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

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 2.0.9 Index]