vegan (version 2.0-10)

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, xlim, ylim, const, ...)
## S3 method for class 'cca':
text(x, display = "sites", labels, choices = c(1, 2), scaling = 2,
    arrow.mul, head.arrow = 0.05, select, const, axis.bp = TRUE, ...)
## S3 method for class 'cca':
points(x, display = "sites", choices = c(1, 2), scaling = 2,
    arrow.mul, head.arrow = 0.05, select, const, axis.bp = TRUE, ...)
## S3 method for class 'cca':
scores(x, choices=c(1,2), display=c("sp","wa","cn"), scaling=2, ...)
## S3 method for class 'rda':
scores(x, choices=c(1,2), display=c("sp","wa","cn"), scaling=2, 
    const, ...)
## S3 method for class 'cca':
summary(object, scaling = 2, axes = 6, display = c("sp", "wa", 
    "lc", "bp", "cn"), digits = max(3, getOption("digits") - 3), ...)
## 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<

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

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

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 decision-vegan

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.

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 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).

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)
## Read description of scaling in RDA in vegan:
vegandocs("decision")