Principal Coordinates Analysis

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 class 'pco'
scatter(x, xax = 1, yax = 2, clab.row = 1, posieig = "top", 
    sub = NULL, csub = 2, ...)

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

Value

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

References

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

Author

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

Examples

data(yanomama)
gen <- quasieuclid(as.dist(yanomama$gen))
geo <- quasieuclid(as.dist(yanomama$geo))
ant <- quasieuclid(as.dist(yanomama$ant))
geo1 <- dudi.pco(geo, scann = FALSE, nf = 3)
gen1 <- dudi.pco(gen, scann = FALSE, nf = 3)
ant1 <- dudi.pco(ant, scann = FALSE, nf = 3)
plot(coinertia(ant1, gen1, scann = FALSE))
#> Error in s.corcircle(dfxy = coinertia(ant1, gen1, scann = FALSE)$aX, xax = 1,     yax = 2, plot = FALSE, storeData = TRUE, pos = -3, psub = list(        text = "Unconstrained axes (X)"), pbackground = list(        box = FALSE), plabels = list(cex = 1.25)): non convenient selection for dfxy (can not be converted to dataframe)