This document contains answers to some of the most frequently asked questions about R package vegan.
This work is licensed under the Creative Commons Attribution 3.0 License.
Copyright © 2008-2025 vegan development team
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 distributed under GPL2 license.
Both R and latest release version of vegan can be obtained through CRAN. Unstable development version of vegan can be obtained through GitHub. The github page gives further instructions for obtaining and installing development versions of vegan.
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.
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 browseVignettes("vegan")
command. The documents included in the vegan package are
NEWS that can be accessed via news() command.FAQ-vegan).intro-vegan).diversity-vegan).decision-vegan).varpart
(partitioning).Web documents outside the package include:
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.
Use command citation("vegan") in R to see the recommended citation to
be used in publications.
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).
Binaries can be available from R Universe: see https://github.com/vegandevs/vegan for instructions.
If you think you have found a bug in vegan, you should report it to vegan maintainers or developers. The preferred forum to report bugs is GitHub. 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.
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.
Vegan is dependent on user contribution. All feedback is welcome. If you have problems with vegan, it may be as simple as incomplete documentation, and we shall 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, or if you submit code.
If you can write code yourself, the best forum to contribute to vegan is GitHub.
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. See
also the next question.
Yes. In most cases they behave like normal data frames. For environmental data there should be no problem with tibbles. Problems can emerge with community data. Community data frames must be numeric, but if tibble has character variables (such as a column of row names), data are non-numeric with guaranteed failure in ordination (see previous question). All character columns should be removed from community data in ordination.
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.
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.
metaMDSThe first (try 0) run of metaMDS starts from the metric scaling
solution and is usually good, and most sofware only return that
solution. However, metaMDS tries to see if that standard solution
can be repeated, or improved and the improved solution still
repeated. In all cases, it will return the best solution found, and
there is no burning need to do anything if you get the message that the
solution could not be repeated. If you are keen to know that the
solution really is the global optimum, you may follow the instructions
in the metaMDS help section “Results Could Not Be Repeated” and try
harder.
metaMDSMost common reason is that you have too few observations for your
NMDS. For \(n\) observations (points) and \(k\) dimensions you need to
estimate \(nk\) parameters (ordination scores) using \(n(n-1)/2\)
dissimilarities. For \(k\) dimensions you must have \(n > 2k + 1\), 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 \(n > 4k + 1\) points for \(k\) dimensions. A
typical symptom of insufficient data is that you have (nearly) zero
stress but no repeated 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 (pco and wcmdscale in
vegan, cmdscale in base R).
Function metaMDS uses function monoMDS as its default method for
NMDS, and this function can handle zero dissimilarities. Alternative
function isoMDS 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 positive 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.
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 there is non-metric relation from input
dissimilarities to the Euclidean ordination space. You can inspect
this non-linear step function 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.
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.
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 command browseVignettes("vegan").
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 non-standardized 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:
Eigenvector methods: Functions rda, cca, dbrda and capscale
give the variation of conditional (partialled), constrained
(canonical) and residual components. Function eigenvals extracts
the eigenvalues, and summary(eigenvals(ord)) reports the
proportions explained in the result object ord, and also works
with decorana and wcmdscale. 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. In addition, there is a special
function varpart for unbiased partitioning of variance between up
to four separate components.
Nonmetric multidimensional scaling. 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
stressplot displays the nonlinear fit and gives this statistic.
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 dbrda can have
Condition() terms in their formula. Condition() defines 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 adonis2 can evaluate 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.
All permutation tests in vegan are based on the permute package
that allows constructing various restricted permutation schemes. For
instance, you can set levels of plots or blocks for a factor
regarded as a random term.
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.
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 can add new points NMDS with function MDSaddpoints.
You should define a class variable as an R factor, and vegan will
automatically handle them.
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 are explained in standard R documentation.
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, dbrda) 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 to 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.
You have some variables that have no use and information. These
variables are correlated with other constraining variables and have no
independent information. This can also concern only some levels of
factor variables. For instance, in vegan data dune.env we know
that Manure level is 0 in Management level NM and only there. If
both are used as constraints (or conditions), one level of Manure or
one level of Management is redundant. These variables or levels are
aliased, and vegan informs about this during fitting the model and
permanently when the short information of the result is printed.
Vegan function alias gives the defining equations for aliased
variables, or optionally their names.
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. In constrained analysis you must fit the centroids or
vectors to the constrained scores (LC scores), and use argument
display = "lc" in envfit.
Vegan uses permute package in all its permutation tests. The
permute package will allow restricted permutation designs for time
series, line transects, spatial grids and blocking factors. The
construction of restricted permutation schemes is explained in the
manual page permutations in vegan and in the documentation of the
permute package.
The permutation scheme influences the permutation distribution of the statistics and significance levels, but does not influence the calculation of the statistics.
Ordination objects typically have many different kind of scores, and
there may be limitations of changing the way how they are
displayed. However, plot for constrained ordination results (cca,
rda, dbrda) can be fully configured since vegan 2.7-1. See
their help. Sometimes it is easier to compose the graph by layers
using pipes (|>) to add text or points of different scores. See
the help of ordination plot functions for examples.
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. Below we give a brief overview of tricks you can use. Gavin Simpson’s blog From the bottom of the heap has a series of articles on “decluttering ordination plots” with more detailed discussion and examples.
Often the easiest solution is to use only points. If you need text
to identify items, most text and plot functions have argument
optimize and bg. With optimize = TRUE the exact location of the
score is marked with a point, and text label is written next to the
point trying to find a position that minimizes over-writing other
labels. This is impossible in very congested areas, but often helps in
moderate cases. Argument bg defines a background colour of
labels. For instance, bg = "white" will write text on white
background. These labels will cover other text below them, but at
least the uppermost are readable instead of both being messed
up. These arguments can be combined, and this often gives the best
result. These arguments can also be used in text functions in pipe
(|>). Finally, text functions take argument labels which allows
editing plotted text, and argument select to drop completely some
cases.
There is a special function orditorp (torp for text-or-point)
function that will use text only if it is not covered by other text
labels and points otherwise (with argument priority to define which
items are printed first).
Companion CRAN package vegan3d has function orditkplot that
provides editable plot where the points are fixed at their ordination
scores, but you can move text labels with mouse.
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)).
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 in CRAN package
vegan3d. You can directly save the edited orditkplot graph in
various graphic formats, or you can export the graph object back to R
session and use plot to display the results.
TWINSPAN for R is available in github.