runKmeans: runKmeans

In JhuangLab/CytoTree: A Toolkit for Flow And Mass Cytometry Data

Description

Perform k-means clustering on a data matrix.

Usage

runKmeans(
  object,
  k = 25,
  iter.max = 10,
  nstart = 1,
  algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
  trace = FALSE,
  scale = FALSE,
  verbose = FALSE,
  ...
)

Arguments

object	a CYT object
k	numeric. The number of clusters.
iter.max	numeric. The maximum number of iterations allowed.
nstart	numeric. If k is a number, how many random sets should be chosen.
algorithm	character. Type of algorithm that will be choosen to calculate kmeans. Four algoritms are provided: Hartigan-Wong, Lloyd, Forgy, MacQueen.
trace	logical or integer number.
scale	logical. Whether to use scaled data in kmeans.
verbose	logical. Whether to print calculation progress.
...	Parameters passing to kmeans function

Value

a CYT object with kmeans.id in meta.data

See Also

kmeans

Examples

cyt.file <- system.file("extdata/cyt.rds", package = "CytoTree")
cyt <- readRDS(file = cyt.file)

cyt <- runKmeans(cyt, k = 25, verbose = TRUE)

JhuangLab/CytoTree documentation built on Nov. 16, 2020, 7:23 a.m.