pco: Principal coordinates analysis

In phiala/ecodist: Dissimilarity-Based Functions for Ecological Analysis

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. data frame with columns containing 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")

phiala/ecodist documentation built on Nov. 5, 2023, 10:47 a.m.