BootA function

Bootstrap clustering

How to bootstrap clustering with 'ape'

BootA(dat, FUN=function(.x) ape::nj(dist(.x)), iter=1000, mc.cores=1, tresh=50,
 cons=TRUE, prop=0.5)

Arguments

• dat: data
• FUN: how to bootstrap (see examples)
• iter: number of iterations, default 1000
• mc.cores: how many cores to employ (system-dependent)
• tresh: Threshold for printing bootstrap values
• cons: Calculate consensus tree?
• prop: 0.5 is majority-rule consensus (default), 1 is strict consensus

Details

This is how to bootstrap clustering with 'ape::boot.phylo()'.

Author(s)

Alexey Shipunov

See Also

Bclust, BootA, ape::boot.phylo

Examples

dat <- iris[, -5]
row.names(dat) <- abbreviate(make.names(iris[, 5], unique=TRUE))
iris.BA1 <- BootA(dat, iter=100)
plot(iris.BA1$boot.tree, show.node.label=TRUE)
plot(iris.BA1$cons.tree)
iris.BA2 <- BootA(dat, FUN=function(.x) ape::as.phylo(hclust(dist(.x))), iter=100)
## Not run:

## change (or remove) 'mc.cores=...' in accordance with your system features
iris.BA3 <- BootA(dat, FUN=function(.x) phangorn::NJ(dist(.x)), iter=100,
 mc.cores=4)
## End(Not run)