plot.cca {vegan} | R Documentation

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

### Arguments

`x, object` | 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 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 sqrt(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 in`rda`, 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`.
`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.
`axes` | Number of axes in summaries.
`digits` | Number of digits in output.
`head, tail` | Number of rows printed from the head and tail of species and site scores. Default `NA` prints all.
`...` | Other parameters for plotting 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 all output for scores by setting `axes = 0` or `display = NA` or `display = NULL`. 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.

### 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)

[Package vegan version 1.8-8 Index]