pco: Principal Coordinate Analysis

In Correlplot: A Collection of Functions for Graphing Correlation Matrices

Principal Coordinate Analysis

Description

pco is a program for Principal Coordinate Analysis.

Usage

pco(Dis)

Arguments

Dis	A distance or dissimilarity matrix

Details

The program pco does a principal coordinates analysis of a dissimilarity (or distance) matrix (Dij) where the diagonal elements, Dii, are zero.

Note that when we dispose of a similarity matrix rather that a distance matrix, a transformation is needed before calling coorprincipal. For instance, if Sij is a similarity matrix, Dij might be obtained as Dij = 1 - Sij/diag(Sij)

Goodness of fit calculations need to be revised such as to deal (in different ways) with negative eigenvalues.

Value

PC	the principal coordinates
Dl	all eigenvalues of the solution
Dk	the positive eigenvalues of the solution
B	double centred matrix for the eigenvalue decomposition
decom	the goodness of fit table

Author(s)

Jan Graffelman ([e-mail])

See Also

cmdscale

Examples

citynames <- c("Aberystwyth","Brighton","Carlisle","Dover","Exeter","Glasgow","Hull",
"Inverness","Leeds","London","Newcastle", "Norwich")    
A <-matrix(c(
0,244,218,284,197,312,215,469,166,212,253,270,
244,0,350,77,167,444,221,583,242,53,325,168,
218,350,0,369,347,94,150,251,116,298,57,284,
284,77,369,0,242,463,236,598,257,72,340,164,
197,167,347,242,0,441,279,598,269,170,359,277,
312,444,94,463,441,0,245,169,210,392,143,378,
215,221,150,236,279,245,0,380,55,168,117,143,
469,583,251,598,598,169,380,0,349,531,264,514,
166,242,116,257,269,210,55,349,0,190,91,173,
212,53,298,72,170,392,168,531,190,0,273,111,
253,325,57,340,359,143,117,264,91,273,0,256,
270,168,284,164,277,378,143,514,173,111,256,0),ncol=12)
rownames(A) <- citynames
colnames(A) <- citynames
out <- pco(A)
plot(out$PC[,2],-out$PC[,1],pch=19,asp=1)
textxy(out$PC[,2],-out$PC[,1],rownames(A))

Correlplot documentation built on March 7, 2023, 8:33 p.m.