vegan (version 1.6-0)

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

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 = 2, type, ...)
## S3 method for class 'cca':
text(x, display = "sites", choices = c(1, 2), scaling = 2,
    mul.arrow = 1, head.arrow = 0.05, ...)
## S3 method for class 'cca':
points(x, display = "sites", choices = c(1, 2), scaling = 2,
    mul.arrow = 1, head.arrow = 0.05, ...)
## S3 method for class 'cca':
scores(x, choices=c(1,2), display=c("sp","wa","cn"),scaling=2, ...)

Arguments

x

A cca result object.

choices

Axes shown.

display

Scores shown. These must some of the alternatives sp for species scores, wa for site scores, lc for linear constraints or ``LC scores'', or bp for biplot arrows or cn for centro

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 larg

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 o

mul.arrow

Factor to expand arrows to fit the graph.

head.arrow

Default length of arrow heads.

...

Other parameters for plotting functions.

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.

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 thus 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 with points.cca and text.cca the user must give the expansion factor in mul.arrow. The behaviour is still more peculiar with display="cn" which requests centroids of levels of factor variables (these are available only if there were factors and a formula interface was used in cca or rda). With this option, biplot arrows are plotted in addition to centroids in cases which do not have a centroid: Continuous variables are presented with arrows and ordered factors with 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.

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", mul=2)
points(mod, pch=21, col="red", bg="yellow", cex=1.2)
text(mod, "species", col="blue", cex=0.8)