shipunov (version 1.5)

Bclust: Bootstrapped hclust

Description

Bootstraps (or jacknife) 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, bootstrap=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

bootstrap

If FALSE (not default), performs jacknife (and makes 'iter=ncol(data)')

Value

List with the following 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). Internally, it uses Hcl2mat() which converts 'hclust' objects into binary matrix of cluster memberships.

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

References

Felsenstein, J. 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution. 783-791.

Efron, B., E. Halloran, and S. Holmes. 1996. Bootstrap confidence levels for phylogenetic trees. Proceedings of the National Academy of Sciences. 93.23: 13429-13429.

See Also

Jclust, BootA, Hcl2mat, Bclabels, Hcoords

Examples

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

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") # net consensus
## majority rule is 'consensus >= 0.5', strict is like 'round(consensus) == 1'

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)

Bclust(t(atmospheres), bootstrap=FALSE) # jacknife
# }