Create alpha bags

This function produces \(\alpha\)-bags, which is a useful graphical summary of the scatter plot. The alpha-bag refers to a contour which contains \(\alpha\)% of the observations.

Usage

alpha.bags(bp, alpha = 0.95, which = NULL, col = ez.col[which], lty = 1,
lwd = 1, max = 2500, trace = TRUE, opacity = 0.25, outlying=FALSE)

Arguments

bp

an object of class biplot.

alpha

numeric vector between 0 and 1 to determine coverage of the bag (\(\alpha\)), with default 0.95.

which

numeric vector indicating the selection of groups or classes to be fitted with \(\alpha\)-bags.

col

vector of colours for the \(\alpha\)-bags. Multiple \(\alpha\) bags for one group will be displayed in the same colour.

lty

vector of line types for the \(\alpha\)-bags. The same line type will be used per value of \(\alpha\).

lwd

vector of line widths for the \(\alpha\)-bags. The same line width will be used per value of \(\alpha\).

max

maximum number of samples to include in \(\alpha\)-bag calculations, with default 2500. If more samples are in the group, a random sample of size max is taken for the computations.

trace

logical, indicating progress of computation.

opacity

level of opacity, with default 0.5.

outlying

logical indicating whether only outlying points should be plotted. Note the which argument may be overwritten when TRUE

Value

A list with the following components is available:

alpha.bags

list of coordinates for the \(\alpha\)-bags for each group.

col

vector of colours for the \(\alpha\)-bags.

lty

vector of line types for the \(\alpha\)-bags.

lwd

vector of line widths for the \(\alpha\)-bags.

References

Gower, J., Gardner-Lubbe, S. & Le Roux, N. (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Chichester, England: John Wiley & Sons Ltd.

Examples

biplot (iris[,1:4]) |> PCA(group.aes=iris[,5]) |> alpha.bags(alpha=0.95) |> plot()
#> Computing 0.95 -bag for setosa 
#> Computing 0.95 -bag for versicolor 
#> Computing 0.95 -bag for virginica 

biplot (iris[,1:4],group.aes=iris[,5]) |> PCA() |> alpha.bags(alpha=0.95) |> plot()
#> Computing 0.95 -bag for setosa 
#> Computing 0.95 -bag for versicolor 
#> Computing 0.95 -bag for virginica