Package 'fpc' - open source solution for an Internet free, intelligent ...

28 clusterboot
Arguments
data
B
distances
bootmethod
bscompare
multipleboot
jittertuning
noisetuning
something that can be coerced into a matrix. The data matrix - either an n*p-data
matrix (or data frame) or an n*n-dissimilarity matrix (or dist-object).
integer. Number of resampling runs for each scheme, see bootmethod.
logical. If TRUE, the data is interpreted as dissimilarity matrix. If data is a
dist-object, distances=TRUE automatically, otherwise distances=FALSE by
default. This means that you have to set it to TRUE manually if data is a dissimilarity
matrix.
vector of strings, defining the methods used for resampling. Possible methods:
"boot": nonparametric bootstrap (precise behaviour is controlled by parameters
bscompare and multipleboot).
"subset": selecting random subsets from the dataset. Size determined by
subtuning.
"noise": replacing a certain percentage of the points by random noise, see
noisetuning.
"jitter" add random noise to all points, see jittertuning. (This didn’t perform
well in Hennig (2007), but you may want to get your own experience.)
"bojit" nonparametric bootstrap first, and then adding noise to the points, see
jittertuning.
Important: only the methods "boot" and "subset" work with dissimilarity
data!
The results in Hennig (2007) indicate that "boot" is generally informative and
often quite similar to "subset" and "bojit", while "noise" sometimes provides
different information. Therefore the default (for distances=FALSE) is to
use "boot" and "noise". However, some clustering methods may have problems
with multiple points, which can be solved by using "bojit" or "subset"
instead of "boot" or by multipleboot=FALSE below.
logical. If TRUE, multiple points in the bootstrap sample are taken into account to
compute the Jaccard similarity to the original clusters (which are represented by
their "bootstrap versions", i.e., the points of the original cluster which also occur
in the bootstrap sample). If a point was drawn more than once, it is in the "bootstrap
version" of the original cluster more than once, too, if bscompare=TRUE.
Otherwise (default) multiple points are ignored for the computation of the Jaccard
similarities. If multipleboot=FALSE, it doesn’t make a difference.
logical. If FALSE, all points drawn more than once in the bootstrap draw are only
used once in the bootstrap samples.
positive numeric. Tuning for the "jitter"-method. The noise distribution for
jittering is a normal distribution with zero mean. The covariance matrix has the
same Eigenvectors as that of the original data set, but the standard deviation
along the principal directions is determined by the jittertuning-quantile of
the distances between neighboring points projected along these directions.
A vector of two positive numerics. Tuning for the "noise"-method. The first
component determines the probability that a point is replaced by noise. Noise
is generated by a uniform distribution on a hyperrectangle along the principal directions
of the original data set, ranging from -noisetuning[2] to noisetuning[2]