pco {Correlplot} R Documentation

## 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 (jan.graffelman@upc.edu)

### 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))


[Package Correlplot version 1.0.4 Index]