# pcoa

##### Principal Coordinate Analysis

Function `pcoa`

computes principal coordinate decomposition (also called classical scaling) of a distance matrix D (Gower 1966). It implements two correction methods for negative eigenvalues.

- Keywords
- multivariate

##### Usage

`pcoa(D, correction="none", rn=NULL)`## S3 method for class 'pcoa':
biplot(x, Y=NULL, plot.axes = c(1,2), dir.axis1=1, dir.axis2=1, rn=NULL, ...)

##### Details

This function implements two methods for correcting for negative values in principal coordinate analysis (PCoA). Negative eigenvalues can be produced in PCoA when decomposing distance matrices produced by coefficients that are not Euclidean (Gower and Legendre 1986, Legendre and Legendre 1998).

In `pcoa`

, when negative eigenvalues are present in the decomposition results, the distance matrix D can be modified using either the Lingoes or the Cailliez procedure to produce results without negative eigenvalues.

In the Lingoes (1971) procedure, a constant c1, equal to twice absolute value of the largest negative value of the original principal coordinate analysis, is added to each original squared distance in the distance matrix, except the diagonal values. A newe principal coordinate analysis, performed on the modified distances, has at most (n-2) positive eigenvalues, at least 2 null eigenvalues, and no negative eigenvalue.

In the Cailliez (1983) procedure, a constant c2 is added to the original distances in the distance matrix, except the diagonal values. The calculation of c2 is described in Legendre and Legendre (1998). A new principal coordinate analysis, performed on the modified distances, has at most (n-2) positive eigenvalues, at least 2 null eigenvalues, and no negative eigenvalue.

In all cases, only the eigenvectors corresponding to positive eigenvalues are shown in the output list. The eigenvectors are scaled to the square root of the corresponding eigenvalues. Gower (1966) has shown that eigenvectors scaled in that way preserve the original distance (in the D matrix) among the objects. These eigenvectors can be used to plot ordination graphs of the objects.

We recommend not to use PCoA to produce ordinations from the chord, chi-square, abundance profile, or Hellinger distances. It is easier to first transform the community composition data using the following transformations, available in the `decostand`

function of the `vegan`

package, and then carry out a principal component analysis (PCA) on the transformed data:

[object Object],[object Object],[object Object],[object Object]

The ordination results will be identical and the calculations shorter. This two-step ordination method, called transformation-based PCA (tb-PCA), was described by Legendre and Gallagher (2001).

The `biplot.pcoa`

function produces plots for any pair of principal coordinates. The original variables can be projected onto the ordination plot.

##### Value

`correction`

and variable 'correct' in the function.`correction`

.

##### References

Cailliez, F. (1983) The analytical solution of the additive constant problem. *Psychometrika*, **48**, 305--308.

Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. *Biometrika*, **53**, 325--338.

Gower, J. C. and Legendre, P. (1986) Metric and Euclidean properties of
dissimilarity coefficients. *Journal of Classification*, **3**, 5--48.

Legendre, P. and Gallagher, E. D. (2001) Ecologically meaningful transformations for ordination of species data. *Oecologia*, **129**, 271--280.

Legendre, P. and Legendre, L. (1998) *Numerical ecology, 2nd English
edition.* Amsterdam: Elsevier Science BV.

Lingoes, J. C. (1971) Some boundary conditions for a monotone analysis of symmetric matrices. *Psychometrika*, **36**, 195--203.

##### Examples

```
# Oribatid mite data from Borcard and Legendre (1994)
if (require(vegan)) {
data(mite) # Community composition data, 70 peat cores, 35 species
# Select rows 1:30. Species 35 is absent from these rows. Transform to log
mite.log <- log(mite[1:30,-35]+1) # Equivalent: log1p(mite[1:30,-35])
# Principal coordinate analysis and simple ordination plot
mite.D <- vegdist(mite.log, "bray")
res <- pcoa(mite.D)
res$values
biplot(res)
# Project unstandardized and standardized species on the PCoA ordination plot
mite.log.st = apply(mite.log, 2, scale, center=TRUE, scale=TRUE)
par(mfrow=c(1,2))
biplot(res, mite.log)
biplot(res, mite.log.st)
# Reverse the ordination axes in the plot
par(mfrow=c(1,2))
biplot(res, mite.log, dir.axis1=-1, dir.axis2=-1)
biplot(res, mite.log.st, dir.axis1=-1, dir.axis2=-1)
}
```

*Documentation reproduced from package ape, version 3.0-2, License: GPL (>= 2)*