ecodist (version 2.1.3)

pco: Principal coordinates analysis

Description

Principal coordinates analysis (classical scaling).

Usage

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

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.

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.

Author

Sarah Goslee

Details

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

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")