labdsv (version 1.8-0)

pco: 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

Value

an object of class ‘pco’ with components:

points

the coordinates of samples on eigenvectors

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

References

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

http://ecology.msu.montana.edu/labdsv/R/labs/lab8/lab8.html

See Also

cmdscale, pca, nmds, cca

Examples

# NOT RUN {
        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
# }