"envfit"(ord, env, permutations = 999, strata = NULL, choices=c(1,2), display = "sites", w = weights(ord), na.rm = FALSE, ...)
"envfit"(formula, data, ...)
"plot"(x, choices = c(1,2), labels, arrow.mul, at = c(0,0), axis = FALSE, p.max = NULL, col = "blue", bg, add = TRUE, ...)
"scores"(x, display, choices, ...)
vectorfit(X, P, permutations = 0, strata = NULL, w, ...)
factorfit(X, P, permutations = 0, strata = NULL, w, ...)scores can be extracted (including a data
frame or matrix of scores).vectorfit and
factors or characters for factorfit. how, or the
number of permutations required, or a permutation matrix where each
row gives the permuted indices. Set permutations = 0 to skip
permutations.formula and data. na.rm = TRUE.envfit. For ordiArrowMul
and ordiArrowTextXY this must be a two-column matrix (or
matrix-like object) containing the coordinates of arrow heads on
the two plot axes, and other methods extract such a structure from
the envfit results.vectors and factors which give the new
plotting labels. If either of these elements is omitted, the
default labels will be used. If there is only one type of elements
(only vectors or only factors), the labels can be
given as vector. The default labels can be displayed with
labels command.plot.cca if this
is not given and add = TRUE.arrrow.mul.permutations to use this option. bg is set, the
labels are displayed with ordilabel instead of
text. See Examples for using semitransparent background.scores.vectorfit and factorfit return lists of
classes vectorfit and factorfit which have a
print method. The result object have the following items:Function envfit returns a list of class envfit with
results of vectorfit and envfit as items.Function plot.envfit scales the vectors by correlation.
envfit finds vectors or factor averages of
environmental variables. Function plot.envfit adds these in an
ordination diagram. If X is a data.frame,
envfit
uses factorfit for factor variables and
vectorfit for other variables. If X is a matrix or a
vector, envfit uses only vectorfit. Alternatively, the
model can be defined a simplified model formula, where
the left hand side must be an ordination result object or a matrix of
ordination scores, and right hand
side lists the environmental variables. The formula interface can be
used for easier selection and/or transformation of environmental
variables. Only the main effects will be analysed even if interaction
terms were defined in the formula. The ordination results are extracted with scores and
all extra arguments are passed to the scores. The fitted
models only apply to the results defined when extracting the scores
when using envfit. For instance, scaling in
constrained ordination (see scores.rda,
scores.cca) must be set in the same way in
envfit and in the plot or the ordination results (see
Examples).
The printed output of continuous variables (vectors) gives the
direction cosines which are the coordinates of the heads of unit
length vectors. In plot these are scaled by their
correlation (square root of the column r2) so that
“weak” predictors have shorter arrows than “strong”
predictors. You can see the scaled relative lengths using command
scores. The plotted (and scaled) arrows are further
adjusted to the current graph using a constant multiplier: this will
keep the relative r2-scaled lengths of the arrows but tries
to fill the current plot. You can see the multiplier using
ordiArrowMul(result_of_envfit), and set it with the
argument arrow.mul.
Functions vectorfit and factorfit can be called directly.
Function vectorfit finds directions in the ordination space
towards which the environmental vectors change most rapidly and to
which they have maximal correlations with the ordination
configuration. Function factorfit finds averages of ordination
scores for factor levels. Function factorfit treats ordered
and unordered factors similarly.
If permutations $> 0$, the `significance' of fitted vectors
or factors is assessed using permutation of environmental variables.
The goodness of fit statistic is squared correlation coefficient
($r^2$).
For factors this is defined as $r^2 = 1 - ss_w/ss_t$, where
$ss_w$ and $ss_t$ are within-group and total sums of
squares. See permutations for additional details on
permutation tests in Vegan.
User can supply a vector of prior weights w. If the ordination
object has weights, these will be used. In practise this means that
the row totals are used as weights with
cca or decorana results. If you do not
like this, but want to give
equal weights to all sites, you should set w = NULL.
The weighted fitting gives similar results to biplot
arrows and class centroids in cca.
For complete
similarity between fitted vectors and biplot arrows, you should set
display = "lc".
The lengths of arrows for fitted vectors are automatically adjusted
for the physical size of the plot, and the arrow lengths cannot be
compared across plots. For similar scaling of arrows, you must
explicitly set the arrow.mul argument in the plot
command; see ordiArrowMul and
ordiArrowTextXY.
The results can be accessed with scores.envfit function which
returns either the fitted vectors scaled by correlation coefficient or
the centroids of the fitted environmental variables.
ordisurf.
data(varespec, varechem)
library(MASS)
ord <- metaMDS(varespec)
(fit <- envfit(ord, varechem, perm = 999))
scores(fit, "vectors")
plot(ord)
plot(fit)
plot(fit, p.max = 0.05, col = "red")
## Adding fitted arrows to CCA. We use "lc" scores, and hope
## that arrows are scaled similarly in cca and envfit plots
ord <- cca(varespec ~ Al + P + K, varechem)
plot(ord, type="p")
fit <- envfit(ord, varechem, perm = 999, display = "lc")
plot(fit, p.max = 0.05, col = "red")
## 'scaling' must be set similarly in envfit and in ordination plot
plot(ord, type = "p", scaling = "sites")
fit <- envfit(ord, varechem, perm = 0, display = "lc", scaling = "sites")
plot(fit, col = "red")
## Class variables, formula interface, and displaying the
## inter-class variability with `ordispider', and semitransparent
## white background for labels (semitransparent colours are not
## supported by all graphics devices)
data(dune)
data(dune.env)
attach(dune.env)
ord <- cca(dune)
fit <- envfit(ord ~ Moisture + A1, dune.env, perm = 0)
plot(ord, type = "n")
ordispider(ord, Moisture, col="skyblue")
points(ord, display = "sites", col = as.numeric(Moisture), pch=16)
plot(fit, cex=1.2, axis=TRUE, bg = rgb(1, 1, 1, 0.5))
## Use shorter labels for factor centroids
labels(fit)
plot(ord)
plot(fit, labels=list(factors = paste("M", c(1,2,4,5), sep = "")),
bg = rgb(1,1,0,0.5))
detach(dune.env)
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