fpc.clusterboot {ClusBoot}

Resampling according to the methods discussed in Hennig (2007)

Description

Resampling according to the methods discussed in Hennig (2007)

Usage

fpc.clusterboot(
  data,
  B,
  distances = (inherits(data, "dist")),
  bootmethod = "boot",
  bscompare = TRUE,
  multipleboot = FALSE,
  jittertuning = 0.05,
  noisetuning = c(0.05, 4),
  subtuning = floor(nrow(data)/2),
  clustermethod,
  noisemethod = FALSE,
  count = TRUE,
  seed = NULL,
  datatomatrix = TRUE,
  ...
)

Arguments

data

a data matrix or distance object which will be the input to the clustering function

B

number of bootstrap replicates

distances

see ?fpc::clusterboot

bootmethod

see ?fpc::clusterboot

bscompare

see ?fpc::clusterboot

multipleboot

see ?fpc::clusterboot

jittertuning

see ?fpc::clusterboot

noisetuning

see ?fpc::clusterboot

subtuning

see ?fpc::clusterboot

clustermethod

see ?fpc::clusterboot

noisemethod

see ?fpc::clusterboot

count

see ?fpc::clusterboot

seed

see ?fpc::clusterboot

datatomatrix

see ?fpc::clusterboot

...

additional arguments to be sent to the function specified in clustermethod

Value

a list with two components; boot.out contains the computations for clusboot and out contains the clustering solution of the original data set