R: Principal coordinates analysis

pco {ecodist} | R Documentation

## Principal coordinates analysis

### Description

Principal coordinates analysis (classical scaling).

### Usage

    pco(x, negvals = "zero", dround = 0)

### Arguments

`x` | a lower-triangular dissimilarity matrix.
`negvals` | if = "zero" sets all negative eigenvalues to zero; if = "rm" corrects for negative eigenvalues using method 1 of Legendre and Anderson 1999.
`dround` | if greater than 0, attempts to correct for round-off error by rounding to that number of places.

### Details

PCO (classical scaling, metric multidimensional scaling) is very similar to principal components analysis, but allows the use of any dissimilarity metric.

### Value

`values` | eigenvalue for each component. This is a measure of the variance explained by each dimension.
`vectors` | eigenvectors. Each column contains the scores for that dimension.

### Author(s)

Sarah Goslee

### See Also

`princomp`, `nmds`

### Examples

    data(iris)
    iris.d <- dist(iris[,1:4])
    iris.pco <- pco(iris.d)

    # scatterplot of the first two dimensions
    plot(iris.pco$vectors[,1:2], col=as.numeric(iris$Species),
      pch=as.numeric(iris$Species), main="PCO", xlab="PCO 1", ylab="PCO 2")

[Package ecodist version 2.0.1 Index]