cca), redundancy analysis (rda) or
constrained analysis of principal coordinates (capscale).
"plot"(x, choices = c(1, 2), display = c("sp", "wa", "cn"), scaling = "species", type, xlim, ylim, const, correlation = FALSE, hill = FALSE, ...)
"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, ...)
"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, ...)
"scores"(x, choices = c(1,2), display = c("sp","wa","cn"), scaling = "species", hill = FALSE, ...)
"scores"(x, choices = c(1,2), display = c("sp","wa","cn"), scaling = "species", const, correlation = FALSE, ...)
"summary"(object, scaling = "species", axes = 6, display = c("sp", "wa", "lc", "bp", "cn"), digits = max(3, getOption("digits") - 3), correlation = FALSE, hill = FALSE, ...)
"print"(x, digits = x$digits, head = NA, tail = head, ...)
"head"(x, n = 6, tail = 0, ...)
"tail"(x, n = 6, head = 0, ...)cca result object.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.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 $\sqrt(1/(1-\lambda))$. 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. 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.
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.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.TRUE for displayed items or a vector of indices
of displayed items.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 for biplot arrows.NA prints all.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.
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.
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).
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. 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)
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