Bclabels {shipunov}

Plot bootstrap values

Description

Print (bootstrap) values on 'hclust' plot

Usage

Bclabels(hcl, values, coords=NULL, horiz=FALSE, method="text",
 threshold=NULL, top=NULL, percent=FALSE, ...)

Arguments

hcl

hclust object

values

numeric, (bootstrap) values to use

coords

If NULL (default), coordinates will be calculated with Hcoords(hcl)

horiz

Plot values for a horizontal tree?

method

If "text" (default), plot text values, if "points", plot points

threshold

If set, do not plot text or points for values < threshold; respects percents if set

top

If set as 'n', plot values only for 'n' highest clusters

percent

Plot values as percents?

...

If "text" (default), additional arguments to text(), if "points", to points()

Details

This low-level plot function plots text or points in accordance with bootstrap values to the corresponding node of the plotted 'hclust' object.

Value

List with components: 'coords' for coordinates, 'labels' for (selected) values.

See Also

Bclust

Examples

## 'atmospheres' data
(bb <- Bclust(t(atmospheres))) # specify 'mc.cores=4' or similar to speed up the process

## standard use
plot(bb$hclust)
Bclabels(bb$hclust, bb$values, col="blue", pos=3, offset=0.1, threshold=0.9)

## 'points' method
plot(bb$hclust)
Bclabels(bb$hclust, bb$values, method="points", threshold=0.9, pch=19, cex=2)

## 'points' which grow with support
plot(bb$hclust)
Bclabels(bb$hclust, bb$values, method="points", pch=19, cex=bb$values*3)

## pre-defined coordinates
coords1 <- Hcoords(bb$hclust)
plot(bb$hclust)
Bclabels(bb$hclust, bb$values, coords=coords1, method="points", pch=19,
 cex=bb$values*3)

## use with horizontal Ploth()
oldpar <- par(mar=c(2,1,0,4))
Ploth(bb$hclust, horiz=TRUE)
Bclabels(bb$hclust, bb$values, col="blue", pos=3, offset=0.1, horiz=TRUE)
par(oldpar)

## 'moldino' data
m.bb <- Bclust(t(moldino)) # specify 'mc.cores=4' or similar to speed up the process
plot(m.bb$hclust)
Bclabels(m.bb$hclust, m.bb$values, col="red", pos=3, offset=0.1, threshold=0.5)

## 'iris' data, with hyper-binding to make number of variables reliable
iris.bb <- Bclust(iris[, rep(1:4, 6)], iter=100) # remove iter=100 for better bootstrap
plot(iris.bb$hclust, labels=FALSE, main="", xlab="", sub="Bootstrap, 100 replicates")
## use 'percent' and 'top'
Bclabels(iris.bb$hclust, iris.bb$values, top=5, percent=TRUE, pos=3, offset=0.1)
Fence(iris.bb$hclust, iris$Species)
legend("topright", legend=levels(iris$Species), col=1:3, lwd=2.5, bty="n")