ade4 (version 1.7-19)

dudi.pco: Principal Coordinates Analysis

Description

dudi.pco performs a principal coordinates analysis of a Euclidean distance matrix and returns the results as objects of class pco and dudi.

Usage

dudi.pco(d, row.w = "uniform", scannf = TRUE, nf = 2, 
    full = FALSE, tol = 1e-07)
# S3 method for pco
scatter(x, xax = 1, yax = 2, clab.row = 1, posieig = "top", 
    sub = NULL, csub = 2, ...)

Value

dudi.pco returns a list of class pco and dudi. See dudi

Arguments

d

an object of class dist containing a Euclidean distance matrix.

row.w

an optional distance matrix row weights. If not NULL, must be a vector of positive numbers with length equal to the size of the distance matrix

scannf

a logical value indicating whether the eigenvalues bar plot should be displayed

nf

if scannf FALSE, an integer indicating the number of kept axes

full

a logical value indicating whether all the axes should be kept

tol

a tolerance threshold to test whether the distance matrix is Euclidean : an eigenvalue is considered positive if it is larger than -tol*lambda1 where lambda1 is the largest eigenvalue.

x

an object of class pco

xax

the column number for the x-axis

yax

the column number for the y-axis

clab.row

a character size for the row labels

posieig

if "top" the eigenvalues bar plot is upside, if "bottom" it is downside, if "none" no plot

sub

a string of characters to be inserted as legend

csub

a character size for the legend, used with par("cex")*csub

...

further arguments passed to or from other methods

Author

Daniel Chessel
Anne-Béatrice Dufour

References

Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325--338.

Examples

data(yanomama)
gen <- quasieuclid(as.dist(yanomama$gen))
geo <- quasieuclid(as.dist(yanomama$geo))
ant <- quasieuclid(as.dist(yanomama$ant))
geo1 <- dudi.pco(geo, scann = FALSE, nf = 3)
gen1 <- dudi.pco(gen, scann = FALSE, nf = 3)
ant1 <- dudi.pco(ant, scann = FALSE, nf = 3)
plot(coinertia(ant1, gen1, scann = FALSE))