Principal Coordinate Analysis (PCO) biplot method

Principal Coordinate Analysis (PCO) biplot method

PCO(bp, Dmat=NULL, dist.func=NULL, dist.func.cat=NULL,
           dim.biplot = c(2,1,3), e.vects = NULL, group.aes=NULL,
           show.class.means = FALSE, axes = c("regression","splines"), ...)

Arguments

bp

an object of class biplot obtained from preceding function biplot().

Dmat

nxn matrix of Euclidean embeddable distances between samples

dist.func

function to compute Euclidean embeddable distances between samples. The default NULL computes Euclidean distance.

dist.func.cat

function to compute Euclidean embeddable distance between categorical variables for the samples. The default NULL computes the extended matching coefficient.

dim.biplot

dimension of the biplot. Only values 1, 2 and 3 are accepted, with default 2.

e.vects

e.vects which eigenvectors (canonical variates) to extract, with default 1:dim.biplot.

group.aes

vector of the same length as the number of rows in the data matrix for differentiated aesthetics for samples.

show.class.means

logical, indicating whether to plot the class means on the biplot.

axes

type of biplot axes, currently only regression axes are implemented

...

more arguments to dist.func

Value

Object of class biplot

Examples

biplot(iris[,1:4]) |> PCO(dist.func = sqrtManhattan)
#> Object of class biplot, based on 150 samples and 4 variables.
#> 4 numeric variables.
# create a CVA biplot
biplot(iris[,1:4]) |> PCO(dist.func = sqrtManhattan) |> plot()