Description Usage Arguments Details Value 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 = FALSE, 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 = FALSE, 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 arrows have
basically unit scaling, but if sites were scaled (`scaling`

`"sites"`

or `"symmetric"`

), the scores of requested axes
are adjusted relative to the axis with highest eigenvalue. With
`scaling = "species"`

or `scaling = "none"`

, the arrows will
be consistent with vectors fitted to linear combination scores
(`display = "lc"`

in function `envfit`

), but with
other scaling alternatives they will differ. The basic `plot`

function uses a simple heuristics for adjusting the unit-length arrows
to the current plot area, 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. With `display = "reg"`

arrows will be drawn for
regression coefficients (a.k.a. canonical coefficients) of constraints
and conditions. Biplot arrows can be interpreted individually, but
regression coefficients must be interpreted all together: the LC score
for each site is the sum of regressions displayed by arrows. The
partialled out conditions are zero and not shown in biplot arrows, but
they are shown for regressions, and show the effect that must be
partialled out to get the LC scores. The biplot arrows are more
standard and more easily interpreted, and regression arrows should be
used only if you know that you need them.

If you want to have a better control of plots, it is best to
construct the plot `text`

and `points`

commands which
accept graphical parameters. It is important to remember to use the
same `scaling`

, `correlation`

and `hill`

arguments
in all calls. The `plot.cca`

command returns invisibly an
`ordiplot`

result object, and this will have consistent
scaling for all its elements. The easiest way for full control of
graphics is to first set up the plot frame using `plot`

with
`type = "n"`

and all needed scores in `display`

and save
this result. The `points`

and `text`

commands for
`ordiplot`

will allow full graphical control (see
section Examples).

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.

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 16 17 18 19 20 21 22 23 | ```
data(dune)
data(dune.env)
mod <- cca(dune ~ A1 + Moisture + Management, dune.env)
## better control -- remember to set scaling etc identically
plot(mod, type="n", scaling="sites")
text(mod, dis="cn", scaling="sites")
points(mod, pch=21, col="red", bg="yellow", cex=1.2, scaling="sites")
text(mod, "species", col="blue", cex=0.8, scaling="sites")
## catch the invisible result and use ordiplot support - the example
## will make a biplot with arrows for species and correlation scaling
pca <- rda(dune)
pl <- plot(pca, type="n", scaling="sites", correlation=TRUE)
with(dune.env, points(pl, "site", pch=21, col=1, bg=Management))
text(pl, "sp", arrow=TRUE, length=0.05, col=4, cex=0.6, xpd=TRUE)
with(dune.env, legend("bottomleft", levels(Management), pch=21, pt.bg=1:4, bty="n"))
## 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)
``` |

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