[Vegan-commits] r2616 - in branches/2.0: . inst inst/doc vignettes
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Sep 11 10:34:17 CEST 2013
Author: jarioksa
Date: 2013-09-11 10:34:17 +0200 (Wed, 11 Sep 2013)
New Revision: 2616
Added:
branches/2.0/vignettes/
branches/2.0/vignettes/decision-vegan.Rnw
Removed:
branches/2.0/.Rinstignore
branches/2.0/inst/doc/FAQ-vegan.texi
branches/2.0/inst/doc/Makefile
branches/2.0/inst/doc/decision-vegan.Rnw
branches/2.0/inst/doc/diversity-vegan.Rnw
branches/2.0/inst/doc/intro-vegan.Rnw
branches/2.0/inst/doc/vegan.bib
branches/2.0/inst/doc/vegan.sty
branches/2.0/vignettes/decision-vegan.Rnw
Modified:
branches/2.0/DESCRIPTION
branches/2.0/inst/ChangeLog
Log:
merge r2597: move vignettes to vignettes/ directory and depend on R >= 2.14.0
Deleted: branches/2.0/.Rinstignore
===================================================================
--- branches/2.0/.Rinstignore 2013-09-11 07:22:45 UTC (rev 2615)
+++ branches/2.0/.Rinstignore 2013-09-11 08:34:17 UTC (rev 2616)
@@ -1,5 +0,0 @@
-Makefile
-.*tex$
-.*sty$
-.*bib$
-.*texi$
Modified: branches/2.0/DESCRIPTION
===================================================================
--- branches/2.0/DESCRIPTION 2013-09-11 07:22:45 UTC (rev 2615)
+++ branches/2.0/DESCRIPTION 2013-09-11 08:34:17 UTC (rev 2616)
@@ -6,8 +6,8 @@
Peter R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos,
M. Henry H. Stevens, Helene Wagner
Maintainer: Jari Oksanen <jari.oksanen at oulu.fi>
-Depends: lattice, permute
-Suggests: MASS, mgcv, cluster, scatterplot3d, rgl, tcltk
+Depends: permute, lattice, R (>= 2.14.0)
+Suggests: MASS, mgcv, cluster, scatterplot3d, rgl, tcltk
Description: Ordination methods, diversity analysis and other
functions for community and vegetation ecologists.
License: GPL-2
Modified: branches/2.0/inst/ChangeLog
===================================================================
--- branches/2.0/inst/ChangeLog 2013-09-11 07:22:45 UTC (rev 2615)
+++ branches/2.0/inst/ChangeLog 2013-09-11 08:34:17 UTC (rev 2616)
@@ -6,6 +6,8 @@
* merge 2613: 'medoid' to 'median' in betadisper output and doc.
* merge 2600: remove tools::: in vegandocs().
+ * merge 2597: move vignettes from inst/doc to vignettes/; depend
+ on R 2.14.0.
* merge 2593: remove stats::: in anova.ccabyaxis().
* merge 2590: typos.
* merge r2588: remove unneeded utils::: in vegandocs (some remain).
Deleted: branches/2.0/inst/doc/FAQ-vegan.texi
===================================================================
--- branches/2.0/inst/doc/FAQ-vegan.texi 2013-09-11 07:22:45 UTC (rev 2615)
+++ branches/2.0/inst/doc/FAQ-vegan.texi 2013-09-11 08:34:17 UTC (rev 2616)
@@ -1,833 +0,0 @@
-\input texinfo
-
- at macro pkg {p}
- at strong{\p\}
- at end macro
-
- at c %**start of header
- at setfilename FAQ- at pkg{vegan}.info
- at settitle @pkg{vegan} FAQ
- at setchapternewpage on
- at set FAQ_YEAR 2013
- at afourpaper
- at c %**end of header
-
- at copying
- at ifnottex
-This document contains answers to some of the most frequently asked
-questions about R package @pkg{vegan}.
-This is version of $Date$.
- at end ifnottex
-
- at quotation
-This work is licensed under the Creative Commons Attribution 3.0
-License. To view a copy of this license, visit
- at uref{http://creativecommons.org/licenses/by/3.0/} or send a letter to
-Creative Commons, 543 Howard Street, 5th Floor, San Francisco,
-California, 94105, USA.
-
-Copyright @copyright{} 2008-2013 Jari Oksanen
- at end quotation
- at end copying
-
- at dircategory Programming
- at direntry
-* R @pkg{vegan} FAQ: (FAQ- at pkg{vegan}). FAQ for R package @pkg{vegan}.
- at end direntry
-
- at finalout
-
- at titlepage
- at title @pkg{vegan} @acronym{FAQ}
- at subtitle Frequently Asked Questions on R package @pkg{vegan}
- at subtitle Version of $Date$
- at author Jari Oksanen
-
- at vskip 0pt plus 1fill
- at insertcopying
- at end titlepage
-
- at ifnothtml
- at contents
- at end ifnothtml
-
-
- at ifnottex
- at node Top, Introduction, (dir), (dir)
- at top @pkg{vegan} FAQ
- at insertcopying
- at end ifnottex
-
-
-
-
- at menu
-* Introduction::
-* Ordination::
-* Other analysis methods ::
- at end menu
-
- at node Introduction, Ordination, Top, Top
- at chapter Introduction
-
- at menu
-* What is @pkg{vegan}?::
-* What is R?::
-* How to obtain @pkg{vegan} and R?::
-* What R packages @pkg{vegan} depends on?::
-* What other packages are available for ecologists?::
-* What other documentation is available for @pkg{vegan}?::
-* Is there a Graphical User Interface (GUI) for @pkg{vegan}?::
-* How to cite @pkg{vegan}?::
-* How to build @pkg{vegan} from sources?::
-* Are there binaries for devel versions?::
-* Can I use @pkg{vegan} in Mac?::
-* How to report a bug in @pkg{vegan}?::
-* Is it a bug or a feature?::
-* Can I contribute to @pkg{vegan}?::
-* Can I have write access to @pkg{vegan} repository?::
- at end menu
-
- at node What is @pkg{vegan}?, What is R?, Introduction, Introduction
- at section What is @pkg{vegan}?
-
- at pkg{Vegan} is an R package for community ecologists. It contains the most
-popular methods of multivariate analysis needed in analysing ecological
-communities, and tools for diversity analysis, and other potentially
-useful functions. @pkg{Vegan} is not self-contained but it must be run under
-R statistical environment, and it also depends on many other R
-packages. @pkg{Vegan} is @uref{http://www.gnu.org/philosophy/free-sw.html, free
-software} and distributed under
- at uref{http://www.gnu.org/licenses/gpl.html, ,GPL2 license}.
-
- at node What is R?, How to obtain @pkg{vegan} and R?, What is @pkg{vegan}?, Introduction
- at section What is R?
-
-R is a system for statistical computation and graphics. It consists of
-a language plus a run-time environment with graphics, a debugger, access
-to certain system functions, and the ability to run programs stored in
-script files.
-
-R has a home page at @uref{http://www.R-project.org/}. It is
- at uref{http://www.gnu.org/philosophy/free-sw.html, free software}
-distributed under a @acronym{GNU}-style
- at uref{http://www.gnu.org/copyleft/copyleft.html, copyleft}, and an
-official part of the @uref{http://www.gnu.org/, @acronym{GNU}} project
-(``@acronym{GNU} S'').
-
- at node How to obtain @pkg{vegan} and R?, What R packages @pkg{vegan} depends on?, What is R?, Introduction
- at section How to obtain @pkg{vegan} and R?
-
-Both R and latest release version of @pkg{vegan} can be obtained through
- at uref{http://cran.r-project.org,,CRAN}. Unstable development version of
- at pkg{vegan} can be obtained through
- at uref{http://r-forge.r-project.org/projects/vegan/,,R-Forge}.
-
-
-
- at node What R packages @pkg{vegan} depends on?, What other packages are available for ecologists?, How to obtain @pkg{vegan} and R?, Introduction
- at section What R packages @pkg{vegan} depends on?
-
- at pkg{Vegan} depends on the @pkg{permute} package which will provide
-advanced and flexible permutation routines for vegan (but currently only
-a small part of functions use @pkg{permute}). The @pkg{permute} package
-is developed together with @pkg{vegan} in
- at uref{http://vegan.r-forge.r-project.org/,,R-Forge}.
-
-Some individual @pkg{vegan} functions depend on packages @pkg{MASS},
- at pkg{mgcv}, @pkg{cluster}, @pkg{lattice} and @pkg{tcltk}. These all are
-base or recommended R packages that should be available in every R
-installation. In addition, some @pkg{vegan} functions @code{require}
-non-standard R packages. @pkg{Vegan} declares these packages only as
-suggested ones, and you can install @pkg{vegan} and use most of its
-functions without these packages. The non-standard packages needed by
-some @pkg{vegan} functions are:
- at itemize
-
- at item Package @pkg{scatterplot3d}
-is needed by @code{ordiplot3d}
-
- at item Package @pkg{rgl}
-is needed by @code{ordirgl}
-and @code{rgl.isomap}
-
- at end itemize
-
- at node What other packages are available for ecologists?, What other documentation is available for @pkg{vegan}?, What R packages @pkg{vegan} depends on?, Introduction
- at section What other packages are available for ecologists?
-
- at acronym{CRAN} @uref{http://cran.r-project.org/src/contrib/Views/,,Task
-Views} include entries like @code{Environmetrics}, @code{Multivariate}
-and @code{Spatial} that describe several useful packages and functions.
-If you install R package @pkg{ctv}, you can inspect Task Views from your
-R session, and automatically install sets of most important packages.
-
- at node What other documentation is available for @pkg{vegan}?, Is there a Graphical User Interface (GUI) for @pkg{vegan}?, What other packages are available for ecologists?, Introduction
- at section What other documentation is available for @pkg{vegan}?
-
- at pkg{Vegan} is a fully documented R package with standard help pages. These
-are the most authoritative sources of documentation (and as a last
-resource you can use the force and the read the source, as @pkg{vegan} is open
-source). @pkg{Vegan} package ships with other documents which can be read
-with @code{vegandocs} command (documented in the @pkg{vegan} help). The
-documents included in the @pkg{vegan} package are
- at itemize
- at item
- at pkg{Vegan} @code{NEWS}
- at item
- at pkg{Vegan} @code{ChangeLog}.
- at item
-This document (@code{FAQ-vegan.pdf}).
- at item
-Short introduction to basic ordination methods in @pkg{vegan}
-(@code{intro-vegan.pdf}).
- at item
-Introduction to diversity methods in @pkg{vegan} (@code{diversity-vegan.pdf}).
- at item
-Discussion on design decisions in @pkg{vegan} (@code{decision-vegan.pdf}).
- at item
-Description of variance partition procedures in function
- at code{varpart} (@code{partitioning.pdf}).
- at end itemize
-
-
-Web documents outside the package include:
- at itemize
-
- at item
- at uref{http://vegan.r-forge.r-project.org/}: @pkg{vegan} homepage.
- at item
- at uref{http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf}: @pkg{vegan}
-tutorial.
-
- at end itemize
-
- at node Is there a Graphical User Interface (GUI) for @pkg{vegan}?, How to cite @pkg{vegan}?, What other documentation is available for @pkg{vegan}?, Introduction
- at section Is there a Graphical User Interface (GUI) for @pkg{vegan}?
-
-Roeland Kindt has made package @pkg{BiodiversityR} which provides a
-GUI for @pkg{vegan}. The package is available at
- at uref{http://cran.r-project.org/src/contrib/Descriptions/BiodiversityR.html,,CRAN}.
-It is not a mere GUI for @pkg{vegan}, but adds some new functions and
-complements @pkg{vegan} functions in order to provide a
-workbench for biodiversity analysis. You can install @pkg{BiodiversityR} using
- at code{install.packages("BiodiversityR")} or graphical package
-management menu in R. The GUI works on Windows, MacOS X and Linux.
-
- at node How to cite @pkg{vegan}?, How to build @pkg{vegan} from sources?, Is there a Graphical User Interface (GUI) for @pkg{vegan}?, Introduction
- at section How to cite @pkg{vegan}?
-
-Use command @code{citation("vegan")} in R to see the recommended
-citation to be used in publications.
-
- at node How to build @pkg{vegan} from sources?, Are there binaries for devel versions?, How to cite @pkg{vegan}?, Introduction
- at section How to build @pkg{vegan} from sources?
-
-In general, you do not need to build @pkg{vegan} from sources, but binary
-builds of release versions are available through
- at uref{http://cran.r-project.org/,,CRAN} for Windows and MacOS X. If you
-use some other operating systems, you may have to use source packages.
- at pkg{Vegan} is a standard R package, and can be built like instructed in R
-documentation. @pkg{Vegan} contains source files in C and @acronym{FORTRAN},
-and you need appropriate compilers (which may need more work in Windows
-and MacOS X).
-
- at node Are there binaries for devel versions?, Can I use @pkg{vegan} in Mac?, How to build @pkg{vegan} from sources?, Introduction
- at section Are there binaries for devel versions?
-
- at uref{http://r-forge.r-project.org/projects/vegan/,,R-Forge} runs daily
-tests on the devel package, and if passed, it builds source package
-together with Windows and MacOS X binaries. You can install those
-packages within R with command
- at code{install.packages("vegan", repos="http://r-forge.r-project.org/")}.
-If you use GUI menu entry, you must select or define the R-Forge
-repository.
-
- at node Can I use @pkg{vegan} in Mac?, How to report a bug in @pkg{vegan}?, Are there binaries for devel versions?, Introduction
- at section Can I use @pkg{vegan} in Mac?
-
-Yes, you can, and @pkg{vegan} binaries are available for Mac through
- at uref{http://cran.r-project.org,,CRAN}. However, in some cases you may
-need to install extra tools packages available in
- at uref{http://cran.r-project.org/bin/macosx/tools/,,MacOS tools} pages:
-If you use function such as @code{orditkplot} that need @code{Tcl/Tk}
-you may need to install @code{tcltk} package. If you use @pkg{vegan}
-binaries from other places than from
- at uref{http://cran.r-project.org,,CRAN}, you may also need to install
- at code{gfortran} package.
-
- at node How to report a bug in @pkg{vegan}?, Is it a bug or a feature?, Can I use @pkg{vegan} in Mac?, Introduction
- at section How to report a bug in @pkg{vegan}?
-
-If you think you have found a bug in @pkg{vegan}, you should report it to
- at pkg{vegan} maintainers or developers. The bug report should be so detailed
-that the bug can be replicated and corrected. Preferably, you should
-send an example that causes a bug. If it needs a data set that is not
-available in R, you should send a minimal data set as well. You also
-should paste the output or error message in your message. You also
-should specify which version of @pkg{vegan} you used.
-
-Bug reports are welcome: they are the only way to make @pkg{vegan} non-buggy.
-
-Please note that you shall not send bug reports to R mailing lists,
-since @pkg{vegan} is not a standard R package.
-
-There also is a bug reporting tool at
- at uref{http://r-forge.r-project.org/projects/vegan/,,R-Forge}, but you
-need to register as a site user to report bugs (this is site policy).
-
- at node Is it a bug or a feature?, Can I contribute to @pkg{vegan}?, How to report a bug in @pkg{vegan}?, Introduction
- at section Is it a bug or a feature?
-
-It is not necessarily a bug if some function gives different
-results than you expect: That may be a deliberate design decision. It
-may be useful to check the documentation of the function to see what
-was the intended behaviour. It may also happen that function has an
-argument to switch the behaviour to match your expectation. For
-instance, function @code{vegdist} always calculates quantitative
-indices (when this is possible). If you expect it to calculate a
-binary index, you should use argument @code{binary = TRUE}.
-
- at node Can I contribute to @pkg{vegan}?, Can I have write access to @pkg{vegan} repository?, Is it a bug or a feature?, Introduction
- at section Can I contribute to @pkg{vegan}?
-
- at pkg{Vegan} is dependent on user contribution. All feedback is welcome. If
-you have problem with @pkg{vegan}, it may be as simple as incomplete
-documentation, and we'll do our best to improve the documents.
-
-Feature requests also are welcome, but they are not necessarily
-fulfilled. A new feature will be added if it is easy to do and it looks
-useful to me or in general, or if you submit code.
-
-Contributed code and functions are welcome and more certain to be
-included than mere requests. However, not all functions will be added,
-but I they must be suitable for @pkg{vegan}. We also audit the code, and
-typically we edit the code in @pkg{vegan} style for easier maintenance. All
-included contributions will be credited.
-
- at node Can I have write access to @pkg{vegan} repository?, , Can I contribute to @pkg{vegan}?, Introduction
- at section Can I have write access to @pkg{vegan} repository?
-
-The @pkg{vegan} development happens mainly in
- at uref{http://r-forge.r-project.org/,,R-Forge} which uses subversion for
-version control. Subversion is a centralized version control system,
-and only @pkg{vegan} developers can have write access to the central
-repository. However, the @uref{http://r-forge.r-project.org/,,R-Forge}
-is mirrored in
- at uref{https://github.com/jarioksa/vegan.git,,GitHub}. This is a
-distributed version control system and freely accessible for anybody. We
-suggest you develop your own ideas in
- at uref{https://github.com/jarioksa/vegan.git,,GitHub} and send a pull
-request to us for incorporating your changes in @pkg{vegan} releases.
-
- at node Ordination, Other analysis methods , Introduction, Top
- at chapter Ordination
-
- at menu
-* I have only numeric and positive data but @pkg{vegan} still complains::
-* Can I analyse binary or cover class data?::
-* Why dissimilarities in @pkg{vegan} differ from other sources?::
-* Why NMDS stress is sometimes 0.1 and sometimes 10?::
-* I get zero stress but no convergent solutions in @code{metaMDS}::
-* Zero dissimilarities in isoMDS::
-* I have heard that you cannot fit environmental vectors or surfaces to NMDS results which only have rank-order scores::
-* Where can I find numerical scores of ordination axes?::
-* How the RDA results are scaled?::
-* cca fails with ``data.frame expected'' or ``"site.env" missing''::
-* Variance explained by ordination axes::
-* Can I have random effects in constrained ordination or in @code{adonis}?::
-* Is it possible to have passive points in ordination?::
-* Class variables and dummies::
-* How are environmental arrows scaled?::
-* I want to use Helmert or sum contrasts::
-* What are aliased variables and how to see them?::
-* Plotting aliased variables::
-* Constrained permutations in @pkg{vegan}::
-* How to use different plotting symbols in ordination graphics?::
-* How to avoid cluttered ordination graphs?::
-* Can I flip an axis in ordination diagram?::
-* Can I zoom into an ordination plot?::
- at end menu
-
- at node I have only numeric and positive data but @pkg{vegan} still complains, Can I analyse binary or cover class data?, Ordination, Ordination
- at comment node-name, next, previous, up
- at section I have only numeric and positive data but @pkg{vegan} still complains
-
-You are wrong! Computers are painfully pedantic, and if they find
-non-numeric or negative data entries, you really have them. Check your
-data. Most common reasons for non-numeric data are that row names were
-read as a non-numeric variable instead of being used as row names (check
-argument @code{row.names} in reading the data), or that the column names
-were interpreted as data (check argument @code{header = TRUE} in reading
-the data). Another common reason is that you had empty cells in your
-input data, and these were interpreted as missing values.
-
- at node Can I analyse binary or cover class data?, Why dissimilarities in @pkg{vegan} differ from other sources?, I have only numeric and positive data but @pkg{vegan} still complains, Ordination
- at section Can I analyse binary or cover class data?
-
-Yes. Most @pkg{vegan} methods can handle binary data or cover abundance data.
-Most statistical tests are based on permutation, and do not make
-distributional assumptions. There are some methods (mainly in diversity
-analysis) that need count data. These methods check that input data are
-integers, but they may be fooled by cover class data.
-
- at node Why dissimilarities in @pkg{vegan} differ from other sources?, Why NMDS stress is sometimes 0.1 and sometimes 10?, Can I analyse binary or cover class data?, Ordination
- at section Why dissimilarities in @pkg{vegan} differ from other sources?
-
-Most commonly the reason is that other software use presence--absence
-data whereas @pkg{vegan} used quantitative data. Usually @pkg{vegan} indices are
-quantitative, but you can use argument @code{binary = TRUE} to make them
-presence--absence. However, the index name is the same in both cases,
-although different names usually occur in literature. For instance,
-Jaccard index actually refers to the binary index, but @pkg{vegan} uses
-name @code{"jaccard"} for the quantitative index, too.
-
-Another reason may be that indices indeed are defined differently,
-because people use same names for different indices.
-
- at node Why NMDS stress is sometimes 0.1 and sometimes 10?, I get zero stress but no convergent solutions in @code{metaMDS}, Why dissimilarities in @pkg{vegan} differ from other sources?, Ordination
- at section Why @acronym{NMDS} stress is sometimes 0.1 and sometimes 10?
-
-Stress is a proportional measure of badness of fit. The proportions can
-be expressed either as parts of one or as percents. Function
- at code{isoMDS} (@pkg{MASS} package) uses percents, and function @code{monoMDS}
-(@pkg{vegan} package) uses proportions, and therefore the same stress is 100
-times higher in @code{isoMDS}. The results of @code{goodness} function
-also depend on the definition of stress, and the same @code{goodness} is
-100 times higher in @code{isoMDS} than in @code{monoMDS}. Both of these
-conventions are equally correct.
-
- at node I get zero stress but no convergent solutions in @code{metaMDS}, Zero dissimilarities in isoMDS, Why NMDS stress is sometimes 0.1 and sometimes 10?, Ordination
- at section I get zero stress but no convergent solutions in @code{metaMDS}
-
-Most common reason is that you have too few observations for your
- at acronym{NMDS}. For @code{n} observations (points) and @code{k}
-dimensions you need to estimate @code{n*k} parameters (ordination
-scores) using @code{n*(n-1)/2} dissimilarities. For @code{k} dimensions
-you must have @code{n > 2*k + 1}, or for two dimensions at least six
-points. In some degenerate situations you may need even a larger number
-of points. If you have a lower number of points, you can find an
-undefined number of perfect (stress is zero) but different solutions.
-Conventional wisdom due to Kruskal is that you should have @code{n > 4*k
-+ 1} points for @code{k} dimensions. A typical symptom of insufficient
-data is that you have (nearly) zero stress but no two convergent
-solutions. In those cases you should reduce the number of dimensions
-(@code{k}) and with very small data sets you should not use @code{NMDS},
-but rely on metric methods.
-
-It seems that local and hybrid scaling with @code{monoMDS} have similar
-lower limits in practice (although theoretically they could differ).
-However, higher number of dimensions can be used in metric scaling, both
-with @code{monoMDS} and in principal coordinates analysis
-(@code{cmdscale} in @pkg{stats}, @code{wcmdscale} in @pkg{vegan}).
-
- at node Zero dissimilarities in isoMDS, I have heard that you cannot fit environmental vectors or surfaces to NMDS results which only have rank-order scores, I get zero stress but no convergent solutions in @code{metaMDS}, Ordination
- at section Zero dissimilarities in isoMDS
-
-Function @code{metaMDS} uses function @code{monoMDS} as its default
-method for @acronym{NMDS}, and this function can handle zero
-dissimilarities. The alternative function @code{isoMDS} was the only
-choice before @pkg{vegan} 2.0-0, and it cannot handle zero dissimilarities. If
-you want to use @code{isoMDS}, you can use argument @code{zerodist =
-"add"} in @code{metaMDS} to handle zero dissimilarities. With this
-argument, zero dissimilarities are replaced with a small above zero
-value, and they can be handled in @code{isoMDS}. This is a kluge, and
-some people do not like this. A more principal solution is to remove
-duplicate sites using R command @code{unique}. However, after some
-standardizations or with some dissimilarity indices, originally
-non-unique sites can have zero dissimilarity, and you have to resort to
-the kluge (or work harder with your data). Usually it is better to use
- at code{monoMDS}.
-
- at node I have heard that you cannot fit environmental vectors or surfaces to NMDS results which only have rank-order scores, Where can I find numerical scores of ordination axes?, Zero dissimilarities in isoMDS, Ordination
- at section I have heard that you cannot fit environmental vectors or surfaces to NMDS results which only have rank-order scores
-
-Claims like this have indeed been at large in the Internet, but they are
-based on grave misunderstanding and are plainly wrong. @acronym{NMDS}
-ordination results are strictly metric, and in @pkg{vegan}
- at code{metaMDS} and @code{monoMDS} they are even strictly Euclidean. The
-method is called ``non-metric'' because the Euclidean distances in
-ordination space have a non-metric rank-order relationship to community
-dissimilarities. You can inspect this non-linear step curve using
-function @code{stressplot} in @pkg{vegan}. Because the ordination scores
-are strictly Euclidean, it is correct to use @pkg{vegan} functions
- at code{envfit} and @code{ordisurf} with @acronym{NMDS} results.
-
- at node Where can I find numerical scores of ordination axes?, How the RDA results are scaled?, I have heard that you cannot fit environmental vectors or surfaces to NMDS results which only have rank-order scores, Ordination
- at section Where can I find numerical scores of ordination axes?
-
-Normally you can use function @code{scores} to extract ordination scores
-for any ordination method. The @code{scores} function can also find
-ordination scores for many non- at pkg{vegan} functions such as for
- at code{prcomp} and @code{princomp} and for some @pkg{ade4} functions.
-
-In some cases the ordination result object stores raw scores, and
-the axes are also scaled appropriate when you access them with
- at code{scores}. For instance, in @code{cca} and @code{rda} the
-ordination object has only so-called normalized scores, and they are
-scaled for ordination plots or for other use when they are accessed with
- at code{scores}.
-
- at node How the RDA results are scaled?, cca fails with ``data.frame expected'' or ``"site.env" missing'', Where can I find numerical scores of ordination axes?, Ordination
- at section How the @acronym{RDA} results are scaled?
-
-The scaling or @acronym{RDA} results indeed differ from most other
-software packages. The scaling of @acronym{RDA} is such a complicated
-issue that it cannot be explained in this @acronym{FAQ}, but it is
-explained in a separate @acronym{pdf} document on ``Design decision and
-implementation details in vegan'' that you can read with @pkg{vegan}
-command @code{vegandocs("decision")}.
-
- at node cca fails with ``data.frame expected'' or ``"site.env" missing'', Variance explained by ordination axes, How the RDA results are scaled?, Ordination
- at section cca fails with ``data.frame expected'' or ``"site.env" missing''
-
-This is not a @pkg{vegan} error message, but it comes from the
- at code{cca} function in the @pkg{ade4} package. There is an unfortunate
-name clash, and if you have loaded @pkg{ade4} after @pkg{vegan}, the
- at pkg{ade4} version of @code{cca} will mask the @pkg{vegan} version. You
-can use the @pkg{vegan} version using command @code{vegan::cca()}. If
-you do not need package @pkg{ade4}, you can detach it with command
- at code{detach(package:ade4)}.
-
- at node Variance explained by ordination axes, Can I have random effects in constrained ordination or in @code{adonis}?, cca fails with ``data.frame expected'' or ``"site.env" missing'', Ordination
- at section Variance explained by ordination axes.
-
-In general, @pkg{vegan} does not directly give any statistics on the
-``variance explained'' by ordination axes or by the constrained axes.
-This is a design decision: I think this information is normally useless
-and often misleading. In community ordination, the goal typically is
-not to explain the variance, but to find the ``gradients'' or main
-trends in the data. The ``total variation'' often is meaningless, and
-all proportions of meaningless values also are meaningless. Often a
-better solution explains a smaller part of ``total variation''. For
-instance, in unstandardized principal components analysis most of the
-variance is generated by a small number of most abundant species, and
-they are easy to ``explain'' because data really are not very
-multivariate. If you standardize your data, all species are equally
-important. The first axes explains much less of the ``total
-variation'', but now they explain all species equally, and results
-typically are much more useful for the whole community. Correspondence
-analysis uses another measure of variation (which is not variance), and
-again it typically explains a ``smaller proportion'' than principal
-components but with a better result. Detrended correspondence analysis
-and nonmetric multidimensional scaling even do not try to ``explain''
-the variation, but use other criteria. All methods are incommensurable,
-and it is impossible to compare methods using ``explanation of
-variation''.
-
-If you still want to get ``explanation of variation'' (or a deranged
-editor requests that from you), it is possible to get this information
-for some methods:
- at itemize
- at item Eigenvector methods:
-Functions @code{rda}, @code{cca} and @code{capscale} give the variation
-of conditional (partialled), constrained (canonical) and residual
-components, but you must calculate the proportions by hand. Function
- at code{eigenvals} extracts the eigenvalues, and
- at code{summary(eigenvals(ord))} reports the proportions explained in the
-result object @code{ord}. Function @code{RsquareAdj} gives the
-R-squared and adjusted R-squared (if available) for constrained
-components. Function @code{goodness} gives the same statistics for
-individual species or sites (species are unavailable with
- at code{capscale}). In addition, there is a special function
- at code{varpart} for unbiased partitioning of variance between up to four
-separate components in redundancy analysis.
-
- at item Detrended correspondence analysis (function @code{decorana}).
-The total amount of variation is undefined in detrended correspondence
-analysis, and therefore proportions from total are unknown and
-undefined. @acronym{DCA} is not a method for decomposition of
-variation, and therefore these proportions would not make sense either.
-
- at item Nonmetric multidimensional scaling.
- at acronym{NMDS} is a method for nonlinear mapping, and the concept of
-of variation explained does not make sense. However, 1 - stress^2
-transforms nonlinear stress into quantity analogous to squared
-correlation coefficient. Function @code{stressplot} displays the
-nonlinear fit and gives this statistic.
-
- at end itemize
-
- at node Can I have random effects in constrained ordination or in @code{adonis}?, Is it possible to have passive points in ordination?, Variance explained by ordination axes, Ordination
- at section Can I have random effects in constrained ordination or in @code{adonis}?
-
-No. Strictly speaking, this is impossible. However, you can define
-models that respond to similar goals as random effects models, although
-they strictly speaking use only fixed effects.
-
-Constrained ordination functions @code{cca}, @code{rda} and
- at code{capscale} can have @code{Condition()} terms in their formula. The
- at code{Condition()} define partial terms that are fitted before other
-constraints and can be used to remove the effects of background
-variables, and their contribution to decomposing inertia (variance) is
-reported separately. These partial terms are often regarded as similar
-to random effects, but they are still fitted in the same way as other
-terms and strictly speaking they are fixed terms.
-
-Function @code{adonis} evaluates terms sequentially. In a model with
-right-hand-side @code{~ A + B} the effects of @code{A} are evaluated
-first, and the effects of @code{B} after removing the effects of
- at code{A}. Sequential tests are also available in @code{anova} function
-for constrained ordination results by setting argument @code{by = "term"}.
-In this way, the first terms can serve in a similar role as
-random effects, although they are fitted in the same way as all other
-terms, and strictly speaking they are fixed terms.
-
-The permutation tests can usually have a @code{strata} argument which
-restricts the permutations within levels of a factor given in the
-argument. This can be used to restrict the permutations within levels of
-factor regarded as a random term. More structured permutations are
-available with the @pkg{permute} package.
-
-A major reason why real random effects models are impossible in most
- at pkg{vegan} functions is that their tests are based on the permutation
-of the data. The data are given, that is fixed, and therefore
-permutation tests are basically tests of fixed terms on fixed data.
-Random effect terms would require permutations of data with a random
-component instead of the given, fixed data, and such tests are not
-available in @pkg{vegan}.
-
- at node Is it possible to have passive points in ordination?, Class variables and dummies, Can I have random effects in constrained ordination or in @code{adonis}?, Ordination
- at section Is it possible to have passive points in ordination?
-
- at pkg{Vegan} does not have a concept of passive points, or a point that should
-only little influence the ordination results. However, you can add
-points to eigenvector methods using @code{predict} functions with
- at code{newdata}. You can first perform an ordination without some
-species or sites, and then you can find scores for all points using your
-complete data as @code{newdata}. The @code{predict} functions are
-available for basic eigenvector methods in @pkg{vegan} (@code{cca},
- at code{rda}, @code{decorana}, for an up-to-date list, use command
- at code{methods("predict")}). You also can simulate the passive points in
-R by using low weights to row and columns (this is the method used in
-software with passive points). For instance, the following command makes
-row 3 ``passive'': @code{dune[3,] <- 0.001*dune[3,]}.
-
-
- at node Class variables and dummies, How are environmental arrows scaled?, Is it possible to have passive points in ordination?, Ordination
- at section Class variables and dummies
-
-You should define a class variable as an R @code{factor}, and @pkg{vegan} will
-automatically handle them with formula interface. You also can define
-constrained ordination without formula interface, but then you must
-code your class variables by hand.
-
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/vegan -r 2616
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