Description Usage Arguments Details Value Note Author(s) See Also Examples

Functions to plot or extract results of constrained correspondence analysis
(`cca`

), redundancy analysis (`rda`

) or
constrained analysis of principal coordinates (`capscale`

).

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 | ```
## S3 method for class 'cca'
plot(x, choices = c(1, 2), display = c("sp", "wa", "cn"),
scaling = "species", type, xlim, ylim, const,
correlation = FALSE, hill = FALSE, ...)
## S3 method for class 'cca'
text(x, display = "sites", labels, choices = c(1, 2),
scaling = "species", arrow.mul, head.arrow = 0.05, select, const,
axis.bp = TRUE, correlation = FALSE, hill = FALSE, ...)
## S3 method for class 'cca'
points(x, display = "sites", choices = c(1, 2),
scaling = "species", arrow.mul, head.arrow = 0.05, select, const,
axis.bp = TRUE, correlation = FALSE, hill = FALSE, ...)
## S3 method for class 'cca'
scores(x, choices = c(1,2), display = c("sp","wa","cn"),
scaling = "species", hill = FALSE, ...)
## S3 method for class 'rda'
scores(x, choices = c(1,2), display = c("sp","wa","cn"),
scaling = "species", const, correlation = FALSE, ...)
## S3 method for class 'cca'
summary(object, scaling = "species", axes = 6,
display = c("sp", "wa", "lc", "bp", "cn"),
digits = max(3, getOption("digits") - 3),
correlation = FALSE, hill = FALSE, ...)
## S3 method for class 'summary.cca'
print(x, digits = x$digits, head = NA, tail = head, ...)
## S3 method for class 'summary.cca'
head(x, n = 6, tail = 0, ...)
## S3 method for class 'summary.cca'
tail(x, n = 6, head = 0, ...)
``` |

`x, object` |
A |

`choices` |
Axes shown. |

`display` |
Scores shown. These must include some of the
alternatives |

`scaling` |
Scaling for species and site scores. Either species
( The type of scores can also be specified as one of |

`correlation, hill` |
logical; if |

`type` |
Type of plot: partial match to |

`xlim, ylim` |
the x and y limits (min,max) of the plot. |

`labels` |
Optional text to be used instead of row names. |

`arrow.mul` |
Factor to expand arrows in the graph. Arrows will be scaled automatically to fit the graph if this is missing. |

`head.arrow` |
Default length of arrow heads. |

`select` |
Items to be displayed. This can either be a logical
vector which is |

`const` |
General scaling constant to |

`axis.bp` |
Draw |

`axes` |
Number of axes in summaries. |

`digits` |
Number of digits in output. |

`n, head, tail` |
Number of rows printed from the head and tail of
species and site scores. Default |

`...` |
Parameters passed to other functions. |

Same `plot`

function will be used for `cca`

and
`rda`

. This produces a quick, standard plot with current
`scaling`

.

The `plot`

function sets colours (`col`

), plotting
characters (`pch`

) and character sizes (`cex`

) to
certain standard values. For a fuller control of produced plot, it is
best to call `plot`

with `type="none"`

first, and then add
each plotting item separately using `text.cca`

or
`points.cca`

functions. These use the default settings of standard
`text`

and `points`

functions and accept all
their parameters, allowing a full user control of produced plots.

Environmental variables receive a special treatment. With
`display="bp"`

, arrows will be drawn. These are labelled with
`text`

and unlabelled with `points`

. The basic `plot`

function uses a simple (but not very clever) heuristics for adjusting
arrow lengths to plots, but the user can give the expansion factor in
`mul.arrow`

. With `display="cn"`

the centroids of levels of
`factor`

variables are displayed (these are available only if there were
factors and a formula interface was used in `cca`

or
`rda`

). With this option continuous
variables still are presented as arrows and ordered factors as arrows
and centroids.

If you want to have still a better control of plots, it is better to
produce them using primitive `plot`

commands. Function
`scores`

helps in extracting the
needed components with the selected `scaling`

.

Function `summary`

lists all scores and the output can be very
long. You can suppress scores by setting `axes = 0`

or
`display = NA`

or `display = NULL`

. You can display some
first or last (or both) rows of scores by using `head`

or
`tail`

or explicit `print`

command for the `summary`

.

Palmer (1993) suggested using linear constraints
(“LC scores”) in ordination diagrams, because these gave better
results in simulations and site scores (“WA scores”) are a step from
constrained to unconstrained analysis. However, McCune (1997) showed
that noisy environmental variables (and all environmental
measurements are noisy) destroy “LC scores” whereas “WA scores”
were little affected. Therefore the `plot`

function uses site
scores (“WA scores”) as the default. This is consistent with the
usage in statistics and other functions in **R**
(`lda`

, `cancor`

).

The `plot`

function returns invisibly a plotting structure which
can be used by function `identify.ordiplot`

to identify
the points or other functions in the `ordiplot`

family.

Package ade4 has function `cca`

which
returns constrained correspondence analysis of the same class as the
vegan function. If you have results of ade4 in your
working environment, vegan functions may try to handle them and
fail with cryptic error messages. However, there is a simple utility
function `ade2vegancca`

which tries to translate ade4
`cca`

results to vegan `cca`

results so that some
vegan functions may work partially with ade4 objects
(with a warning).

Jari Oksanen

`cca`

, `rda`

and `capscale`

for getting something
to plot, `ordiplot`

for an alternative plotting routine
and more support functions, and `text`

,
`points`

and `arrows`

for the basic routines.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
data(dune)
data(dune.env)
mod <- cca(dune ~ A1 + Moisture + Management, dune.env)
plot(mod, type="n")
text(mod, dis="cn")
points(mod, pch=21, col="red", bg="yellow", cex=1.2)
text(mod, "species", col="blue", cex=0.8)
## Limited output of 'summary'
head(summary(mod), tail=2)
## Scaling can be numeric or more user-friendly names
## e.g. Hill's scaling for (C)CA
scrs <- scores(mod, scaling = "sites", hill = TRUE)
## or correlation-based scores in PCA/RDA
scrs <- scores(rda(dune ~ A1 + Moisture + Management, dune.env),
scaling = "sites", correlation = TRUE)
``` |

```
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-3
Call:
cca(formula = dune ~ A1 + Moisture + Management, data = dune.env)
Partitioning of mean squared contingency coefficient:
Inertia Proportion
Total 2.1153 1.0000
Constrained 1.1392 0.5385
Unconstrained 0.9761 0.4615
Eigenvalues, and their contribution to the mean squared contingency coefficient
Importance of components:
CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
Eigenvalue 0.4483 0.3001 0.14995 0.10733 0.05668 0.04335 0.03345
Proportion Explained 0.2119 0.1419 0.07089 0.05074 0.02680 0.02050 0.01581
Cumulative Proportion 0.2119 0.3538 0.42470 0.47544 0.50223 0.52273 0.53855
CA1 CA2 CA3 CA4 CA5 CA6 CA7
Eigenvalue 0.3064 0.13191 0.11516 0.10947 0.07724 0.07575 0.04871
Proportion Explained 0.1448 0.06236 0.05444 0.05175 0.03652 0.03581 0.02303
Cumulative Proportion 0.6834 0.74574 0.80018 0.85194 0.88845 0.92427 0.94730
CA8 CA9 CA10 CA11 CA12
Eigenvalue 0.03758 0.03106 0.02102 0.01254 0.009277
Proportion Explained 0.01777 0.01468 0.00994 0.00593 0.004390
Cumulative Proportion 0.96506 0.97975 0.98968 0.99561 1.000000
Accumulated constrained eigenvalues
Importance of components:
CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
Eigenvalue 0.4483 0.3001 0.1499 0.10733 0.05668 0.04335 0.03345
Proportion Explained 0.3935 0.2635 0.1316 0.09422 0.04976 0.03806 0.02937
Cumulative Proportion 0.3935 0.6570 0.7886 0.88282 0.93258 0.97063 1.00000
Scaling 2 for species and site scores
* Species are scaled proportional to eigenvalues
* Sites are unscaled: weighted dispersion equal on all dimensions
Species scores
CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
Achimill 0.8150 0.4375 -0.11236 0.35595 -0.114763 -0.01972
Agrostol -0.7488 -0.4783 0.03561 0.17039 0.187389 0.23471
Airaprae -0.8186 1.7469 1.04506 -0.28593 0.191836 0.73077
Alopgeni -0.3442 -1.0216 0.37620 0.02296 -0.004041 0.04789
Anthodor 0.3367 0.7694 -0.07602 -0.05421 0.136354 0.42463
Bellpere 0.6535 0.2200 0.03438 0.60436 -0.090469 0.28138
....
Bracruta -0.1309 0.2009 -0.03708 -0.17421 -0.109657 0.04381
Callcusp -1.5181 0.3834 -0.23255 0.15246 0.104239 -0.11424
Site scores (weighted averages of species scores)
CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
1 1.2468 -0.4017 0.91955 0.7292 1.5785 -1.0196
2 0.8622 -0.1641 0.25789 1.7240 -0.7592 -0.6479
3 0.3165 -0.9785 0.82952 0.7451 0.6556 0.3256
4 0.2405 -0.8699 1.07861 1.4103 1.1164 2.4714
5 1.1362 0.2621 -1.10847 -0.9417 0.5630 1.1495
6 1.0575 0.4041 -1.65035 -1.8483 1.0287 -0.1690
....
19 -0.7913 2.7451 2.93017 -1.3851 -0.3932 1.7277
20 -2.0770 1.0113 -0.02581 -0.8949 1.6406 -1.7917
Site constraints (linear combinations of constraining variables)
CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
1 0.7245 -0.3695 1.25652 -0.3678 0.9827 -0.60590
2 0.9033 0.4250 0.03901 1.0557 -1.0860 -1.61234
3 0.4493 -0.6694 0.67765 0.8695 0.9609 1.52307
4 0.4550 -0.6532 0.72768 0.8529 0.9795 1.50218
5 0.9671 -0.2010 -1.93972 -0.5807 0.2582 0.31905
6 1.0805 0.1235 -0.93911 -0.9126 0.6307 -0.09863
....
19 -1.4581 1.6074 1.16812 -0.5305 0.3178 -0.40336
20 -1.4468 1.6399 1.26818 -0.5637 0.3551 -0.44513
Biplot scores for constraining variables
CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
A1 -0.5554 -0.1617 -0.67982 0.10708 -0.17998 0.30507
Moisture.L -0.9437 -0.1638 0.07974 -0.02238 0.03067 -0.02368
Moisture.Q -0.1876 0.3571 -0.45352 -0.17237 0.28350 -0.63025
Moisture.C -0.2069 0.1732 0.10635 0.68203 0.50123 0.35887
ManagementHF 0.3645 -0.1171 -0.42202 -0.67746 0.17212 -0.12317
ManagementNM -0.5855 0.7267 -0.01115 -0.09642 -0.11445 0.27037
ManagementSF -0.1511 -0.6957 0.38543 0.24770 0.29469 0.23829
Centroids for factor constraints
CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
Moisture1 0.9119 0.35388 -0.40013 -0.26218 0.02084 -0.4708
Moisture2 0.5015 -0.06706 0.60222 1.12478 0.33942 1.2024
Moisture4 -0.1522 -1.35873 0.76544 -1.37289 -1.80794 0.3849
Moisture5 -1.3394 0.11972 -0.20942 0.04843 0.39751 -0.3902
ManagementBF 0.8376 0.41614 0.13885 1.40679 -0.97766 -0.9604
ManagementHF 0.5426 -0.17426 -0.62822 -1.00848 0.25622 -0.1834
ManagementNM -1.1010 1.36665 -0.02097 -0.18131 -0.21523 0.5084
ManagementSF -0.2320 -1.06831 0.59183 0.38035 0.45250 0.3659
```

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