oneclust {oneclust} | R Documentation |
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 |
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)
[Package oneclust version 0.3.0 Index]