Jclust: Simple bootstrap and jackknife clustering

In shipunov: Miscellaneous Functions from Alexey Shipunov

Simple bootstrap and jackknife clustering

Description

Simple bootstrap and jackknife clustering

Usage

Jclust(data, n.cl, iter=1000, method.d="euclidean", method.c="ward.D",
 bootstrap=TRUE, monitor=TRUE)

## S3 method for class 'Jclust'
print(x, ...)

## S3 method for class 'Jclust'
plot(x, main="", xlab=NULL, rect.lty=3, rect.col=1,
 rect.xpd=TRUE, top=FALSE, lab.pos=3, lab.offset=0.5, lab.col=par("col"),
 lab.font=par("font"), ...)

Arguments

data	Data
n.cl	Number of desired clusters
iter	Number of iterations, default 1000
method.d	Distance method
method.c	Hierarchical clustering method
bootstrap	Bootstrap or jackknife?
monitor	If TRUE (default), prints a dot for each replicate
x	Object of the class 'Jclust'
main	Plot title
xlab	Horizontal axis label
rect.lty	Line type for the rectangles
rect.col	Color of rectangles
rect.xpd	Plot rectangle sides if they go outside the plotting region?
top	Plot values on top?
lab.pos	Position specifier for the values text labels
lab.offset	Distance of the text labels in fractions of a character width
lab.col	Color of the text labels
lab.font	Font of the text labels
...	Additional arguments to the print() or plot.hclust()

Details

Simple method to bootstrap and jackknife cluster memberships, and plot consensus membership tree. Requires the desired number of clusters.

The default clustering method is the variance-minimizing "ward.D" (which works better with Euclidean distances); to make it coherent with hclust() default, specify 'method.c="complete"'.

Note that Jclust() is fast indirect bootstrap, it boostrap the consensus (not the original) tree and narrows results with the desired number of clusters. Please consider also Bclust() which is the direct method, and phylogeny-based BootA().

Value

Returns 'Jclust' object which is a list with components "meth" (bootstrap or jacknife), "mat" (matrix of results, consensus matrix), "hclust" (consensus tree as 'hclust' object), "gr" (groups), "supp" (support values), "iter" (number of iterations) and "n.cl" (number of cluters used.)

Author(s)

Alexey Shipunov

See Also

Bclust,

BootA,

Fence

Examples

## 'moldino' data, 1000 iterations
(mo.j <- Jclust(t(moldino), n.cl=3, iter=1000))
plot(mo.j)

## adjust locations of value labels
data.jb <- Jclust(t(atmospheres), method.c="complete", n.cl=3)
plot(data.jb, top=TRUE, lab.pos=1, lab.offset=1, lab.col=2, lab.font=2)

## plot together with Fence()
iris.jb <- Jclust(iris[, -5], n.cl=3)
plot(iris.jb, labels=FALSE)
Fence(iris.jb$hclust, iris$Species)
legend("topright", legend=levels(iris$Species), col=1:3, lwd=2.5, bty="n")


## This is how one can bootstrap _all_ reliable cluster numbers:
for (i in 2:(nrow(t(moldino)) - 1)) print(Jclust(t(moldino), i, iter=1000, boot=TRUE))

shipunov documentation built on Feb. 16, 2023, 9:05 p.m.