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, Sarah.Goslee@ars.usda.gov

See Also

princomp, nmds

Examples


## Not run: 
data(iris)
iris.md <- distance(iris[,1:4], "mahal")
iris.pco <- pco(iris.md)

# scatterplot of the first two dimensions
plot(iris.pco$vectors[,1], iris.pco$vectors[,2], pch=as.numeric(iris[,5]))
## End(Not run)


[Package ecodist version 1.2.2 Index]