Node:Top, Next:Introduction, Previous:(dir), Up:(dir)

- Introduction:
- R Basics:
- R and S:
- R Web Interfaces:
- R Add-On Packages:
- R and Emacs:
- R Miscellanea:
- R Programming:
- R Bugs:
- Acknowledgments:

Node:Introduction, Next:R Basics, Previous:Top, Up:Top

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

Node:Legalese, Next:Obtaining this document, Previous:Introduction, Up:Introduction

This document is copyright © 1998-2002 by Kurt Hornik.

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.

Node:Obtaining this document, Next:Citing this document, Previous:Legalese, Up:Introduction

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.

You can also obtain the R FAQ from the `doc/FAQ`

subdirectory of a CRAN site (see What is CRAN?).

Node:Citing this document, Next:Notation, Previous:Obtaining this document, Up:Introduction

In publications, please refer to this FAQ as Hornik
(2002), "The R FAQ", and give the above,
*official* URL and the ISBN 3-901167-51-X.

Node:Notation, Next:Feedback, Previous:Citing this document, Up:Introduction

Everything should be pretty standard. `R>`

is used for the R
prompt, and a `$`

for the shell prompt (where applicable).

Node:Feedback, Previous:Notation, Up:Introduction

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" and the "R for Macintosh FAQ/DOC". If you have information on Mac or Windows systems that you think should be added to this document, please let me know.

Node:R Basics, Next:R and S, Previous:Introduction, Up:Top

- What is R?:
- What machines does R run on?:
- What is the current version of R?:
- How can R be obtained?:
- How can R be installed?:
- Are there Unix binaries for R?:
- What documentation exists for R?:
- Citing R:
- What mailing lists exist for R?:
- What is CRAN?:
- Can I use R for commercial purposes?:

Node:What is R?, Next:What machines does R run on?, Previous:R Basics, Up:R Basics

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 (see What is S?) and Sussman's Scheme. Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme. See 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 (see R Add-On Packages).

R was initially written by Ross Ihaka and Robert Gentleman 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").

Node:What machines does R run on?, Next:What is the current version of R?, Previous:What is R?, Up:R Basics

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, `cpu`-linux-gnu for
the i386, alpha, arm, hppa, ia64, m68k, powerpc, and sparc CPUs (see
e.g. http://buildd.debian.org/build.php?&pkg=r-base),
i386-sun-solaris, powerpc-apple-darwin, mips-sgi-irix, alpha-dec-osf4,
rs6000-ibm-aix, hppa-hp-hpux, and sparc-sun-solaris.

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

Node:What is the current version of R?, Next:How can R be obtained?, Previous:What machines does R run on?, Up:R Basics

The current released version is 1.5.0. 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.

Node:How can R be obtained?, Next:How can R be installed?, Previous:What is the current version of R?, Up:R Basics

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

Sources are also available via anonymous rsync. Use

rsync -rC rsync.r-project.org::moduleR

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/.

Node:How can R be installed?, Next:Are there Unix binaries for R?, Previous:How can R be obtained?, Up:R Basics

- How can R be installed (Unix):
- How can R be installed (Windows):
- How can R be installed (Macintosh):

Node:How can R be installed (Unix), Next:How can R be installed (Windows), Previous:How can R be installed?, Up:How can R be installed?

If binaries are available for your platform (see 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 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 (see 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.

Node:How can R be installed (Windows), Next:How can R be installed (Macintosh), Previous:How can R be installed (Unix), Up:How can R be installed?

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, ME, NT4, 2000, and XP (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 Duncan Murdoch and
Brian D. Ripley.

For most installations the installer `SetupR.exe`

will be the
easiest tool to use.

See the "R for Windows FAQ" for more details.

Node:How can R be installed (Macintosh), Previous:How can R be installed (Windows), Up:How can R be installed?

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.

The "R for Macintosh FAQ/DOC" has more details.

Binaries of base distributions for MacOS X (Darwin) with X11 are made
available by Jan de Leeuw in the
`bin/macosx`

directory of a CRAN site.

Node:Are there Unix binaries for R?, Next:What documentation exists for R?, Previous:How can R be installed?, Up:R Basics

The `bin/linux`

directory of a CRAN site contains Debian
potato/woody packages for the i386 platform (now part of the Debian
distribution and maintained by Dirk Eddelbuettel), Red Hat 6.x i386, 7.x
i386 and 7.x alpha packages (maintained by Stephen Eglen, Martyn Plummer
and Naoki Takebayashi, respectively), SuSE 7.2 i386 packages by Albrecht
Gebhardt, Mandrake 8.1 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/debiandistributionmain

(where `distribution` is either `stable`

or `testing`

;
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.

Node:What documentation exists for R?, Next:Citing R, Previous:Are there Unix binaries for R?, Up:R Basics

Online documentation for most of the functions and variables in R
exists, and can be printed on-screen by typing `help( name)`
(or

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 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 R and S) can be used in combination with this FAQ (see 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.
See 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

Node:Citing R, Next:What mailing lists exist for R?, Previous:What documentation exists for R?, Up:R Basics

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} }

Node:What mailing lists exist for R?, Next:What is CRAN?, Previous:Citing R, Up:R Basics

Thanks to Martin Maechler, 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.

Subscriptions to `r-help' and `r-devel' are also available in digest
format, see the `doc/html/mail.html`

file in CRAN for
more information.

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 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://maths.newcastle.edu.au/~rking/R/.

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

Node:What is CRAN?, Next:Can I use R for commercial purposes?, Previous:What mailing lists exist for R?, Up:R Basics

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.br.r-project.org/ (Universidade Federal de Paraná, Brazil) http://cran.ch.r-project.org/ (ETH Zürich, Switzerland) http://cran.de.r-project.org/ (APP, Germany) 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 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.

Node:Can I use R for commercial purposes?, Previous:What is CRAN?, Up:R Basics

R is released under the GNU General Public License (GPL). If you have any questions regarding the legality of using R in any particular situation you should bring it up with your legal counsel. We are in no position to offer legal advice.

It is the opinion of the R Core Team that one can use R for commercial purposes (e.g., in business or in consulting). The GPL, like all Open Source licenses, permits all and any use of the package. It only restricts distribution of R or of other programs containing code from R. This is made clear in clause 6 ("No Discrimination Against Fields of Endeavor") of the Open Source Definition:

The license must not restrict anyone from making use of the program in a specific field of endeavor. For example, it may not restrict the program from being used in a business, or from being used for genetic research.

It is also explicitly stated in clause 0 of the GPL, which says in part

Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program.

Most add-on packages, including all recommended ones, also explicitly allow commercial use in this way. A few packages are restricted to "non-commercial use"; you should contact the author to clarify whether these may be used or seek the advice of your legal counsel.

None of the discussion in this section constitutes legal advice. The R Core Team does not provide legal advice under any circumstances.

Node:R and S, Next:R Web Interfaces, Previous:R Basics, Up:Top

- What is S?:
- What is S-PLUS?:
- What are the differences between R and S?:
- Is there anything R can do that S-PLUS cannot?:
- What is R-plus?:

Node:What is S?, Next:What is S-PLUS?, Previous:R and S, Up:R and 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 at CMU.

Node:What is S-PLUS?, Next:What are the differences between R and S?, Previous:What is S?, Up:R and S

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 for further information.

Node:What are the differences between R and S?, Next:Is there anything R can do that S-PLUS cannot?, Previous:What is S-PLUS?, Up: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.

Node:Lexical scoping, Next:Models, Previous:What are the differences between R and S?, Up:What are the differences between R and S?

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" (see 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
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 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.

Node:Models, Next:Others, Previous:Lexical scoping, Up:What are the differences between R and S?

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`

.

Node:Others, Previous:Models, Up:What are the differences between R and S?

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

. Then, it searches for a site-wide startup profile unless the command line option`$R_HOME`

/library/base/R/Rprofile`--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.site

in versions prior to 1.4.0). This code is loaded in package`$R_HOME`

/etc/Rprofile**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, givenx <- 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(`

remains`arg`)`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.

Node:Is there anything R can do that S-PLUS cannot?, Next:What is R-plus?, Previous:What are the differences between R and S?, Up:R and S

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.

Node:What is R-plus?, Previous:Is there anything R can do that S-PLUS cannot?, Up:R and S

There is no such thing.

Node:R Web Interfaces, Next:R Add-On Packages, Previous:R and S, Up:Top

**Rcgi** is a CGI WWW interface to R by
Mark J. Ray. 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. The
Rweb Home Page 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/).

Node:R Add-On Packages, Next:R and Emacs, Previous:R Web Interfaces, Up:Top

- Which add-on packages exist for R?:
- How can add-on packages be installed?:
- How can add-on packages be used?:
- How can add-on packages be removed?:
- How can I create an R package?:
- How can I contribute to R?:

Node:Which add-on packages exist for R?, Next:How can add-on packages be installed?, Previous:R Add-On Packages, Up:R Add-On Packages

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.
**tools**- Tools for package development and administration.
**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.
**Bhat**- Functions for general likelihood exploration (MLE, MCMC, CIs).
**CircStats**- Circular Statistics, from "Topics in Circular Statistics" by S. Rao
Jammalamadaka and A. SenGupta, 2001, World Scientific.
**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.
**EMV**- Estimation of missing values in a matrix by a k-th nearest
neighboors algorithm.
**GLMMGibbs**- Generalised Linear Mixed Models by Gibbs sampling.
**GenKern**- Functions for generating and manipulating generalised binned kernel
density estimates.
**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.
**RArcInfo**- Functions to import Arc/Info V7.x coverages and data.
**RMySQL**- An interface between R and the MySQL database system.
**ROracle**- Oracle Database Interface driver for R. Uses the ProC/C++ embedded SQL.
**RPgSQL**- Provides methods for accessing data stored in PostgreSQL tables.
**RQuantLib**- Provides access to (some) of the QuantLib functions from within R;
currently limited to some Option pricing and analysis functions. The
QuantLib project aims to provide a comprehensive software framework for
quantitative finance.
**RSQLite**- Database Interface R driver for SQLite. Embeds the SQLite database
engine in R.
**RadioSonde**- A collection of programs for reading and plotting SKEW-T,log p diagrams
and wind profiles for data collected by radiosondes (the typical weather
balloon-borne instrument).
**RmSQL**- An interface between R and the mSQL database system.
**Rmpi**- an interface (wrapper) to MPI (Message-Passing Interface) APIs. It also
provides interactive R slave functionalities to make MPI programming
easier in R than in C(++) or FORTRAN.
**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).
**VLMC**- Functions, classes & methods for estimation, prediction, and simulation
(bootstrap) of VLMC (Variable Length Markov Chain) models.
**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.
**agce**- Analysis of growth curve experiments.
**akima**- Linear or cubic spline interpolation for irregularly gridded data.
**ash**- David Scott's ASH routines for 1D and 2D density estimation.
**aws**- Functions to perform adaptive weights smoothing.
**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.
**cobs**- Constrained B-splines: qualitatively constrained (regression) smoothing
via linear programming.
**coda**- Output analysis and diagnostics for Markov Chain Monte Carlo (MCMC)
simulations.
**combinat**- Combinatorics utilities.
**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. **dblcens**- Calculates the NPMLE of the survival distribution for doubly censored
data.
**deldir**- Calculates the Delaunay triangulation and the Dirichlet or Voronoi
tesselation (with respect to the entire plane) of a planar point set.
**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.
**dr**- Functions, methods, and datasets for fitting dimension reduction
regression, including pHd and inverse regression methods SIR and SAVE.
**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, support vector machines, and
fuzzy clustering, shortest path computation, bagged clustering, and some
more.
**ellipse**- Package for drawing ellipses and ellipse-like confidence regions.
**emplik**- Empirical likelihood ratio for means/quantiles/hazards from possibly
right censored data.
**evd**- Functions for extreme value distributions. Extends simulation,
distribution, quantile and density functions to univariate, bivariate
and (for simulation) multivariate parametric extreme value
distributions, and provides fitting functions which calculate maximum
likelihood estimates for univariate and bivariate models.
**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).
**g.data**- Create and maintain delayed-data packages (DDP's).
**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.
**geoRglm**- Functions for inference in generalised linear spatial models.
**gld**- Basic functions for the generalised (Tukey) lambda distribution.
**gllm**- Routines for log-linear models of incomplete contingency tables,
including some latent class models via EM and Fisher scoring approaches.
**gregmisc**- Miscellaneous functions written/maintained by Gregory R. Warnes.
**grid**- The Grid graphics package, a rewrite of the graphics layout
capabilities, plus some support for interaction.
**gss**- A comprehensive package for structural multivariate function estimation
using smoothing splines.
**hdf5**- Interface to the NCSA HDF5 library.
**ifs**- Iterated Function Systems distribution function estimator.
**ineq**- Inequality, concentration and poverty measures, and Lorenz curves
(empirical and theoretic).
**ipred**- Improved predictive models by direct and indirect bootstrap aggregation
in classification and regression as well as resampling based estimators
of prediction error.
**lasso2**- Routines and documentation for solving regression problems while
imposing an L1 constraint on the estimates, based on the algorithm of
Osborne et al. (1998)
**lattice**- Lattice graphics, an implementation of Trellis Graphics functions.
**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.
**moc**- Fits a variety of mixtures models for multivariate observations with
user-difined distributions and curves.
**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.
**npmc**- Nonparametric Multiple Comparisons: provides simultaneous rank test
procedures for the one-way layout without presuming a certain
distribution.
**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.
**pastecs**- Package for Analysis of Space-Time Ecological Series.
**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.
**pear**- Periodic Autoregression Analysis.
**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.
**pixmap**- Functions for import, export, plotting and other manipulations of
bitmapped images.
**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.
**qtl**- Analysis of experimental crosses to identify QTLs.
**quadprog**- For solving quadratic programming problems.
**quantreg**- Quantile regression and related methods.
**randomForest**- Breiman's random forest classifier.
**rmeta**- Functions for simple fixed and random effects meta-analysis for
two-sample comparison of binary outcomes.
**rpart**- Recursive PARTitioning and regression trees.
**rpvm**- R interface to PVM (Parallel Virtual Machine). Provides interface to
PVM APIs, and examples and documentation for its use.
**rsprng**- Provides interface to SPRNG (Scalable Parallel Random Number Generators)
APIs, and examples and documentation for its use.
**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. **spatstat**- Data analysis and modelling of two-dimensional point patterns, including
multitype points and spatial covariates.
**spdep**- A collection of functions to create spatial weights matrix objects from
polygon contiguities, from point patterns by distance and tesselations,
for summarising these objects, and for permitting their use in spatial
data analysis; a collection of tests for spatial autocorrelation,
including global Moran's I and Geary's C, local Moran's I, saddlepoint
approximations for global and local Moran's I; and functions for
estimating spatial simultaneous autoregressive (SAR) models. (Was
formerly the three packages:
**spweights**,**sptests**, and**spsarlm**.) **splancs**- Spatial and space-time point pattern analysis functions.
**strucchange**- Various tests on structural change in linear regression models.
**subselect**- A collection of functions which assess the quality of variable subsets
as surrogates for a full data set, and search for subsets which are
optimal under various criteria.
**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.
**vegan**- Various help functions for vegetation scientists and community
ecologists.
**waveslim**- Basic wavelet routines for time series analysis.
**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.

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

**DBI**- A common database interface (DBI) class and method definitions. All
classes in this package are virtual and need to be extended by the
various DBMS implementations.
**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.
**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.
**R2HTML**- Functions for exporting R objects & graphics in an HTML document.
**RODBC**- An ODBC database interface.
**RandomFields**- Creating random fields using various methods.
**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.
**event.chart**- Package for creating event charts.
**hpower**- A suite of functions to compute power and sample size for tests of the
general linear hypothesis.
**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. 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.

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

Node:How can add-on packages be installed?, Next:How can add-on packages be used?, Previous:Which add-on packages exist for R?, Up:R Add-On Packages

(Unix only.) The add-on packages on CRAN come as gzipped tar
files named

, which may in fact be
"bundles" containing more than one package. Provided that
`pkg`_`version`.tar.gz`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

) otherwise. (Versions of R prior to 1.3.0 installed
to the default library by default.)
`R_HOME`

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

$ R CMD INSTALL -llib/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.)

Node:How can add-on packages be used?, Next:How can add-on packages be removed?, Previous:How can add-on packages be installed?, Up:R Add-On Packages

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, ... grid The Grid Graphics Package lattice Lattice Graphics 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 tools Tools for Package Development and Administration 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")

Node:How can add-on packages be removed?, Next:How can I create an R package?, Previous:How can add-on packages be used?, Up:R Add-On Packages

Use

$ R CMD REMOVEpkg_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 -llibpkg_1...pkg_n

Node:How can I create an R package?, Next:How can I contribute to R?, Previous:How can add-on packages be removed?, Up:R Add-On Packages

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, see 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.

See What is CRAN?, for information on uploading a package to CRAN.

Node:How can I contribute to R?, Previous:How can I create an R package?, Up:R Add-On Packages

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 What is S?); Generalized Additive Models (see Are GAMs implemented in R?) and some of the nonlinear modeling code are not there yet.

The R Developer Page 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.

Node:R and Emacs, Next:R Miscellanea, Previous:R Add-On Packages, Up:Top

Node:Is there Emacs support for R?, Next:Should I run R from within Emacs?, Previous:R and Emacs, Up:R and Emacs

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) and SAS. Stata and SPSS dialect (SPSS, PSPP) support is being examined for possible future implementation

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 stable version of ESS are available via CRAN or the ESS web page. 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.

Node:Should I run R from within Emacs?, Next:Debugging R from within Emacs, Previous:Is there Emacs support for R?, Up:R and 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.

Node:Debugging R from within Emacs, Previous:Should I run R from within Emacs?, Up:R and 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

Node:R Miscellanea, Next:R Programming, Previous:R and Emacs, Up:Top

- Why does R run out of memory?:
- Why does sourcing a correct file fail?:
- How can I set components of a list to NULL?:
- How can I save my workspace?:
- How can I clean up my workspace?:
- How can I get eval() and D() to work?:
- Why do my matrices lose dimensions?:
- How does autoloading work?:
- How should I set options?:
- How do file names work in Windows?:
- Why does plotting give a color allocation error?:
- How do I convert factors to numeric?:
- Are Trellis displays implemented in R?:
- What are the enclosing and parent environments?:
- How can I substitute into a plot label?:
- What are valid names?:
- Are GAMs implemented in R?:
- Why is the output not printed when I source() a file?:
- Why does outer() behave strangely with my function?:
- Why does the output from anova() depend on the order of factors in the model?:

Node:Why does R run out of memory?, Next:Why does sourcing a correct file fail?, Previous:R Miscellanea, Up:R Miscellanea

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.

The new garbage collector does not move objects in memory, meaning that it is possible for the free memory to become fragmented so that large objects cannot be allocated even when there is apparently enough memory for them.

Node:Why does sourcing a correct file fail?, Next:How can I set components of a list to NULL?, Previous:Why does R run out of memory?, Up:R Miscellanea

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.

Node:How can I set components of a list to NULL?, Next:How can I save my workspace?, Previous:Why does sourcing a correct file fail?, Up:R Miscellanea

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.

Node:How can I save my workspace?, Next:How can I clean up my workspace?, Previous:How can I set components of a list to NULL?, Up:R Miscellanea

`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(`

one
can save the image under a different name.
`file`)

Node:How can I clean up my workspace?, Next:How can I get eval() and D() to work?, Previous:How can I save my workspace?, Up:R Miscellanea

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.)

Node:How can I get eval() and D() to work?, Next:Why do my matrices lose dimensions?, Previous:How can I clean up my workspace?, Up:R Miscellanea

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.

Node:Why do my matrices lose dimensions?, Next:How does autoloading work?, Previous:How can I get eval() and D() to work?, Up:R Miscellanea

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]

Node:How does autoloading work?, Next:How should I set options?, Previous:Why do my matrices lose dimensions?, Up:R Miscellanea

R has a special environment called `.AutoloadEnv`

. Using
`autoload( name, pkg)`, where

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.

Node:How should I set options?, Next:How do file names work in Windows?, Previous:How does autoloading work?, Up:R Miscellanea

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.

Node:How do file names work in Windows?, Next:Why does plotting give a color allocation error?, Previous:How should I set options?, Up:R Miscellanea

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"`

).

Node:Why does plotting give a color allocation error?, Next:How do I convert factors to numeric?, Previous:How do file names work in Windows?, Up:R Miscellanea

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.

Node:How do I convert factors to numeric?, Next:Are Trellis displays implemented in R?, Previous:Why does plotting give a color allocation error?, Up:R Miscellanea

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.

Node:Are Trellis displays implemented in R?, Next:What are the enclosing and parent environments?, Previous:How do I convert factors to numeric?, Up:R Miscellanea

The recommended package **lattice** (which is based on another
recommended package, **grid**) provides graphical functionality
that is compatible with most Trellis commands.

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 chosen for the R equivalent.)

Node:What are the enclosing and parent environments?, Next:How can I substitute into a plot label?, Previous:Are Trellis displays implemented in R?, Up:R Miscellanea

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.

Node:How can I substitute into a plot label?, Next:What are valid names?, Previous:What are the enclosing and parent environments?, Up:R Miscellanea

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.

Node:What are valid names?, Next:Are GAMs implemented in R?, Previous:How can I substitute into a plot label?, Up:R Miscellanea

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:

- 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. - 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()`

). - 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. - 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.) - 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".

Node:Are GAMs implemented in R?, Next:Why is the output not printed when I source() a file?, Previous:What are valid names?, Up:R Miscellanea

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**.

Node:Why is the output not printed when I source() a file?, Next:Why does outer() behave strangely with my function?, Previous:Are GAMs implemented in R?, Up:R Miscellanea

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"`

(see 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)

Node:Why does outer() behave strangely with my function?, Next:Why does the output from anova() depend on the order of factors in the model?, Previous:Why is the output not printed when I source() a file?, Up:R Miscellanea

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)

Node:Why does the output from anova() depend on the order of factors in the model?, Previous:Why does outer() behave strangely with my function?, Up:R Miscellanea

In a model such as `~A+B+A:B`

, R will report the difference in sums
of squares between the models `~1`

, `~A`

, `~A+B`

and
`~A+B+A:B`

. If the model were `~B+A+A:B`

, R would report
differences between `~1`

, `~B`

, `~A+B`

, and
`~A+B+A:B`

. In the first case the sum of squares for `A`

is
comparing `~1`

and `~A`

, in the second case it is comparing
`~B`

and `~B+A`

. In a non-orthogonal design (i.e., most
unbalanced designs) these comparisons are (conceptually and numerically)
different.

Some packages report instead the sums of squares based on comparing the full model to the models with each factor removed one at a time (the famous `Type III sums of squares' from SAS, for example). These do not depend on the order of factors in the model. The question of which set of sums of squares is the Right Thing provokes low-level holy wars on r-help from time to time.

There is no need to be agitated about the particular sums of squares
that R reports. You can compute your favorite sums of squares quite
easily. Any two models can be compared with `anova(`

, and `model1`,
`model2`)`drop1(`

will show the sums of
squares resulting from dropping single terms.
`model1`)

Node:R Programming, Next:R Bugs, Previous:R Miscellanea, Up:Top

- How should I write summary methods?:
- How can I debug dynamically loaded code?:
- How can I inspect R objects when debugging?:
- How can I change compilation flags?:

Node:How should I write summary methods?, Next:How can I debug dynamically loaded code?, Previous:R Programming, Up:R Programming

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.

Node:How can I debug dynamically loaded code?, Next:How can I inspect R objects when debugging?, Previous:How should I write summary methods?, Up:R Programming

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, see What documentation exists for R?.

Node:How can I inspect R objects when debugging?, Next:How can I change compilation flags?, Previous:How can I debug dynamically loaded code?, Up:R Programming

The most convenient way is to call `R_PV`

from the symbolic
debugger.

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

Node:How can I change compilation flags?, Previous:How can I inspect R objects when debugging?, Up:R Programming

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

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
`R_HOME`

/etc/Makeconf`MAKEFLAGS`

, as in

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

Node:R Bugs, Next:Acknowledgments, Previous:R Programming, Up:Top

Node:What is a bug?, Next:How to report a bug, Previous:R Bugs, Up:R Bugs

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.

Node:How to report a bug, Previous:What is a bug?, Up:R Bugs

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.

Node:Acknowledgments, Previous:R Bugs, Up:Top

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 ...