vegan (version 1.2-0)

decorana: Detrended Correspondence Analysis and Basic Reciprocal Averaging

Description

Performs detrended correspondence analysis and basic reciprocal averaging or orthogonal correspondence analysis.

Usage

decorana(veg, iweigh=0, iresc=4, ira=0, mk=26, short=0, before=NULL,
         after=NULL)
## S3 method for class 'decorana':
plot(x, choices=c(1,2), origin=TRUE, display=c("both","sites","species","none"),
     cex = 0.8, tol = 0.1, cols = c(1,2), ...)
## S3 method for class 'decorana':
summary(x, digits=3, origin=TRUE, display=c("both", "species","sites","none"), ...)

Arguments

veg

Community data matrix.

iweigh

Downweighting of rare species (0: no).

iresc

Number of rescaling cycles (0: no rescaling).

ira

Type of analysis (0: detrended, 1: basic reciprocal averaging).

mk

Number of segments in rescaling.

short

Shortest gradient to be rescaled.

before

Hill's piecewise transformation: values before transformation.

after

Hill's piecewise transformation: values after transformation -- these must correspond to values in before.

x

A decorana result object.

choices

Axes to be plotted.

origin

Use true origin even in detrended correspondence analysis.

display

Display only sites, only species, both or neither.

cex

Plot character size.

tol

Proportion of empty space at the margins.

cols

Colours used for sites and species.

Value

• Function returns an object of class decorana, which has print, summary and plot methods.

Details

In late 1970s, correspondence analysis became the method of choice for ordination in vegetation science, since it seemed to be able to cope with non-linear species responses better than principal components analysis. However, even correspondence analysis produced arc-shaped configuration of a single gradient. Mark Hill developed detrended correspondence analysis to correct two assumed `faults' in correspondence analysis: curvature of straight gradients and packing of sites at the ends of the gradient.

The curvature is removed by replacing the orthogonalization of axes with detrending. In orthogonalization the successive axes are made non-correlated, but detrending should remove all systematic dependence between axes. Detrending is made using a five-segment smoothing window with weigths (1,2,3,2,1) on mk segments -- which indeed is morerobust than the suggested alternative of detrending by polynomials. The packing of sites at the ends of the gradient is undone by rescaling the axes after extraction. After rescaling, the axis is supposed to be scaled by `SD' units, so that the average width of Gaussian species responses is supposed to be one over whole axis. Other innovations were the piecewise linear transformation of species abundances and downweighting of rare species which were regarded to have an unduly high influence on ordination axes.

It seems that detrending works actually by twisting the ordination space, so that the results look non-curved in two-dimensional projections (`lolly paper effect'). As a result, the points have usually an easily recognized triangle or diamond shaped pattern, obviously as a detrending artefact. Rescaling works differently than commonly presented, too. Decorana does not use, or even evaluate, the widths of species responses. Instead, it tries to equalize the weighted variance of species scores on axis segments (parameter mk has only a small effect, since decorana finds the segment number from the current estimate of axis length). This equalizes response widths only for the idealized species packing model, where all species initially have unit width responses and equally spaced modes.

Function summary.decorana prints the ordination scores, possible prior weights used in downweighting, and the marginal totals after applying these weights. Function plot.decorana plots species and site scores. Classical decorana scaled the axes so that smallest site score was 0 (and smallest species score was negative), but summary.decorana and plot.decorana use the true origin, unless origin=FALSE.

References

Hill, M.O. and Gauch, H.G. (1980). Detrended correspondence analysis: an improved ordination technique. Vegetatio 42, 47--58.

See Also

For unconstrained ordination, non-metric multidimensional scaling in isoMDS may be more robust. Constrained (or `canonical') correspondence analysis can be made with CAIV. Orthogonal correspondence analysis can be made with ca, or with decorana or CAIV, but the scaling of results vary.

Examples

data(varespec)
vare.dca <- decorana(varespec)
vare.dca
summary(vare.dca)
plot(vare.dca)
### the detrending rationale:
gaussresp <- function(x,u) exp(-(x-u)^2/2)
x <- seq(0,6,length=15) ## The gradient
u <- seq(-2,8,len=23)   ## The optima
pack <- outer(x,u,gaussresp)
matplot(x, pack, type="l", main="Species packing")
library(MASS)   ## isoMDS, eqscplot
library(mva)    ## prcomp, cmdscale (for isoMDS)
library(multiv) ## ca
opar <- par(mfrow=c(2,2))
eqscplot(prcomp(pack)$x, type="b", main="PCA")
eqscplot(ca(pack)$rproj, type="b", main="CA")
eqscplot(decorana(pack)$rproj, type="b", main="DCA")
eqscplot(isoMDS(vegdist(pack), maxit=200)$points, type="b", main="NMDS")
### Let's add some noise:
noisy <- (0.5 + runif(length(pack)))*pack
par(mfrow=c(2,1))
matplot(x, pack, type="l", main="Ideal model")
matplot(x, noisy, type="l", main="Noisy model")
par(mfrow=c(2,2))
eqscplot(prcomp(noisy)$x, type="b", main="PCA")
eqscplot(ca(noisy)$rproj, type="b", main="CA")
eqscplot(decorana(noisy)$rproj, type="b", main="DCA")
eqscplot(isoMDS(vegdist(noisy), maxit=200)$points, type="b", main="NMDS")
par(opar)