shipunov (version 1.2)

Bclust: Bootstrapped hclust

Description

Bootstraps hierarchical clustering

Usage

Bclust(data, method.d="manhattan", method.c="ward.D",
FUN=function(.x) hclust(dist(.x, method=method.d), method=method.c),
iter=1000, mc.cores=1, monitor=TRUE)

Arguments

data

Data suitable for the chosen distance method

method.d

Method for dist()

method.c

Method for hclust()

FUN

Function to make 'hclust' objects

iter

Number of replicates

mc.cores

integer, number of processes to run in parallel

monitor

If TRUE (default), prints a dot for each replicate

Value

List with components: 'values' for bootstrapped frequencies of each node, 'hcl' for original 'hclust' object, 'consensus' which is a sum of all Hcl2mat() matrices.

Details

This function provides bootstrapping for hierarchical clustering (hclust objects).

Bclust() and companion functions were based on functions from the 'bootstrap' package of Sebastian Gibb.

References

Felsenstein, Joseph. Confidence limits on phylogenies: an approach using the Bclust. Evolution (1985): 783-791.

Efron, Bradley, Elizabeth Halloran, and Susan Holmes. Bootstrap confidence levels for phylogenetic trees. Proceedings of the National Academy of Sciences 93.23 (1996): 13429-13429.

See Also

Jclust, link{BootA}

Examples

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

plot(bb$hclust)
Bclabels(bb$hclust, bb$values, col="blue", pos=3, offset=0.1)

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

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

plot(bb$hclust)
Bclabels(bb$hclust, bb$values, method="points", pch=19, cex=bb$values*3)

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

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)

plot(hclust(dist(bb$consensus)), main="Net consensus tree")

bb1 <- Bclust(t(atmospheres), FUN=function(.x) hclust(Gower.dist(.x)), monitor=FALSE)
plot(bb1$hclust)
Bclabels(bb1$hclust, bb1$values, col="green", pos=3, offset=0.1)
# }