plot.cca: Plot or Extract Results of Constrained Correspondence...

In vegan: Community Ecology Package

Description

Functions to plot or extract results of constrained correspondence analysis (cca), redundancy analysis (rda) or constrained analysis of principal coordinates (capscale).

Usage

## 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, ...)

Arguments

x, object	A cca result object.
choices	Axes shown.
display	Scores shown. These must include some of the alternatives species or sp for species scores, sites or wa for site scores, lc for linear constraints or “LC scores”, or bp for biplot arrows or cn for centroids of factor constraints instead of an arrow.
scaling	Scaling for species and site scores. Either species (2) or site (1) scores are scaled by eigenvalues, and the other set of scores is left unscaled, or with 3 both are scaled symmetrically by square root of eigenvalues. Corresponding negative values can be used in cca to additionally multiply results with √(1/(1-λ)). This scaling is know as Hill scaling (although it has nothing to do with Hill's rescaling of decorana). With corresponding negative values inrda, species scores are divided by standard deviation of each species and multiplied with an equalizing constant. Unscaled raw scores stored in the result can be accessed with scaling = 0. The type of scores can also be specified as one of "none", "sites", "species", or "symmetric", which correspond to the values 0, 1, 2, and 3 respectively. Arguments correlation and hill in scores.rda and scores.cca respectively can be used in combination with these character descriptions to get the corresponding negative value.
correlation, hill	logical; if scaling is a character description of the scaling type, correlation or hill are used to select the corresponding negative scaling type; either correlation-like scores or Hill's scaling for PCA/RDA and CA/CCA respectively. See argument scaling for details.
type	Type of plot: partial match to text for text labels, points for points, and none for setting frames only. If omitted, text is selected for smaller data sets, and points for larger.
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 TRUE for displayed items or a vector of indices of displayed items.
const	General scaling constant to rda scores. The default is to use a constant that gives biplot scores, that is, scores that approximate original data (see vignette on ‘Design Decisions’ with browseVignettes("vegan") for details and discussion). If const is a vector of two items, the first is used for species, and the second item for site scores.
axis.bp	Draw axis for biplot arrows.
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 NA prints all.
...	Parameters passed to other functions.

Details

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).

Value

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.

Note

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).

Author(s)

Jari Oksanen

See Also

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.

Examples

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)

Example output

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

vegan documentation built on May 2, 2019, 5:51 p.m.