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This document contains answers to some of the most frequently asked questions about R package vegan. This is version of $Date: 2013-09-08 18:59:14 +0300 (Sun, 08 Sep 2013) $.
This work is licensed under the Creative Commons Attribution 3.0 License. To view a copy of this license, visit 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 © 2008-2013 Jari Oksanen
1. Introduction | ||
2. Ordination | ||
3. Other analysis methods |
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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. Vegan is not self-contained but it must be run under R statistical environment, and it also depends on many other R packages. Vegan is free software and distributed under GPL2 license.
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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 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”).
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Both R and latest release version of vegan can be obtained through CRAN. Unstable development version of vegan can be obtained through R-Forge.
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Vegan depends on the permute package which will provide advanced and flexible permutation routines for vegan (but currently only a small part of functions use permute). The permute package is developed together with vegan in R-Forge.
Some individual vegan functions depend on packages MASS,
mgcv, cluster, lattice and tcltk. These all are
base or recommended R packages that should be available in every R
installation. In addition, some vegan functions require
non-standard R packages. Vegan declares these packages only as
suggested ones, and you can install vegan and use most of its
functions without these packages. The non-standard packages needed by
some vegan functions are:
ordiplot3d
ordirgl
and rgl.isomap
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CRAN Task Views include entries like Environmetrics
, Multivariate
and Spatial
that describe several useful packages and functions.
If you install R package ctv, you can inspect Task Views from your
R session, and automatically install sets of most important packages.
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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 vegan is open
source). Vegan package ships with other documents which can be read
with vegandocs
command (documented in the vegan help). The
documents included in the vegan package are
NEWS
ChangeLog
.
FAQ-vegan.pdf
).
intro-vegan.pdf
).
diversity-vegan.pdf
).
decision-vegan.pdf
).
varpart
(partitioning.pdf
).
Web documents outside the package include:
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Roeland Kindt has made package BiodiversityR which provides a
GUI for vegan. The package is available at
CRAN.
It is not a mere GUI for vegan, but adds some new functions and
complements vegan functions in order to provide a
workbench for biodiversity analysis. You can install BiodiversityR using
install.packages("BiodiversityR")
or graphical package
management menu in R. The GUI works on Windows, MacOS X and Linux.
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Use command citation("vegan")
in R to see the recommended
citation to be used in publications.
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In general, you do not need to build vegan from sources, but binary builds of release versions are available through CRAN for Windows and MacOS X. If you use some other operating systems, you may have to use source packages. Vegan is a standard R package, and can be built like instructed in R documentation. Vegan contains source files in C and FORTRAN, and you need appropriate compilers (which may need more work in Windows and MacOS X).
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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
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.
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Yes, you can, and vegan binaries are available for Mac through
CRAN. However, in some cases you may
need to install extra tools packages available in
MacOS tools pages:
If you use function such as orditkplot
that need Tcl/Tk
you may need to install tcltk
package. If you use vegan
binaries from other places than from
CRAN, you may also need to install
gfortran
package.
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If you think you have found a bug in vegan, you should report it to 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 vegan you used.
Bug reports are welcome: they are the only way to make vegan non-buggy.
Please note that you shall not send bug reports to R mailing lists, since vegan is not a standard R package.
There also is a bug reporting tool at R-Forge, but you need to register as a site user to report bugs (this is site policy).
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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 vegdist
always calculates quantitative
indices (when this is possible). If you expect it to calculate a
binary index, you should use argument binary = TRUE
.
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Vegan is dependent on user contribution. All feedback is welcome. If you have problem with 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 vegan. We also audit the code, and typically we edit the code in vegan style for easier maintenance. All included contributions will be credited.
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The vegan development happens mainly in R-Forge which uses subversion for version control. Subversion is a centralized version control system, and only vegan developers can have write access to the central repository. However, the R-Forge is mirrored in GitHub. This is a distributed version control system and freely accessible for anybody. We suggest you develop your own ideas in GitHub and send a pull request to us for incorporating your changes in vegan releases.
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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 row.names
in reading the data), or that the column names
were interpreted as data (check argument 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.
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Yes. Most 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.
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Most commonly the reason is that other software use presence–absence
data whereas vegan used quantitative data. Usually vegan indices are
quantitative, but you can use argument 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 vegan uses
name "jaccard"
for the quantitative index, too.
Another reason may be that indices indeed are defined differently, because people use same names for different indices.
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Stress is a proportional measure of badness of fit. The proportions can
be expressed either as parts of one or as percents. Function
isoMDS
(MASS package) uses percents, and function monoMDS
(vegan package) uses proportions, and therefore the same stress is 100
times higher in isoMDS
. The results of goodness
function
also depend on the definition of stress, and the same goodness
is
100 times higher in isoMDS
than in monoMDS
. Both of these
conventions are equally correct.
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metaMDS
Most common reason is that you have too few observations for your
NMDS. For n
observations (points) and k
dimensions you need to estimate n*k
parameters (ordination
scores) using n*(n-1)/2
dissimilarities. For k
dimensions
you must have 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 n > 4*k
+ 1
points for 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
(k
) and with very small data sets you should not use NMDS
,
but rely on metric methods.
It seems that local and hybrid scaling with 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 monoMDS
and in principal coordinates analysis
(cmdscale
in stats, wcmdscale
in vegan).
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Function metaMDS
uses function monoMDS
as its default
method for NMDS, and this function can handle zero
dissimilarities. The alternative function isoMDS
was the only
choice before vegan 2.0-0, and it cannot handle zero dissimilarities. If
you want to use isoMDS
, you can use argument zerodist =
"add"
in metaMDS
to handle zero dissimilarities. With this
argument, zero dissimilarities are replaced with a small above zero
value, and they can be handled in isoMDS
. This is a kluge, and
some people do not like this. A more principal solution is to remove
duplicate sites using R command 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
monoMDS
.
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Claims like this have indeed been at large in the Internet, but they are
based on grave misunderstanding and are plainly wrong. NMDS
ordination results are strictly metric, and in vegan
metaMDS
and 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 stressplot
in vegan. Because the ordination scores
are strictly Euclidean, it is correct to use vegan functions
envfit
and ordisurf
with NMDS results.
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Normally you can use function scores
to extract ordination scores
for any ordination method. The scores
function can also find
ordination scores for many non-vegan functions such as for
prcomp
and princomp
and for some ade4 functions.
In some cases the ordination result object stores raw scores, and
the axes are also scaled appropriate when you access them with
scores
. For instance, in cca
and 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
scores
.
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The scaling or RDA results indeed differ from most other
software packages. The scaling of RDA is such a complicated
issue that it cannot be explained in this FAQ, but it is
explained in a separate pdf document on “Design decision and
implementation details in vegan” that you can read with vegan
command vegandocs("decision")
.
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This is not a vegan error message, but it comes from the
cca
function in the ade4 package. There is an unfortunate
name clash, and if you have loaded ade4 after vegan, the
ade4 version of cca
will mask the vegan version. You
can use the vegan version using command vegan::cca()
. If
you do not need package ade4, you can detach it with command
detach(package:ade4)
.
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Constrained ordination (cca
, rda
, capscale
) will
sometimes fail with error message
Error in La.svd(x, nu, nv): error code 1 from Lapack routine 'dgesdd'.
It seems that the basic problem is in the svd
function of
LAPACK
that is used for numerical analysis in R. LAPACK
is an external library that is beyond the control of package developers
and R core team so that these problems may be unsolvable. It seems that
the problems with the LAPACK
code are so common that even the
help page of svd
warns about them
Reducing the range of constraints (environmental variables) helps
sometimes. For instance, multiplying constraints by a constant < 1. This
rescaling does not influence the numerical results of constrained
ordination, but it can complicate further analyses when values of
constraints are needed, because the same scaling must be applied
there. We can only hope that this problem is fixed in the future
versions of R and LAPACK
.
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In general, 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:
rda
, cca
and capscale
give the variation
of conditional (partialled), constrained (canonical) and residual
components, but you must calculate the proportions by hand. Function
eigenvals
extracts the eigenvalues, and
summary(eigenvals(ord))
reports the proportions explained in the
result object ord
. Function RsquareAdj
gives the
R-squared and adjusted R-squared (if available) for constrained
components. Function goodness
gives the same statistics for
individual species or sites (species are unavailable with
capscale
). In addition, there is a special function
varpart
for unbiased partitioning of variance between up to four
separate components in redundancy analysis.
decorana
).
The total amount of variation is undefined in detrended correspondence
analysis, and therefore proportions from total are unknown and
undefined. DCA is not a method for decomposition of
variation, and therefore these proportions would not make sense either.
stressplot
displays the
nonlinear fit and gives this statistic.
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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 cca
, rda
and
capscale
can have Condition()
terms in their formula. The
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 adonis
evaluates terms sequentially. In a model with
right-hand-side ~ A + B
the effects of A
are evaluated
first, and the effects of B
after removing the effects of
A
. Sequential tests are also available in anova
function
for constrained ordination results by setting argument 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 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 permute package.
A major reason why real random effects models are impossible in most 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 vegan.
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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 predict
functions with
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 newdata
. The predict
functions are
available for basic eigenvector methods in vegan (cca
,
rda
, decorana
, for an up-to-date list, use command
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”: dune[3,] <- 0.001*dune[3,]
.
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You should define a class variable as an R factor
, and 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.
R (and vegan) knows both unordered and ordered factors. Unordered factors are internally coded as dummy variables, but one redundant level is removed or aliased. With default contrasts, the removed level is the first one. Ordered factors are expressed as polynomial contrasts. Both of these contrasts explained in standard R documentation.
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The printed output of envfit
gives the direction cosines which
are the coordinates of unit length arrows. For plotting, these are
scaled by their correlation (square roots of column r2
). You can
see the scaled lengths of envfit
arrows using command
scores
.
The scaled environmental vectors from envfit
and the arrows for
continuous environmental variables in constrained ordination
(cca
, rda
, capscale
) are adjusted to fill the
current graph. The lengths of arrows do not have fixed meaning with
respect to the points (species, sites), but they can only compared
against each other, and therefore only their relative lengths are
important.
If you want change the scaling of the arrows, you can use text
(plotting arrows and text) or points
(plotting only arrows)
functions for constrained ordination. These functions have argument
arrow.mul
which sets the multiplier. The plot
function
for envfit
also has the arrow.mul
argument to set the
arrow multiplier. If you save the invisible result of the constrained
ordination plot
command, you can see the value of the currently
used arrow.mul
which is saved as an attribute of biplot
scores.
An unexported function ordiArrowMul
is used to find the scaling
for the current plot. You can use this function to see how arrows would
be scaled:
sol <- cca(varespec) ef <- envfit(sol ~ ., varechem) plot(sol) vegan:::ordiArrowMul(scores(ef, display="vectors")) |
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vegan
uses standard R utilities for defining
contrasts. The default in standard installations is to use treatment
contrasts, but you can change the behaviour globally setting
options
or locally by using keyword contrasts
. Please
check the R help pages and user manuals for details.
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Aliased variable has no information because it can be expressed with the help of other variables. Such variables are automatically removed in constrained ordination in vegan. The aliased variables can be redundant levels of factors or whole variables.
Vegan function alias
gives the defining equations for aliased
variables. If you only want to see the names of aliased variables or
levels in solution sol
, use alias(sol, names.only=TRUE)
.
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You can fit vectors or class centroids for aliased variables using
envfit
function. The envfit
function uses weighted
fitting, and the fitted vectors are identical to the vectors in
correspondence analysis.
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You can constrain your permutations within strata
or levels of
factors. You can use stratified permutations in all vegan
functions that use permutation, such as adonis
, anosim
,
anova.cca
, mantel
, mrpp
, envfit
and
protest
.
Vegan will move to use permute package in all its
permutation tests, but currently this package is only used in
permutest.betadisper
. The permute package will allow
restricted permutation designs for time series, line transects, spatial
grids and blocking factors.
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The default ordination plot
function is intended for fast
plotting and it is not very configurable. To use different plotting
symbols, you should first create and empty ordination plot with
plot(..., type="n")
, and then add points
or text
to
the created empty frame (here ...
means other arguments you want
to give to your plot
command). The points
and text
commands are fully configurable, and allow different plotting symbols
and characters.
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If there is a really high number of species or sites, the graphs often are congested and many labels are overwritten. It may be impossible to have complete readable graphics with some data sets. However, here are some tricks you can use:
plot(..., type="n")
, if you are not satisfied with the default
graph. (Here and below ...
means other arguments you want
to give to your plot
command.)
identify
command if you do not need to see all labels.
ordilabel
which uses non-transparent
background to the text. The labels still shadow each other, but the
uppermost labels are readable. Argument priority
will help in
displaying the most interesting labels.
orditorp
function that uses labels only if these can be
added to a graph without overwriting other labels, and points otherwise,
if you do not need to see all labels. You must first create an empty
plot using plot(..., type="n")
, and then add labels or points
with orditorp
.
ordipointlabel
which uses points and text labels to the
points, and tries to optimize the location of the text to minimize the
overlap.
text
and points
functions have argument
select
that can be used for full control of selecting items
plotted as text or points.
orditkplot
function that lets you drag
labels of points to better positions if you need to see all labels. Only
one set of points can be used.
plot
functions allow you to zoom to a part of the
graph using xlim
and ylim
arguments to reduce clutter in
congested areas.
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Use xlim
or ylim
with flipped limits. If you have model
mod <- cca(dune)
you can flip the first axis with plot(mod,
xlim = c(3, -2))
.
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You can use xlim
and ylim
arguments in plot
or
ordiplot
to zoom into ordination diagrams. Normally you must set
both xlim
and ylim
because ordination plots will keep the
equal aspect ratio of axes, and they will fill the graph so that the
longer axis will fit.
Dynamic zooming can be done with function orditkplot
. You can
directly save the edited orditkplot
graph in various graphic
formats, or you can export the graph object back to R and use
plot
to display the results.
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3.1 Is there TWINSPAN? | ||
3.2 Why strata do not influence adonis results? | ||
3.3 How is deviance calculated? |
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No. It may be possible to port TWINSPAN to vegan, but it is not among the vegan top priorities. If anybody wants to try porting, I will be happy to help. TWINSPAN has a very permissive license, and it would be completely legal to port the function into R.
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Permutation happens only within strata
and this influences the
permutation distribution of the statistics and probably the significance
levels, but strata
do not influence the calculation of the
statistics.
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Some vegan functions, such as radfit
use base R facility of
family
in maximum likelihood estimation. This allows use of
several alternative error distributions, among them "poisson"
and "gaussian"
. The R family
also defines the
deviance. You can see the equations for deviance with commands like
poisson()$dev
or gaussian()$dev
.
In general, deviance is 2 times log.likelihood shifted so that models with exact fit have zero deviance.
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