envfit: Fits an Environmental Vector or Factor onto an Ordination

Description Usage Arguments Details Value Note Author(s) See Also Examples

Description

The function fits environmental vectors or factors onto an ordination. The projections of points onto vectors have maximum correlation with corresponding environmental variables, and the factors show the averages of factor levels.

Usage

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## Default S3 method:
envfit(ord, env, permutations = 999, strata = NULL, 
   choices=c(1,2),  display = "sites", w  = weights(ord), na.rm = FALSE, ...)
## S3 method for class 'formula'
envfit(formula, data, ...)
## S3 method for class 'envfit'
plot(x, choices = c(1,2), labels, arrow.mul, at = c(0,0), 
   axis = FALSE, p.max = NULL, col = "blue", bg, add = TRUE, ...)
## S3 method for class 'envfit'
scores(x, display, choices, ...)
vectorfit(X, P, permutations = 0, strata = NULL, w, ...)
factorfit(X, P, permutations = 0, strata = NULL, w, ...)

Arguments

ord

An ordination object or other structure from which the ordination scores can be extracted (including a data frame or matrix of scores).

env

Data frame, matrix or vector of environmental variables. The variables can be of mixed type (factors, continuous variables) in data frames.

X

Matrix or data frame of ordination scores.

P

Data frame, matrix or vector of environmental variable(s). These must be continuous for vectorfit and factors or characters for factorfit.

permutations

a list of control values for the permutations as returned by the function 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, data

Model formula and data.

na.rm

Remove points with missing values in ordination scores or environmental variables. The operation is casewise: the whole row of data is removed if there is a missing value and na.rm = TRUE.

x

A result object from 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.

choices

Axes to plotted.

labels

Change plotting labels. The argument should be a list with elements 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.

arrow.mul

Multiplier for vector lengths. The arrows are automatically scaled similarly as in plot.cca if this is not given and add = TRUE.

at

The origin of fitted arrows in the plot. If you plot arrows in other places then origin, you probably have to specify arrrow.mul.

axis

Plot axis showing the scaling of fitted arrows.

p.max

Maximum estimated P value for displayed variables. You must calculate P values with setting permutations to use this option.

col

Colour in plotting.

bg

Background colour for labels. If bg is set, the labels are displayed with ordilabel instead of text. See Examples for using semitransparent background.

add

Results added to an existing ordination plot.

strata

An integer vector or factor specifying the strata for permutation. If supplied, observations are permuted only within the specified strata.

display

In fitting functions these are ordinary site scores or linear combination scores ("lc") in constrained ordination (cca, rda, capscale). In scores function they are either "vectors" or "factors" (with synonyms "bp" or "cn", resp.).

w

Weights used in fitting (concerns mainly cca and decorana results which have nonconstant weights).

...

Parameters passed to scores.

Details

Function 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 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" (and possibly scaling = 2).

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.

Value

Functions vectorfit and factorfit return lists of classes vectorfit and factorfit which have a print method. The result object have the following items:

arrows

Arrow endpoints from vectorfit. The arrows are scaled to unit length.

centroids

Class centroids from factorfit.

r

Goodness of fit statistic: Squared correlation coefficient

permutations

Number of permutations.

control

A list of control values for the permutations as returned by the function how.

pvals

Empirical P-values for each variable.

Function envfit returns a list of class envfit with results of vectorfit and envfit as items.

Function plot.envfit scales the vectors by correlation.

Note

Fitted vectors have become the method of choice in displaying environmental variables in ordination. Indeed, they are the optimal way of presenting environmental variables in Constrained Correspondence Analysis cca, since there they are the linear constraints. In unconstrained ordination the relation between external variables and ordination configuration may be less linear, and therefore other methods than arrows may be more useful. The simplest is to adjust the plotting symbol sizes (cex, symbols) by environmental variables. Fancier methods involve smoothing and regression methods that abound in R, and ordisurf provides a wrapper for some.

Author(s)

Jari Oksanen. The permutation test derives from the code suggested by Michael Scroggie.

See Also

A better alternative to vectors may be ordisurf.

Examples

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data(varespec)
data(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")

## 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)

Example output

Loading required package: permute
Loading required package: lattice
This is vegan 2.4-3
Square root transformation
Wisconsin double standardization
Run 0 stress 0.1843196 
Run 1 stress 0.2386162 
Run 2 stress 0.2140424 
Run 3 stress 0.2141096 
Run 4 stress 0.2335639 
Run 5 stress 0.2265716 
Run 6 stress 0.2094761 
Run 7 stress 0.2254456 
Run 8 stress 0.1974407 
Run 9 stress 0.2325123 
Run 10 stress 0.1843196 
... New best solution
... Procrustes: rmse 1.758584e-05  max resid 6.905317e-05 
... Similar to previous best
Run 11 stress 0.195049 
Run 12 stress 0.2354621 
Run 13 stress 0.2145795 
Run 14 stress 0.2166094 
Run 15 stress 0.2422932 
Run 16 stress 0.2263597 
Run 17 stress 0.1852397 
Run 18 stress 0.2152708 
Run 19 stress 0.2050707 
Run 20 stress 0.267362 
*** Solution reached

***VECTORS

            NMDS1    NMDS2     r2 Pr(>r)    
N        -0.05039 -0.99873 0.2080  0.101    
P         0.68715  0.72652 0.1755  0.135    
K         0.82741  0.56161 0.1657  0.154    
Ca        0.75022  0.66118 0.2810  0.033 *  
Mg        0.69688  0.71718 0.3493  0.008 ** 
S         0.27641  0.96104 0.1774  0.127    
Al       -0.83763  0.54625 0.5155  0.002 ** 
Fe       -0.86176  0.50732 0.3999  0.005 ** 
Mn        0.80224 -0.59700 0.5322  0.001 ***
Zn        0.66533  0.74655 0.1779  0.119    
Mo       -0.84871  0.52886 0.0517  0.564    
Baresoil  0.87195 -0.48960 0.2494  0.040 *  
Humdepth  0.92627 -0.37686 0.5591  0.001 ***
pH       -0.79905  0.60127 0.2625  0.039 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Permutation: free
Number of permutations: 999


               NMDS1      NMDS2
N        -0.02298243 -0.4555318
P         0.28786999  0.3043624
K         0.33677266  0.2285862
Ca        0.39766734  0.3504696
Mg        0.41185372  0.4238519
S         0.11640862  0.4047417
Al       -0.60138592  0.3921850
Fe       -0.54497790  0.3208271
Mn        0.58527476 -0.4355458
Zn        0.28061460  0.3148670
Mo       -0.19291014  0.1202102
Baresoil  0.43546052 -0.2445131
Humdepth  0.69257033 -0.2817812
pH       -0.40937530  0.3080495
$vectors
[1] "A1"

$factors
[1] "Moisture1" "Moisture2" "Moisture4" "Moisture5"

vegan documentation built on May 31, 2017, 4:08 a.m.