pco: Principal Coordinates Analysis

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/pco.R

Description

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

Usage

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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 droberts@montana.edu http://ecology.msu.montana.edu/droberts/droberts.html

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

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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)

Example output

Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-28. For overview type 'help("mgcv-package")'.
Loading required package: MASS
Loading required package: cluster

Attaching package: 'labdsv'

The following object is masked from 'package:stats':

    density

labdsv documentation built on Aug. 4, 2019, 5:03 p.m.

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