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In vegan: Community Ecology Package

vegan FAQ

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. To view a copy of this license, visit https://creativecommons.org/licenses/by/3.0/ or send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA.

Copyright © 2008-2016 vegan development team

Introduction

What is vegan?

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.

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 https://www.R-project.org/. It is free software distributed under a GNU-style copyleft, and an official part of the GNU project (“GNU S”).

How to obtain vegan and R?

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.

What R packages vegan depends on?

Vegan depends on the permute package which will provide advanced and flexible permutation routines for vegan. The permute package is developed together with vegan in GitHub.

Some individual vegan functions depend on packages MASS, mgcv, parallel, cluster and lattice. Vegan dependence on tcltk is deprecated and will be removed in future releases. These all are base or recommended R packages that should be available in every R installation. Vegan declares these as suggested or imported packages, and you can install vegan and use most of its functions without these packages.

Vegan is accompanied with a supporting package vegan3d for three-dimensional and dynamic plotting. The vegan3d package needs tcltk and non-standard packages rgl and scatterplot3d.

What other packages are available for ecologists?

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.

What other documentation is available for vegan?

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

Web documents outside the package include:

Is there a Graphical User Interface (GUI) for vegan?

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.

How to cite vegan?

Use command citation("vegan") in R to see the recommended citation to be used in publications.

How to build vegan from sources?

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).

Are there binaries for devel versions?

Binaries can be available from R Universe: see https://github.com/vegandevs/vegan for instructions.

How to report a bug in vegan?

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.

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 vegdist always calculates quantitative indices (when this is possible). If you expect it to calculate a binary index, you should use argument binary = TRUE.

Can I contribute to vegan?

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.

Ordination

I have only numeric and positive data but 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 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.

Can I analyse binary or cover class data?

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.

Why dissimilarities in vegan differ from other sources?

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.

Why 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 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.

I get zero stress but no repeated solutions in metaMDS

The 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 tha 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 more.

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).

Zero dissimilarities in isoMDS

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.

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. 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.

Where can I find numerical scores of ordination axes?

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.

How the RDA results are scaled?

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").

I cannot print and plot RDA results properly

If the RDA ordination results have a weird format or you cannot plot them properly, you probably have a name clash with klaR package which also has function rda, and the klaR print, plot or predict functions are used for vegan RDA results. You can choose between rda functions using vegan::rda() or klaR::rda(): you will get obscure error messages if you use the wrong function. In general, vegan should be able to work normally if vegan was loaded after klaR, but if klaR was loaded later, its functions will take precedence over vegan. Sometimes vegan namespace is loaded automatically when restoring a previously stored workspace at the start-up, and then klaR methods will always take precedence over vegan. You should check your loaded packages. klaR may be also loaded indirectly via other packages (in the reported cases it was most often loaded via agricolae package). Vegan and klaR both have the same function name (rda), and it may not be possible to use these packages simultaneously, and the safest choice is to unload one of the packages if only possible. See discussion in vegan github issues.

Ordination fails with “Error in La.svd”

Constrained ordination (cca, rda, dbrda, 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.

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. The reports on the problems are getting rare and it may that this problem is fixed in R and LAPACK.

Variance explained by ordination axes.

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:

Can I have random effects in constrained ordination or in 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. 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 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.

Is it possible to have passive points in ordination?

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")).

Class variables and dummies

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 explained in standard R documentation.

How are environmental arrows scaled?

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 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.

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)
ordiArrowMul(scores(ef, display="vectors"))

I want to use Helmert or sum contrasts

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.

What are aliased variables and how to see them?

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).

Plotting aliased variables

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.

Restricted permutations in vegan

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.

How to use different plotting symbols in ordination graphics?

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.

How to avoid cluttered ordination graphs?

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.

Can I flip an axis in ordination diagram?

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)).

Can I zoom into an ordination plot?

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.

Other analysis methods

Is there TWINSPAN?

TWINSPAN for R is available in github.

Why restricted permutation does not influence adonis results?

The permutation scheme influences the permutation distribution of the statistics and probably the significance levels, but does not influence the calculation of the statistics.

How is deviance calculated?

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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vegan documentation built on Sept. 11, 2024, 7:57 p.m.

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