data analysis, over the usual tools.

   R support contains code for editing R source code (syntactic indentation
and highlighting of source code, partial evaluations of code, loading and
error-checking of code, and source code revision maintenance) and
documentation (syntactic indentation and highlighting of source code,
sending examples to running ESS process, and previewing), interacting with
an inferior R process from within Emacs (command-line editing, searchable
command history, command-line completion of R object and file names, quick
access to object and search lists, transcript recording, and an interface
to the help system), and transcript manipulation (recording and saving
transcript files, manipulating and editing saved transcripts, and
re-evaluating commands from transcript files).

   The latest versions of ESS are available from
`http://ess.stat.wisc.edu/pub/ESS/' or `ftp://ess.stat.wisc.edu/pub/ESS/',
or via CRAN.  The HTML version of the documentation can be found at
`http://stat.ethz.ch/ESS/'.

   ESS comes with detailed installation instructions.

   For help with ESS, send email to <ESS-help@stat.ethz.ch>.

   Please send bug reports and suggestions on ESS to
<ESS-bugs@stat.math.ethz.ch>.  The easiest way to do this from is within
Emacs by typing `M-x ess-submit-bug-report' or using the [ESS] or [iESS]
pulldown menus.

6.2 Should I run R from within Emacs?
=====================================

   Yes, _definitely_.  Inferior R mode provides a readline/history
mechanism, object name completion, and syntax-based highlighting of the
interaction buffer using Font Lock mode, as well as a very convenient
interface to the R help system.

   Of course, it also integrates nicely with the mechanisms for editing R
source using Emacs.  One can write code in one Emacs buffer and send whole
or parts of it for execution to R; this is helpful for both data analysis
and programming.  One can also seamlessly integrate with a revision control
system, in order to maintain a log of changes in your programs and data, as
well as to allow for the retrieval of past versions of the code.

   In addition, it allows you to keep a record of your session, which can
also be used for error recovery through the use of the transcript mode.

   To specify command line arguments for the inferior R process, use `C-u
M-x R' for starting R.

6.3 Debugging R from within Emacs
=================================

   To debug R "from within Emacs", there are several possibilities.  To use
the Emacs GUD (Grand Unified Debugger) library with the recommended
debugger GDB, type `M-x gdb' and give the path to the R _binary_ as
argument.  At the gdb prompt, set `R_HOME' and other environment variables
as needed (using e.g. `set env R_HOME /path/to/R/', but see also below),
and start the binary with the desired arguments (e.g., `run --vsize=12M').

   If you have ESS, you can do `C-u M-x R <RET> - d <SPC> g d b <RET>' to
start an inferior R process with arguments `-d gdb'.

   A third option is to start an inferior R process via ESS (`M-x R') and
then start GUD (`M-x gdb') giving the R binary (using its full path name)
as the program to debug.  Use the program `ps' to find the process number
of the currently running R process then use the `attach' command in gdb to
attach it to that process.  One advantage of this method is that you have
separate `*R*' and `*gud-gdb*' windows.  Within the `*R*' window you have
all the ESS facilities, such as object-name completion, that we know and
love.

   When using GUD mode for debugging from within Emacs, you may find it
most convenient to use the directory with your code in it as the current
working directory and then make a symbolic link from that directory to the
R binary.  That way `.gdbinit' can stay in the directory with the code and
be used to set up the environment and the search paths for the source, e.g.
as follows:

     set env R_HOME /opt/R
     set env R_PAPERSIZE letter
     set env R_PRINTCMD lpr
     dir /opt/R/src/appl
     dir /opt/R/src/main
     dir /opt/R/src/nmath
     dir /opt/R/src/unix

7 R Miscellanea
***************

7.1 Why does R run out of memory?
=================================

   Versions of R prior to 1.2.0 used a _static_ memory model.  At startup,
R asked the operating system to reserve a fixed amount of memory for it.
The size of this chunk could not be changed subsequently.  Hence, it could
happen that not enough memory was allocated, e.g., when trying to read
large data sets into R.  In such cases, it was necessary to restart R with
more memory available, as controlled by the command line options `--nsize'
and `--vsize'.

   R version 1.2.0 introduces a new "generational" garbage collector, which
will increase the memory available to R as needed.  Hence, user
intervention is no longer necessary for ensuring that enough memory is
available.

7.2 Why does sourcing a correct file fail?
==========================================

   Versions of R prior to 1.2.1 may have had problems parsing files not
ending in a newline.  Earlier R versions had a similar problem when reading
in data files.  This should no longer happen.

7.3 How can I set components of a list to NULL?
===============================================

   You can use

     x[i] <- list(NULL)

to set component `i' of the list `x' to `NULL', similarly for named
components.  Do not set `x[i]' or `x[[i]]' to `NULL', because this will
remove the corresponding component from the list.

   For dropping the row names of a matrix `x', it may be easier to use
`rownames(x) <- NULL', similarly for column names.

7.4 How can I save my workspace?
================================

   `save.image()' saves the objects in the user's `.GlobalEnv' to the file
`.RData' in the R startup directory.  (This is also what happens after
`q("yes")'.)  Using `save.image(FILE)' one can save the image under a
different name.

7.5 How can I clean up my workspace?
====================================

   To remove all objects in the currently active environment (typically
`.GlobalEnv'), you can do

     rm(list = ls(all = TRUE))

(Without `all = TRUE', only the objects with names not starting with a `.'
are removed.)

7.6 How can I get eval() and D() to work?
=========================================

   Strange things will happen if you use `eval(print(x), envir = e)' or
`D(x^2, "x")'.  The first one will either tell you that "`x'" is not found,
or print the value of the wrong `x'.  The other one will likely return zero
if `x' exists, and an error otherwise.

   This is because in both cases, the first argument is evaluated in the
calling environment first.  The result (which should be an object of mode
`"expression"' or `"call"') is then evaluated or differentiated.  What you
(most likely) really want is obtained by "quoting" the first argument upon
surrounding it with `expression()'.  For example,

     R> D(expression(x^2), "x")
     2 * x

   Although this behavior may initially seem to be rather strange, is
perfectly logical.  The "intuitive" behavior could easily be implemented,
but problems would arise whenever the expression is contained in a
variable, passed as a parameter, or is the result of a function call.
Consider for instance the semantics in cases like

     D2 <- function(e, n) D(D(e, n), n)

or

     g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2)))
     g(a * b)

   See the help page for `deriv()' for more examples.

7.7 Why do my matrices lose dimensions?
=======================================

   When a matrix with a single row or column is created by a subscripting
operation, e.g., `row <- mat[2, ]', it is by default turned into a vector.
In a similar way if an array with dimension, say, 2 x 3 x 1 x 4 is created
by subscripting it will be coerced into a 2 x 3 x 4 array, losing the
unnecessary dimension.  After much discussion this has been determined to
be a _feature_.

   To prevent this happening, add the option `drop = FALSE' to the
subscripting.  For example,

     rowmatrix <- mat[2, , drop = FALSE]  # creates a row matrix
     colmatrix <- mat[, 2, drop = FALSE]  # creates a column matrix
     a <- b[1, 1, 1, drop = FALSE]        # creates a 1 x 1 x 1 array

   The `drop = FALSE' option should be used defensively when programming.
For example, the statement

     somerows <- mat[index, ]

will return a vector rather than a matrix if `index' happens to have length
1, causing errors later in the code.  It should probably be rewritten as

     somerows <- mat[index, , drop = FALSE]

7.8 How does autoloading work?
==============================

   R has a special environment called `.AutoloadEnv'.  Using
`autoload(NAME, PKG)', where NAME and PKG are strings giving the names of
an object and the package containing it, stores some information in this
environment.  When R tries to evaluate NAME, it loads the corresponding
package PKG and reevaluates NAME in the new package's environment.

   Using this mechanism makes R behave as if the package was loaded, but
does not occupy memory (yet).

   See the help page for `autoload()' for a very nice example.

7.9 How should I set options?
=============================

   The function `options()' allows setting and examining a variety of
global "options" which affect the way in which R computes and displays its
results.  The variable `.Options' holds the current values of these
options, but should never directly be assigned to unless you want to drive
yourself crazy--simply pretend that it is a "read-only" variable.

   For example, given

     test1 <- function(x = pi, dig = 3) {
       oo <- options(digits = dig); on.exit(options(oo));
       cat(.Options$digits, x, "\n")
     }
     test2 <- function(x = pi, dig = 3) {
       .Options$digits <- dig
       cat(.Options$digits, x, "\n")
     }

we obtain:

     R> test1()
     3 3.14
     R> test2()
     3 3.141593

   What is really used is the _global_ value of `.Options', and using
`options(OPT = VAL)' correctly updates it.  Local copies of `.Options',
either in `.GlobalEnv' or in a function environment (frame), are just
silently disregarded.

7.10 How do file names work in Windows?
=======================================

   As R uses C-style string handling, `\' is treated as an escape
character, so that for example one can enter a newline as `\n'.  When you
really need a `\', you have to escape it with another `\'.

   Thus, in filenames use something like `"c:\\data\\money.dat"'.  You can
also replace `\' by `/' (`"c:/data/money.dat"').

7.11 Why does plotting give a color allocation error?
=====================================================

   Sometimes plotting, e.g., when running `demo(image)', results in "Error:
color allocation error".  This is an X problem, and only indirectly related
to R.  It occurs when applications started prior to R have used all the
available colors.  (How many colors are available depends on the X
configuration; sometimes only 256 colors can be used.)

   One application which is notorious for "eating" colors is Netscape.  If
the problem occurs when Netscape is running, try (re)starting it with
either the `-no-install' (to use the default colormap) or the `-install'
(to install a private colormap) option.

   You could also set the `colortype' of `X11()' to `"pseudo.cube"' rather
than the default `"pseudo"'.  See the help page for `X11()' for more
information.

7.12 How do I convert factors to numeric?
=========================================

   It may happen that when reading numeric data into R (usually, when
reading in a file), they come in as factors.  If `f' is such a factor
object, you can use

     as.numeric(as.character(f))

to get the numbers back.  More efficient, but harder to remember, is

     as.numeric(levels(f))[as.integer(f)]

   In any case, do not call `as.numeric()' or their likes directly.

7.13 Are Trellis displays implemented in R?
===========================================

   An R package called *lattice*, which provides graphical functionality
that is compatible with most Trellis commands, is under development.  The
latest version of the package may be downloaded from the `contrib/Devel'
area of CRAN.  *lattice* currently relies on another package called *grid*
(also under development) so you will need to download that as well.
Because these packages are under active development, they might only work
with the latest development version of R.

   You could also look at `coplot()' and `dotchart()' which might do at
least some of what you want.  Note also that the R version of `pairs()' is
fairly general and provides most of the functionality of `splom()', and
that R's default plot method has an argument `asp' allowing to specify (and
fix against device resizing) the aspect ratio of the plot.

   (Because the word "Trellis" has been claimed as a trademark we do not
use it in R.  The name "lattice" has been proposed for the R equivalent.)

7.14 What are the enclosing and parent environments?
====================================================

   Inside a function you may want to access variables in two additional
environments: the one that the function was defined in ("enclosing"), and
the one it was invoked in ("parent").

   If you create a function at the command line or load it in a package its
enclosing environment is the global workspace.  If you define a function
`f()' inside another function `g()' its enclosing environment is the
environment inside `g()'.  The enclosing environment for a function is
fixed when the function is created.  You can find out the enclosing
environment for a function `f()' using `environment(f)'.

   The "parent" environment, on the other hand, is defined when you invoke
a function.  If you invoke `lm()' at the command line its parent
environment is the global workspace, if you invoke it inside a function
`f()' then its parent environment is the environment inside `f()'.  You can
find out the parent environment for an invocation of a function by using
`parent.frame()' or `sys.frame(sys.parent())'.

   So for most user-visible functions the enclosing environment will be the
global workspace, since that is where most functions are defined.  The
parent environment will be wherever the function happens to be called from.
If a function `f()' is defined inside another function `g()' it will
probably be used inside `g()' as well, so its parent environment and
enclosing environment will probably be the same.

   Parent environments are important because things like model formulas
need to be evaluated in the environment the function was called from, since
that's where all the variables will be available.  This relies on the
parent environment being potentially different with each invocation.

   Enclosing environments are important because a function can use
variables in the enclosing environment to share information with other
functions or with other invocations of itself (see the section on lexical
scoping).  This relies on the enclosing environment being the same each
time the function is invoked.

   Scoping _is_ hard.  Looking at examples helps.  It is particularly
instructive to look at examples that work differently in R and S and try to
see why they differ.  One way to describe the scoping differences between R
and S is to say that in S the enclosing environment is _always_ the global
workspace, but in R the enclosing environment is wherever the function was
created.

7.15 How can I substitute into a plot label?
============================================

   Often, it is desired to use the value of an R object in a plot label,
e.g., a title.  This is easily accomplished using `paste()' if the label is
a simple character string, but not always obvious in case the label is an
expression (for refined mathematical annotation).  In such a case, either
use `parse()' on your pasted character string or use `substitute()' on an
expression.  For example, if `ahat' is an estimator of your parameter a of
interest, use

     title(substitute(hat(a) == ahat, list(ahat = ahat)))

(note that it is `==' and not `=').  There are more worked examples in the
mailing list achives.

7.16 What are valid names?
==========================

   When creating data frames using `data.frame()' or `read.table()', R by
default ensures that the variable names are syntactically valid.  (The
argument `check.names' to these functions controls whether variable names
are checked and adjusted by `make.names()' if needed.)

   To understand what names are "valid", one needs to take into account
that the term "name" is used in several different (but related) ways in the
language:

  1. A _syntactic name_ is a string the parser interprets as this type of
     expression.  It consists of letters, numbers, and the dot character
     and starts with a letter or the dot.

  2. An _object name_ is a string associated with an object that is
     assigned in an expression either by having the object name on the left
     of an assignment operation or as an argument to the `assign()'
     function.  It is usually a syntactic name as well, but can be any
     non-empty string if it is quoted (and it is always quoted in the call
     to `assign()').

  3. An _argument name_ is what appears to the left of the equals sign when
     supplying an argument in a function call (for example, `f(trim=.5)').
     Argument names are also usually syntactic names, but again can be
     anything if they are quoted.

  4. An _element name_ is a string that identifies a piece of an object (a
     component of a list, for example.)  When it is used on the right of
     the `$' operator, it must be a syntactic name, or quoted.  Otherwise,
     element names can be any strings.  (When an object is used as a
     database, as in a call to `eval()' or `attach()', the element names
     become object names.)

  5. Finally, a _file name_ is a string identifying a file in the operating
     system for reading, writing, etc.  It really has nothing much to do
     with names in the language, but it is traditional to call these
     strings file "names".

7.17 Are GAMs implemented in R?
===============================

   There is a `gam()' function for Generalized Additive Models in package
*mgcv*, but it is not an exact clone of what is described in the White Book
(no `lo()' for example).  Package *gss* can fit spline-based GAMs too.  And
if you can accept regression splines you can use `glm()'.  For gaussian
GAMs you can use `bruto()' from package *mda*.

7.18 Why is the output not printed when I source() a file?
==========================================================

   Most R commands do not generate any output. The command

     1+1

computes the value 2 and returns it; the command

     summary(glm(y~x+z, family=binomial))

fits a logistic regression model, computes some summary information and
returns an object of class `"summary.glm"' (*note How should I write
summary methods?::).

   If you type `1+1' or `summary(glm(y~x+z, family=binomial))' at the
command line the returned value is automatically printed (unless it is
`invisible()'), but in other circumstances, such as in a `source()'d file
or inside a function it isn't printed unless you specifically print it.

   To print the value use

     print(1+1)

or

     print(summary(glm(y~x+z, family=binomial)))

instead, or use `source(FILE, echo=TRUE)'.

7.19 Why does outer() behave strangely with my function?
========================================================

   As the help for `outer()' indicates, it does not work on arbitrary
functions the way the `apply()' family does.  It requires functions that
are vectorized to work elementwise on arrays.  As you can see by looking at
the code, `outer(x, y, FUN)' creates two large vectors containing every
possible combination of elements of `x' and `y' and then passes this to
`FUN' all at once.  Your function probably cannot handle two large vectors
as parameters.

   If you have a function that cannot handle two vectors but can handle two
scalars, then you can still use `outer()' but you will need to wrap your
function up first, to simulate vectorized behavior.  Suppose your function
is

     foo <- function(x, y, happy) {
       stopifnot(length(x) == 1, length(y) == 1) # scalars only!
       (x + y) * happy
     }

If you define the general function

     wrapper <- function(x, y, my.fun, ...) {
       sapply(seq(along=x), FUN = function(i) my.fun(x[i], y[i], ...))
     }

then you can use `outer()' by writing, e.g.,

     outer(1:4, 1:2, FUN = wrapper, my.fun = foo, happy = 10)

8 R Programming
***************

8.1 How should I write summary methods?
=======================================

   Suppose you want to provide a summary method for class `foo'.  Then
`summary.foo()' should not print anything, but return an object of class
`"summary.foo"', _and_ you should write a method `print.summary.foo()'
which nicely prints the summary information and invisibly returns its
object.  This approach is preferred over having `summary.foo()' print
summary information and return something useful, as sometimes you need to
grab something computed by `summary()' inside a function or similar.  In
such cases you don't want anything printed.

8.2 How can I debug dynamically loaded code?
============================================

   Roughly speaking, you need to start R inside the debugger, load the
code, send an interrupt, and then set the required breakpoints.

   See section "Finding entry points in dynamically loaded code" in
`Writing R Extensions'.  This manual is included in the R distribution,
*note What documentation exists for R?::.

8.3 How can I inspect R objects when debugging?
===============================================

   The most convenient way is to call `R_PV' from the symbolic debugger.

   See section "Inspecting R objects when debugging" in `Writing R
Extensions'.

8.4 How can I change compilation flags?
=======================================

   Suppose you have C code file for dynloading into R, but you want to use
`R CMD SHLIB' with compilation flags other than the default ones (which
were determined when R was built).  You could change the file
``R_HOME'/etc/Makeconf' to reflect your preferences.  If you are a Bourne
shell user, you can also pass the desired flags to Make (which is used for
controlling compilation) via the Make variable `MAKEFLAGS', as in

     MAKEFLAGS="CFLAGS=-O3" R CMD SHLIB *.c

9 R Bugs
********

9.1 What is a bug?
==================

   If R executes an illegal instruction, or dies with an operating system
error message that indicates a problem in the program (as opposed to
something like "disk full"), then it is certainly a bug.  If you call
`.C()', `.Fortran()', `.External()' or `.Call()' (or `.Internal()')
yourself (or in a function you wrote), you can always crash R by using
wrong argument types (modes).  This is not a bug.

   Taking forever to complete a command can be a bug, but you must make
certain that it was really R's fault.  Some commands simply take a long
time.  If the input was such that you _know_ it should have been processed
quickly, report a bug.  If you don't know whether the command should take a
long time, find out by looking in the manual or by asking for assistance.

   If a command you are familiar with causes an R error message in a case
where its usual definition ought to be reasonable, it is probably a bug.
If a command does the wrong thing, that is a bug.  But be sure you know for
certain what it ought to have done.  If you aren't familiar with the
command, or don't know for certain how the command is supposed to work,
then it might actually be working right.  Rather than jumping to
conclusions, show the problem to someone who knows for certain.

   Finally, a command's intended definition may not be best for statistical
analysis.  This is a very important sort of problem, but it is also a
matter of judgment.  Also, it is easy to come to such a conclusion out of
ignorance of some of the existing features.  It is probably best not to
complain about such a problem until you have checked the documentation in
the usual ways, feel confident that you understand it, and know for certain
that what you want is not available.  If you are not sure what the command
is supposed to do after a careful reading of the manual this indicates a
bug in the manual.  The manual's job is to make everything clear.  It is
just as important to report documentation bugs as program bugs.  However,
we know that the introductory documentation is seriously inadequate, so you
don't need to report this.

   If the online argument list of a function disagrees with the manual, one
of them must be wrong, so report the bug.

9.2 How to report a bug
=======================

   When you decide that there is a bug, it is important to report it and to
report it in a way which is useful.  What is most useful is an exact
description of what commands you type, starting with the shell command to
run R, until the problem happens.  Always include the version of R,
machine, and operating system that you are using; type `version' in R to
print this.

   The most important principle in reporting a bug is to report _facts_,
not hypotheses or categorizations.  It is always easier to report the
facts, but people seem to prefer to strain to posit explanations and report
them instead.  If the explanations are based on guesses about how R is
implemented, they will be useless; others will have to try to figure out
what the facts must have been to lead to such speculations.  Sometimes this
is impossible.  But in any case, it is unnecessary work for the ones trying
to fix the problem.

   For example, suppose that on a data set which you know to be quite large
the command

     R> data.frame(x, y, z, monday, tuesday)

never returns.  Do not report that `data.frame()' fails for large data
sets.  Perhaps it fails when a variable name is a day of the week.  If this
is so then when others got your report they would try out the
`data.frame()' command on a large data set, probably with no day of the
week variable name, and not see any problem.  There is no way in the world
that others could guess that they should try a day of the week variable
name.

   Or perhaps the command fails because the last command you used was a
method for `"["()' that had a bug causing R's internal data structures to
be corrupted and making the `data.frame()' command fail from then on.  This
is why others need to know what other commands you have typed (or read from
your startup file).

   It is very useful to try and find simple examples that produce
apparently the same bug, and somewhat useful to find simple examples that
might be expected to produce the bug but actually do not.  If you want to
debug the problem and find exactly what caused it, that is wonderful.  You
should still report the facts as well as any explanations or solutions.
Please include an example that reproduces the problem, preferably the
simplest one you have found.

   Invoking R with the `--vanilla' option may help in isolating a bug.
This ensures that the site profile and saved data files are not read.

   On Unix systems a bug report can be generated using the function
`bug.report()'.  This automatically includes the version information and
sends the bug to the correct address.  Alternatively the bug report can be
emailed to <r-bugs@r-project.org> or submitted to the Web page at
`http://bugs.r-project.org/'.

   Bug reports on contributed packages should perhaps be sent to the
package maintainer rather than to r-bugs.

10 Acknowledgments
******************

   Of course, many many thanks to Robert and Ross for the R system, and to
the package writers and porters for adding to it.

   Special thanks go to Doug Bates, Peter Dalgaard, Paul Gilbert, Stefano
Iacus, Fritz Leisch, Jim Lindsey, Thomas Lumley, Martin Maechler, Brian D.
Ripley, Anthony Rossini, and Andreas Weingessel for their comments which
helped me improve this FAQ.

   More to some soon ...

