Principal Coordinate Analysis (PCO) biplot method

Usage

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()