capscale {vegan} | R Documentation

## [Partial] Constrained Analysis of Principal Coordinates or distance-based RDA

### Description

Constrained Analysis of Principal Coordinates (CAP) is an ordination method similar to Redundancy Analysis (`rda`), but it allows non-Euclidean dissimilarity indices, such as Manhattan or Bray–Curtis distance. Despite this non-Euclidean feature, the analysis is strictly linear and metric. If called with Euclidean distance, the results are identical to `rda`, but `capscale` will be much more inefficient. Function `capscale` is a constrained version of metric scaling, a.k.a. principal coordintates analysis, which also is based on Euclidean distance but still is more useful with other dissimilarity measures.

### Usage

    capscale(formula, data, distance = "euclidean", comm = NULL, add =
    FALSE,  ...)

### Arguments

`formula` | Model formula. The function can be called only with the formula interface. Most usual features of `formula` hold, especially as defined in `cca` and `rda`. The LHS must be either a community data matrix or a dissimilarity matrix, e.g., from `vegdist` or `dist`. If the LHS is a data matrix, function `vegdist` will be used to find the dissimilarities. RHS defines the constraints. The constraints can be continuous or factors, they can be transformed within the formula, and they can have interactions as in typical `formula`. The RHS can have a special term `Condition` that defines variables ``partialled out'' before constraints, just like in `rda` or `cca`. This allows the use of partial CAP.
`data` | Data frame containing the variables on the right hand side of the model formula.
`distance` | Dissimilarity (or distance) index in `vegdist` used if the LHS of the `formula` is a data frame instead of dissimilarity matrix.
`comm` | Community data frame which will be used for finding species scores when the LHS of the `formula` was a dissimilarity matrix. This is not used if the LHS is a data frame. If this is not supplied, the ``species scores'' are the axes of initial metric scaling (`cmdscale`) and may be confusing.
`add` | logical indicating if an additive constant should be computed, and added to the non-diagonal dissimilarities such that all eigenvalues are non-negative in underlying Principal Co-ordinates Analysis (see `cmdscale` for details).
`...` | Other parameters passed to `rda`.

### Details

The Canonical Analysis of Principal Coordinates (CAP) is simply a Redundancy Analysis of results of Metric (Classical) Multidimensional Scaling (Anderson & Willis 2003). Function capscale uses two steps: (1) it ordinates the dissimilarity matrix using `cmdscale` and (2) analyses these results using `rda`. If the user supplied a community data frame instead of dissimilarities, the function will find the needed dissimilarity matrix using `vegdist` with specified `distance`. However, the method will accept dissimilarity matrices from `vegdist`, `dist`, or any other method producing similar matrices. The constraining variables can be continuous or factors or both, they can have interaction terms, or they can be transformed in the call. Moreover, there can be a special term `Condition` just like in `rda` and `cca` so that ``partial'' CAP can be performed.

The current implementation differs from the method suggested by Anderson & Willis (2003) in three major points which actually make it similar to distance-based redundancy analysis (Legendre & Anderson 1999):

1. Anderson & Willis used orthonormal solution of `cmdscale`, whereas `capscale` uses axes weighted by corresponding eigenvalues, so that the ordination distances are best approximations of original dissimilarities. In the original method, later ``noise'' axes are just as important as first major axes.
2. Anderson & Willis take only a subset of axes, whereas `capscale` uses all axes with positive eigenvalues. The use of subset is necessary with orthonormal axes to chop off some ``noise'', but the use of all axes guarantees that the results are the best approximation of original dissimilarities.
3. Function `capscale` adds species scores as weighted sums of (residual) community matrix (if the matrix is available), whereas Anderson & Willis have no fixed method for adding species scores.

With these definitions, function `capscale` with Euclidean distances will be identical to `rda` in eigenvalues and in site, species and biplot scores (except for possible sign reversal). However, it makes no sense to use `capscale` with Euclidean distances, since direct use of `rda` is much more efficient. Even with non-Euclidean dissimilarities, the rest of the analysis will be metric and linear. The test for all axes is available only for ordinations defined with `formula` interface.

### Value

The function returns an object of class `capscale` which is identical to the result of `rda`. At the moment, `capscale` does not have specific methods, but it uses `cca` and `rda` methods `plot.cca`, `scores.rda` etc. Moreover, you can use `anova.cca` for permutation tests of ``significance'' of the results.

### Note

Warnings of negative eigenvalues are issued with most dissimilarity indices. These are harmless, and negative eigenvalues will be ignored in the analysis. If the warnings are disturbing, you can use argument `add = TRUE` passed to `cmdscale`, or, preferably, a distance measure that does not cause these warnings. In `vegdist`, `method = "jaccard"` gives such an index. Alternatively, after square root transformation many indices do not cause warnings.

Function `rda` usually divides the ordination scores by number of sites minus one. In this way, the inertia is variance instead of sum of squares, and the eigenvalues sum up to variance. Many dissimilarity measures are in the range 0 to 1, so they have already made a similar division. If the largest original dissimilarity is less or equal to 4 (allowing for `stepacross`), this division is undone in `capscale` and original dissimilarities are used. The inertia is called as `squared dissimilarity` (as defined in the dissimilarity matrix), but keyword `mean` is added to the inertia in cases where division was made, e.g. in Euclidean and Manhattan distances.

### Author(s)

Jari Oksanen

### References

Anderson, M.J. & Willis, T.J. (2003). Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84, 511–525.

Legendre, P. & Anderson, M. J. (1999). Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecological Monographs 69, 1–24.

### See Also

`rda`, `cca`, `plot.cca`, `anova.cca`, `vegdist`, `dist`, `cmdscale`.

### Examples

    data(varespec)
    data(varechem)
    vare.cap <- capscale(varespec ~ N + P + K + Condition(Al), varechem, dist="bray")
    vare.cap
    plot(vare.cap)
    anova(vare.cap)

[Package vegan version 1.8-8 Index]