cca {vegan}R Documentation

[Partial] [Constrained] Correspondence Analysis and Redundancy Analysis


Function cca performs correspondence analysis, or optionally constrained correspondence analysis (a.k.a. canonical correspondence analysis), or optionally partial constrained correspondence analysis. Function rda performs redundancy analysis, or optionally principal components analysis. These are all very popular ordination techniques in community ecology.


## S3 method for class 'formula':
cca(formula, data, na.action =, subset = NULL,
## Default S3 method:
cca(X, Y, Z, ...)
## S3 method for class 'formula':
rda(formula, data, scale=FALSE, na.action =,
  subset = NULL, ...)
## Default S3 method:
rda(X, Y, Z, scale=FALSE, ...)


formula Model formula, where the left hand side gives the community data matrix, right hand side gives the constraining variables, and conditioning variables can be given within a special function Condition.
data Data frame containing the variables on the right hand side of the model formula.
X Community data matrix.
Y Constraining matrix, typically of environmental variables. Can be missing.
Z Conditioning matrix, the effect of which is removed (`partialled out') before next step. Can be missing.
scale Scale species to unit variance (like correlations).
na.action Handling of missing values in constraints or conditions. The default ( is to stop with missing value. Choice na.omit removes all rows with missing values. Choice na.exclude keeps all observations but gives NA for results that cannot be calculated. The WA scores of rows may be found also for missing values in constraints. Missing values are never allowed in dependent community data.
subset Subset of data rows. This can be a logical vector which is TRUE for kept observations, or a logical expression which can contain variables in the working environment, data or species names of the community data.
... Other arguments for print or plot functions (ignored in other functions).


Since their introduction (ter Braak 1986), constrained, or canonical, correspondence analysis and its spin-off, redundancy analysis, have been the most popular ordination methods in community ecology. Functions cca and rda are similar to popular proprietary software Canoco, although the implementation is completely different. The functions are based on Legendre & Legendre's (1998) algorithm: in cca Chi-square transformed data matrix is subjected to weighted linear regression on constraining variables, and the fitted values are submitted to correspondence analysis performed via singular value decomposition (svd). Function rda is similar, but uses ordinary, unweighted linear regression and unweighted SVD.

The functions can be called either with matrix-like entries for community data and constraints, or with formula interface. In general, the formula interface is preferred, because it allows a better control of the model and allows factor constraints.

In the following sections, X, Y and Z, although referred to as matrices, are more commonly data frames.

In the matrix interface, the community data matrix X must be given, but the other data matrices may be omitted, and the corresponding stage of analysis is skipped. If matrix Z is supplied, its effects are removed from the community matrix, and the residual matrix is submitted to the next stage. This is called `partial' correspondence or redundancy analysis. If matrix Y is supplied, it is used to constrain the ordination, resulting in constrained or canonical correspondence analysis, or redundancy analysis. Finally, the residual is submitted to ordinary correspondence analysis (or principal components analysis). If both matrices Z and Y are missing, the data matrix is analysed by ordinary correspondence analysis (or principal components analysis).

Instead of separate matrices, the model can be defined using a model formula. The left hand side must be the community data matrix (X). The right hand side defines the constraining model. The constraints can contain ordered or unordered factors, interactions among variables and functions of variables. The defined contrasts are honoured in factor variables. The constraints can also be matrices (but not data frames). The formula can include a special term Condition for conditioning variables (``covariables'') ``partialled out'' before analysis. So the following commands are equivalent: cca(X, Y, Z), cca(X ~ Y + Condition(Z)), where Y and Z refer to constraints and conditions matrices respectively.

Constrained correspondence analysis is indeed a constrained method: CCA does not try to display all variation in the data, but only the part that can be explained by the used constraints. Consequently, the results are strongly dependent on the set of constraints and their transformations or interactions among the constraints. The shotgun method is to use all environmental variables as constraints. However, such exploratory problems are better analysed with unconstrained methods such as correspondence analysis (decorana, corresp) or non-metric multidimensional scaling (isoMDS) and environmental interpretation after analysis (envfit, ordisurf). CCA is a good choice if the user has clear and strong a priori hypotheses on constraints and is not interested in the major structure in the data set.

CCA is able to correct the curve artefact commonly found in correspondence analysis by forcing the configuration into linear constraints. However, the curve artefact can be avoided only with a low number of constraints that do not have a curvilinear relation with each other. The curve can reappear even with two badly chosen constraints or a single factor. Although the formula interface makes easy to include polynomial or interaction terms, such terms often produce curved artefacts (that are difficult to interpret), these should probably be avoided.

According to folklore, rda should be used with ``short gradients'' rather than cca. However, this is not based on research which finds methods based on Euclidean metric as uniformly weaker than those based on Chi-squared metric. However, standardized Euclidean distance may be an appropriate measures (see Hellinger standardization in decostand in particular).

Partial CCA (pCCA; or alternatively partial RDA) can be used to remove the effect of some conditioning or ``background'' or ``random'' variables or ``covariables'' before CCA proper. In fact, pCCA compares models cca(X ~ Z) and cca(X ~ Y + Z) and attributes their difference to the effect of Y cleansed of the effect of Z. Some people have used the method for extracting ``components of variance'' in CCA. However, if the effect of variables together is stronger than sum of both separately, this can increase total Chi-square after ``partialling out'' some variation, and give negative ``components of variance''. In general, such components of ``variance'' are not to be trusted due to interactions between two sets of variables.

The functions have summary and plot methods which are documented separately (see plot.cca, summary.cca).


Function cca returns a huge object of class cca, which is described separately in cca.object.
Function rda returns an object of class rda which inherits from class cca and is described in cca.object. The scaling used in rda scores is described in a separate vignette with this package.


The responsible author was Jari Oksanen, but the code borrows heavily from Dave Roberts (


The original method was by ter Braak, but the current implementations follows Legendre and Legendre.

Legendre, P. and Legendre, L. (1998) Numerical Ecology. 2nd English ed. Elsevier.

McCune, B. (1997) Influence of noisy environmental data on canonical correspondence analysis. Ecology 78, 2617-2623.

Palmer, M. W. (1993) Putting things in even better order: The advantages of canonical correspondence analysis. Ecology 74,2215-2230.

Ter Braak, C. J. F. (1986) Canonical Correspondence Analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167-1179.

See Also

There is a special documentation for plot.cca and summary.cca functions with their helper functions (text.cca, points.cca, scores.cca). Function anova.cca provides an ANOVA like permutation test for the ``significance'' of constraints. Automatic model building (dangerous!) is discussed in deviance.cca. Diagnostic tools, prediction and adding new points in ordination are discussed in goodness.cca and predict.cca. Function cca (library ade4) provide alternative implementations of CCA (these are internally quite different). Function capscale is a non-Euclidean generalization of rda. The result object is described in cca.object. You can use as.mlm to refit ordination result as a multiple response linear model to find some descriptive statistics. Design decisions are explained in vignettedecision-vegan’ which also can be accessed with vegandocs.


## Common but bad way: use all variables you happen to have in your
## environmental data matrix
vare.cca <- cca(varespec, varechem)
## Formula interface and a better model
vare.cca <- cca(varespec ~ Al + P*(K + Baresoil), data=varechem)
## `Partialling out' and `negative components of variance'
cca(varespec ~ Ca, varechem)
cca(varespec ~ Ca + Condition(pH), varechem)
## RDA
dune.Manure <- rda(dune ~ Manure, dune.env)
## For further documentation:
## Not run: 
## End(Not run)

[Package vegan version 1.16-32 Index]