vegan-package: Community Ecology Package: Ordination, Diversity and...
In vegan: Community Ecology Package
Description
Details
Author(s)
Examples
Description
The vegan package provides tools for descriptive
community ecology. It has most basic functions of diversity analysis,
community ordination and dissimilarity analysis. Most of its
multivariate tools can be used for other data types as well.
Details
The functions in the vegan package contain tools for
diversity analysis, ordination methods and tools for the analysis of
dissimilarities. Together with the labdsv package, the vegan
package provides most standard tools of descriptive community
analysis. Package ade4 provides an alternative comprehensive
package, and several other packages complement vegan and provide
tools for deeper analysis in specific fields. Package
BiodiversityR provides a GUI for a large subset of vegan
functionality.
The vegan package is developed at GitHub
(https://github.com/vegandevs/vegan/). GitHub provides up-to-date
information and forums for bug reports.
Most important changes in vegan documents can be read with
news(package="vegan") and vignettes can be browsed with
browseVignettes("vegan"). The vignettes include a vegan
FAQ, discussion on design decisions, short introduction to ordination
and discussion on diversity methods. A tutorial of the package at
http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf provides
a more thorough introduction to the package.
To see the preferable citation of the package, type
citation("vegan").
Author(s)
The vegan development team is Jari Oksanen,
F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter
R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos, M. Henry
H. Stevens, Helene Wagner. Many other people have contributed to
individual functions: see credits in function help pages.
Examples
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### Example 1: Unconstrained ordination
## NMDS
data(varespec)
data(varechem)
ord <- metaMDS(varespec)
plot(ord, type = "t")
## Fit environmental variables
ef <- envfit(ord, varechem)
ef
plot(ef, p.max = 0.05)
### Example 2: Constrained ordination (RDA)
## The example uses formula interface to define the model
data(dune)
data(dune.env)
## No constraints: PCA
mod0 <- rda(dune ~ 1, dune.env)
mod0
plot(mod0)
## All environmental variables: Full model
mod1 <- rda(dune ~ ., dune.env)
mod1
plot(mod1)
## Automatic selection of variables by permutation P-values
mod <- ordistep(mod0, scope=formula(mod1))
mod
plot(mod)
## Permutation test for all variables
anova(mod)
## Permutation test of "type III" effects, or significance when a term
## is added to the model after all other terms
anova(mod, by = "margin")
## Plot only sample plots, use different symbols and draw SD ellipses
## for Managemenet classes
plot(mod, display = "sites", type = "n")
with(dune.env, points(mod, disp = "si", pch = as.numeric(Management)))
with(dune.env, legend("topleft", levels(Management), pch = 1:4,
title = "Management"))
with(dune.env, ordiellipse(mod, Management, label = TRUE))
## add fitted surface of diversity to the model
ordisurf(mod, diversity(dune), add = TRUE)
### Example 3: analysis of dissimilarites a.k.a. non-parametric
### permutational anova
adonis(dune ~ ., dune.env)
adonis(dune ~ Management + Moisture, dune.env)
Example output
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-3
Square root transformation
Wisconsin double standardization
Run 0 stress 0.1843196
Run 1 stress 0.18458
... Procrustes: rmse 0.04943018 max resid 0.1579102
Run 2 stress 0.234577
Run 3 stress 0.1962451
Run 4 stress 0.21377
Run 5 stress 0.20931
Run 6 stress 0.1852397
Run 7 stress 0.2109611
Run 8 stress 0.2143689
Run 9 stress 0.2499291
Run 10 stress 0.2096851
Run 11 stress 0.1985584
Run 12 stress 0.2112259
Run 13 stress 0.2061122
Run 14 stress 0.2253398
Run 15 stress 0.2075297
Run 16 stress 0.2101147
Run 17 stress 0.2459537
Run 18 stress 0.2114468
Run 19 stress 0.1852397
Run 20 stress 0.2363955
*** No convergence -- monoMDS stopping criteria:
20: stress ratio > sratmax
***VECTORS
NMDS1 NMDS2 r2 Pr(>r)
N -0.05038 -0.99873 0.2080 0.085 .
P 0.68719 0.72647 0.1755 0.115
K 0.82745 0.56155 0.1657 0.159
Ca 0.75024 0.66116 0.2809 0.029 *
Mg 0.69691 0.71716 0.3492 0.016 *
S 0.27645 0.96103 0.1774 0.118
Al -0.83757 0.54633 0.5155 0.001 ***
Fe -0.86169 0.50743 0.3999 0.004 **
Mn 0.80219 -0.59707 0.5323 0.003 **
Zn 0.66537 0.74651 0.1779 0.124
Mo -0.84867 0.52892 0.0517 0.582
Baresoil 0.87189 -0.48971 0.2494 0.052 .
Humdepth 0.92623 -0.37696 0.5590 0.001 ***
pH -0.79900 0.60133 0.2625 0.038 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Permutation: free
Number of permutations: 999
Call: rda(formula = dune ~ 1, data = dune.env)
Inertia Rank
Total 84.12
Unconstrained 84.12 19
Inertia is variance
Eigenvalues for unconstrained axes:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
24.795 18.147 7.629 7.153 5.695 4.333 3.199 2.782
(Showed only 8 of all 19 unconstrained eigenvalues)
Call: rda(formula = dune ~ A1 + Moisture + Management + Use + Manure,
data = dune.env)
Inertia Proportion Rank
Total 84.1237 1.0000
Constrained 63.2062 0.7513 12
Unconstrained 20.9175 0.2487 7
Inertia is variance
Some constraints were aliased because they were collinear (redundant)
Eigenvalues for constrained axes:
RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8 RDA9 RDA10 RDA11
22.396 16.208 7.039 4.038 3.760 2.609 2.167 1.803 1.404 0.917 0.582
RDA12
0.284
Eigenvalues for unconstrained axes:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
6.627 4.309 3.549 2.546 2.340 0.934 0.612
Start: dune ~ 1
Df AIC F Pr(>F)
+ Management 3 87.082 2.8400 0.005 **
+ Moisture 3 87.707 2.5883 0.005 **
+ Manure 4 89.232 1.9539 0.005 **
+ A1 1 89.591 1.9217 0.060 .
+ Use 2 91.032 1.1741 0.305
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Step: dune ~ Management
Df AIC F Pr(>F)
- Management 3 89.62 2.84 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
+ Moisture 3 85.567 1.9764 0.005 **
+ Manure 3 87.517 1.3902 0.100 .
+ A1 1 87.424 1.2965 0.180
+ Use 2 88.284 1.0510 0.335
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Step: dune ~ Management + Moisture
Df AIC F Pr(>F)
- Moisture 3 87.082 1.9764 0.010 **
- Management 3 87.707 2.1769 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
+ Manure 3 85.762 1.1225 0.320
+ A1 1 86.220 0.8359 0.625
+ Use 2 86.842 0.8027 0.760
Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
Inertia Proportion Rank
Total 84.1237 1.0000
Constrained 46.4249 0.5519 6
Unconstrained 37.6988 0.4481 13
Inertia is variance
Eigenvalues for constrained axes:
RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
21.588 14.075 4.123 3.163 2.369 1.107
Eigenvalues for unconstrained axes:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13
8.241 7.138 5.355 4.409 3.143 2.770 1.878 1.741 0.952 0.909 0.627 0.311 0.227
Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999
Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
Df Variance F Pr(>F)
Model 6 46.425 2.6682 0.001 ***
Residual 13 37.699
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Permutation test for rda under reduced model
Marginal effects of terms
Permutation: free
Number of permutations: 999
Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
Df Variance F Pr(>F)
Management 3 18.938 2.1769 0.007 **
Moisture 3 17.194 1.9764 0.011 *
Residual 13 37.699
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
Estimated degrees of freedom:
1.28 total = 2.28
REML score: 3.00623
Call:
adonis(formula = dune ~ ., data = dune.env)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
A1 1 0.7230 0.72295 5.2038 0.16817 0.002 **
Moisture 3 1.1871 0.39569 2.8482 0.27613 0.006 **
Management 3 0.9036 0.30121 2.1681 0.21019 0.027 *
Use 2 0.0921 0.04606 0.3315 0.02143 0.980
Manure 3 0.4208 0.14026 1.0096 0.09787 0.482
Residuals 7 0.9725 0.13893 0.22621
Total 19 4.2990 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Call:
adonis(formula = dune ~ Management + Moisture, data = dune.env)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Management 3 1.4686 0.48953 3.7907 0.34161 0.001 ***
Moisture 3 1.1516 0.38387 2.9726 0.26788 0.001 ***
Residuals 13 1.6788 0.12914 0.39051
Total 19 4.2990 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
vegan documentation built on May 2, 2019, 5:51 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) Examples
Description
The vegan package provides tools for descriptive community ecology. It has most basic functions of diversity analysis, community ordination and dissimilarity analysis. Most of its multivariate tools can be used for other data types as well.
Details
The functions in the vegan package contain tools for diversity analysis, ordination methods and tools for the analysis of dissimilarities. Together with the labdsv package, the vegan package provides most standard tools of descriptive community analysis. Package ade4 provides an alternative comprehensive package, and several other packages complement vegan and provide tools for deeper analysis in specific fields. Package BiodiversityR provides a GUI for a large subset of vegan functionality.
The vegan package is developed at GitHub (https://github.com/vegandevs/vegan/). GitHub provides up-to-date information and forums for bug reports.
Most important changes in vegan documents can be read with
news(package="vegan") and vignettes can be browsed with
browseVignettes("vegan"). The vignettes include a vegan
FAQ, discussion on design decisions, short introduction to ordination
and discussion on diversity methods. A tutorial of the package at
http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf provides
a more thorough introduction to the package.
To see the preferable citation of the package, type
citation("vegan").
Author(s)
The vegan development team is Jari Oksanen, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos, M. Henry H. Stevens, Helene Wagner. Many other people have contributed to individual functions: see credits in function help pages.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ### Example 1: Unconstrained ordination
## NMDS
data(varespec)
data(varechem)
ord <- metaMDS(varespec)
plot(ord, type = "t")
## Fit environmental variables
ef <- envfit(ord, varechem)
ef
plot(ef, p.max = 0.05)
### Example 2: Constrained ordination (RDA)
## The example uses formula interface to define the model
data(dune)
data(dune.env)
## No constraints: PCA
mod0 <- rda(dune ~ 1, dune.env)
mod0
plot(mod0)
## All environmental variables: Full model
mod1 <- rda(dune ~ ., dune.env)
mod1
plot(mod1)
## Automatic selection of variables by permutation P-values
mod <- ordistep(mod0, scope=formula(mod1))
mod
plot(mod)
## Permutation test for all variables
anova(mod)
## Permutation test of "type III" effects, or significance when a term
## is added to the model after all other terms
anova(mod, by = "margin")
## Plot only sample plots, use different symbols and draw SD ellipses
## for Managemenet classes
plot(mod, display = "sites", type = "n")
with(dune.env, points(mod, disp = "si", pch = as.numeric(Management)))
with(dune.env, legend("topleft", levels(Management), pch = 1:4,
title = "Management"))
with(dune.env, ordiellipse(mod, Management, label = TRUE))
## add fitted surface of diversity to the model
ordisurf(mod, diversity(dune), add = TRUE)
### Example 3: analysis of dissimilarites a.k.a. non-parametric
### permutational anova
adonis(dune ~ ., dune.env)
adonis(dune ~ Management + Moisture, dune.env)
|
Example output
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-3
Square root transformation
Wisconsin double standardization
Run 0 stress 0.1843196
Run 1 stress 0.18458
... Procrustes: rmse 0.04943018 max resid 0.1579102
Run 2 stress 0.234577
Run 3 stress 0.1962451
Run 4 stress 0.21377
Run 5 stress 0.20931
Run 6 stress 0.1852397
Run 7 stress 0.2109611
Run 8 stress 0.2143689
Run 9 stress 0.2499291
Run 10 stress 0.2096851
Run 11 stress 0.1985584
Run 12 stress 0.2112259
Run 13 stress 0.2061122
Run 14 stress 0.2253398
Run 15 stress 0.2075297
Run 16 stress 0.2101147
Run 17 stress 0.2459537
Run 18 stress 0.2114468
Run 19 stress 0.1852397
Run 20 stress 0.2363955
*** No convergence -- monoMDS stopping criteria:
20: stress ratio > sratmax
***VECTORS
NMDS1 NMDS2 r2 Pr(>r)
N -0.05038 -0.99873 0.2080 0.085 .
P 0.68719 0.72647 0.1755 0.115
K 0.82745 0.56155 0.1657 0.159
Ca 0.75024 0.66116 0.2809 0.029 *
Mg 0.69691 0.71716 0.3492 0.016 *
S 0.27645 0.96103 0.1774 0.118
Al -0.83757 0.54633 0.5155 0.001 ***
Fe -0.86169 0.50743 0.3999 0.004 **
Mn 0.80219 -0.59707 0.5323 0.003 **
Zn 0.66537 0.74651 0.1779 0.124
Mo -0.84867 0.52892 0.0517 0.582
Baresoil 0.87189 -0.48971 0.2494 0.052 .
Humdepth 0.92623 -0.37696 0.5590 0.001 ***
pH -0.79900 0.60133 0.2625 0.038 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Permutation: free
Number of permutations: 999
Call: rda(formula = dune ~ 1, data = dune.env)
Inertia Rank
Total 84.12
Unconstrained 84.12 19
Inertia is variance
Eigenvalues for unconstrained axes:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
24.795 18.147 7.629 7.153 5.695 4.333 3.199 2.782
(Showed only 8 of all 19 unconstrained eigenvalues)
Call: rda(formula = dune ~ A1 + Moisture + Management + Use + Manure,
data = dune.env)
Inertia Proportion Rank
Total 84.1237 1.0000
Constrained 63.2062 0.7513 12
Unconstrained 20.9175 0.2487 7
Inertia is variance
Some constraints were aliased because they were collinear (redundant)
Eigenvalues for constrained axes:
RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8 RDA9 RDA10 RDA11
22.396 16.208 7.039 4.038 3.760 2.609 2.167 1.803 1.404 0.917 0.582
RDA12
0.284
Eigenvalues for unconstrained axes:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
6.627 4.309 3.549 2.546 2.340 0.934 0.612
Start: dune ~ 1
Df AIC F Pr(>F)
+ Management 3 87.082 2.8400 0.005 **
+ Moisture 3 87.707 2.5883 0.005 **
+ Manure 4 89.232 1.9539 0.005 **
+ A1 1 89.591 1.9217 0.060 .
+ Use 2 91.032 1.1741 0.305
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Step: dune ~ Management
Df AIC F Pr(>F)
- Management 3 89.62 2.84 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
+ Moisture 3 85.567 1.9764 0.005 **
+ Manure 3 87.517 1.3902 0.100 .
+ A1 1 87.424 1.2965 0.180
+ Use 2 88.284 1.0510 0.335
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Step: dune ~ Management + Moisture
Df AIC F Pr(>F)
- Moisture 3 87.082 1.9764 0.010 **
- Management 3 87.707 2.1769 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
+ Manure 3 85.762 1.1225 0.320
+ A1 1 86.220 0.8359 0.625
+ Use 2 86.842 0.8027 0.760
Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
Inertia Proportion Rank
Total 84.1237 1.0000
Constrained 46.4249 0.5519 6
Unconstrained 37.6988 0.4481 13
Inertia is variance
Eigenvalues for constrained axes:
RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
21.588 14.075 4.123 3.163 2.369 1.107
Eigenvalues for unconstrained axes:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13
8.241 7.138 5.355 4.409 3.143 2.770 1.878 1.741 0.952 0.909 0.627 0.311 0.227
Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999
Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
Df Variance F Pr(>F)
Model 6 46.425 2.6682 0.001 ***
Residual 13 37.699
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Permutation test for rda under reduced model
Marginal effects of terms
Permutation: free
Number of permutations: 999
Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
Df Variance F Pr(>F)
Management 3 18.938 2.1769 0.007 **
Moisture 3 17.194 1.9764 0.011 *
Residual 13 37.699
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
Estimated degrees of freedom:
1.28 total = 2.28
REML score: 3.00623
Call:
adonis(formula = dune ~ ., data = dune.env)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
A1 1 0.7230 0.72295 5.2038 0.16817 0.002 **
Moisture 3 1.1871 0.39569 2.8482 0.27613 0.006 **
Management 3 0.9036 0.30121 2.1681 0.21019 0.027 *
Use 2 0.0921 0.04606 0.3315 0.02143 0.980
Manure 3 0.4208 0.14026 1.0096 0.09787 0.482
Residuals 7 0.9725 0.13893 0.22621
Total 19 4.2990 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Call:
adonis(formula = dune ~ Management + Moisture, data = dune.env)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Management 3 1.4686 0.48953 3.7907 0.34161 0.001 ***
Moisture 3 1.1516 0.38387 2.9726 0.26788 0.001 ***
Residuals 13 1.6788 0.12914 0.39051
Total 19 4.2990 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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