Hulls: Convex hulls for multiple groups

Description

Calculates and plots groups hulls and related information

Usage

Hulls(pts, groups, match.colors=TRUE, usecolors=NULL,
 plot=TRUE, centers=FALSE, c.pch=0, c.cex=3,
 outliers=TRUE, coef=1.5, ...)

Arguments

pts

Data points to plot, 2-dimensional

groups

Grouping variable, any type

match.colors

Match colors with groups

usecolors

Which group colors to use (does not rotate)

plot

Plot?

centers

Show centers?

c.pch

Type of center points

c.cex

Scale of center points

outliers

Include outliers?

coef

Determines how to detect outliers, see 'coef' from 'boxplot.stats()'

...

Arguments to 'lines()'

Value

Invisibly outputs list of hulls (polygons) with coordinates, and possibly also with 'centers' and 'outliers' attributes. Indices of margin points returned as row names of each polygon.

See also package 'cluster' for ellipsoidhulls() function that allows to draw ellipse-like hulls.

Details

If 'centers=TRUE', Hulls() calculates centroids of polygons corrsponding with convex hulls.

If 'outliers=FALSE', Hulls() uses boxplot.stats() to detect outliers (points which are most distant from centers). This option could be used for cluster sharpening. It also automatically switches to 'centers=TRUE' so if you want to plot smoothed hulls but do not want to plot their centers, use something like 'c.pch=NA' or 'c.cex=0' (see examples).

Please also check Ellipses() function which uses confidence ellipses based on F-distribution.

Note that (at least at the moment), polygons are plotted with line() function, therefore shading is not straightforward (but possible, see examples).

Examples

Run this code

# NOT RUN {
iris.p <- prcomp(iris[, -5])$x[, 1:2]
plot(iris.p, type="n", xlab="PC1", ylab="PC2")
pal <- rainbow(3)
text(iris.p, labels=abbreviate(iris[, 5], 1, method="both.sides"),
 col=pal[as.numeric(iris[, 5])])
Hulls(iris.p, iris[, 5], centers=TRUE, usecolors=pal)

## smoothed hulls
plot(iris.p, col=iris$Species, xlab="PC1", ylab="PC2")
ppts <- Hulls(iris.p, iris[, 5], centers=TRUE, outliers=FALSE, c.pch=NA)
## reveal outliers:
(out <- attr(ppts, "outliers"))
points(iris.p[out, ], pch=4, cex=1.4)

## this might complement Overlap()
cnts <- attr(ppts, "centers")
dist(cnts)
## how to use centers for clustering groups
plot(hclust(dist(cnts)))

## this is how to plot shaded hulls
plot(iris.p, pch=as.numeric(iris$Species))
for (i in seq_along(ppts))
 polygon(ppts[[i]], border=NA, col=adjustcolor(i, alpha.f=0.2))
# }

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