Bclabels function

Plot bootstrap values

Print (bootstrap) values on 'hclust' plot

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.

Returns

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")