shipunov (version 1.13)

Ellipses: Confidence ellipses

Description

Calculates and plots group confidence ellipses

Usage

Ellipses(pts, groups, match.color=TRUE, usecolors=NULL,
 centers=FALSE, c.pch=0, c.cex=3,
 level=0.95, df=1000, prec=51,
 coords=NULL, plot=TRUE, ...)

Arguments

pts

Data points to plot

groups

Grouping variable

match.color

Match colors

usecolors

Use colors (palette)

centers

Show centers?

c.pch

Color of center points

c.cex

Scale of center points

level

Confidence level for F-distribution

df

Used in calculation of P-content according to F(2, df) distribution

prec

Precision of ellipse plotting (default is 51 points)

coords

Pre-calculated ellipses coordinates: list of two-column matrices named as groups (by default, not required)

plot

Plot?

...

Arguments to lines()

Value

Invisibly returns the list in the form similar to Hulls(), to use as a list of polygons or with Overlap().

Details

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

Also, with a help from Pinhull() (see its help), it is possible to reveal "outliers", points outside of each ellipse borders.

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

See Also

Hulls, Overlap, Pinhull

Examples

# NOT RUN {
iris.p <- prcomp(iris[, -5])$x[, 1:2]
plot(iris.p, type="n", xlab="PC1", ylab="PC2")
text(iris.p, labels=abbreviate(iris[, 5], 1, method="both.sides"))
iris.e <- Ellipses(iris.p[, 1:2], iris[, 5], centers=TRUE)

## calculate overlap between ellipses
Overlap(iris.e)

## how to plot filled ellipses
plot(iris.p, type="n", xlab="PC1", ylab="PC2")
text(iris.p, labels=abbreviate(iris[, 5], 1, method="both.sides"))
for (i in seq_along(iris.e))
 polygon(iris.e[[i]], border=NA, col=adjustcolor(i, alpha.f=0.2))

## how to reveal (and label) "outliers", points outside of _all_ ellipses
iris.pie <- Pinhull(iris.p, iris.e)
outs <- which(apply(iris.pie, 1, sum) == 0)
points(iris.p[outs, ], cex=2, pch=4)

## embedded convex hulls
plot(iris.p, col=iris$Species)
for (i in seq_along(iris.e)) lines(iris.e[[i]], col=i, lty=2)
mi <- cbind(seq_len(nrow(iris)), as.numeric(iris$Species)) # indexing matrix
## remove "outliers" in broad sense, points which are outside of its "own" ellipse:
emb <- rowSums(iris.pie) == 1 & iris.pie[mi]
Hulls(iris.p[emb, ], iris$Species[emb])

## LDA ellipes
library(MASS)
ch.lda <- lda(Species ~ ., data=chaetocnema[, -2])
ch.lda.pred <- predict(ch.lda, chaetocnema[, -(1:2)])
## ellipses here are by default bigger then plot so use workaround:
ee <- Ellipses(ch.lda.pred$x, chaetocnema$Species, plot=FALSE)
xx <- range(c(do.call(rbind, ee)[, 1], ch.lda.pred$x[, 1]))
yy <- range(c(do.call(rbind, ee)[, 2], ch.lda.pred$x[, 2]))
plot(ch.lda.pred$x, col=chaetocnema$Species, xlim=xx, ylim=yy)
Ellipses(ch.lda.pred$x, chaetocnema$Species, coords=ee)

## search for the maximal level which gives zero overlap
plot(x5 ~ x17, data=haltica, pch=as.numeric(haltica$Species))
for (i in (99:59)/100) {
cat(i, "\n")
ee <- Ellipses(haltica[, c("x17", "x5")], haltica$Species, level=i, plot=FALSE)
print(mean(Overlap(ee), na.rm=TRUE))
cat("\n")
}
Ellipses(haltica[, c("x17", "x5")], haltica$Species, level=.62)
# }