oneclust: Maximum homogeneity clustering for one-dimensional data

In nanxstats/oneclust: Maximum Homogeneity Clustering for Univariate Data

Maximum homogeneity clustering for one-dimensional data

Description

Maximum homogeneity clustering for one-dimensional data

Usage

oneclust(x, k, w = NULL, sort = TRUE)

Arguments

x	Numeric vector, samples to be clustered.
k	Integer, number of clusters.
w	Numeric vector, sample weights (optional). Note that the weights here should be sampling weights (for example, a certain proportion of the population), not frequency weights (for example, number of occurrences).
sort	Should we sort x (and w) before clustering? Default is TRUE. Otherwise the order of the data is respected.

Value

A list containing:

• cluster - cluster id of each sample.

• cut - index of the optimal cut points.

References

Fisher, Walter D. 1958. On Grouping for Maximum Homogeneity. Journal of the American Statistical Association 53 (284): 789–98.

Examples

set.seed(42)
x <- sample(c(
  rnorm(50, sd = 0.2),
  rnorm(50, mean = 1, sd = 0.3),
  rnorm(100, mean = -1, sd = 0.25)
))
oneclust(x, 3)

nanxstats/oneclust documentation built on March 14, 2024, 6:27 a.m.