pco: Principal Coordinates Analysis

In labdsv: Ordination and Multivariate Analysis for Ecology

Principal Coordinates Analysis

Description

Principal coordinates analysis is an eigenanalysis of distance or metric dissimilarity matrices.

Usage

pco(dis, k=2)

Arguments

dis	the distance or dissimilarity matrix object of class "dist" returned from dist, vegdist, or dsvdis
k	the number of dimensions to return

Details

pco is simply a wrapper for the cmdscale function of Venebles and Ripley to make plotting of the function similar to other LabDSV functions

Value

An object of class ‘pco’ with components:

points

the coordinates of samples on eigenvectors

Note

Principal Coordinates Analysis was pioneered by Gower (1966) as an alternative to PCA better suited to ecological datasets.

Author(s)

of the ‘cmdscale’ function: Venebles and Ripley

of the wrapper function David W. Roberts [e-mail]

References

Gower, J.C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325-328.

See Also

cmdscale, pca, nmds, cca

Examples

data(bryceveg) # returns a vegetation data.frame
dis.bc <- dsvdis(bryceveg,'bray/curtis')
                  # returns an object of class dist'
veg.pco <- pco(dis.bc,k=4) # returns first 4 dimensions
plot(veg.pco)

labdsv documentation built on April 10, 2023, 5:08 p.m.