select: Clustering selection

In adjclust: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix

Clustering selection

Description

Clustering selection from a chac object with the slope heuristic or the broken stick heuristic

Usage

select(
  x,
  type = c("capushe", "bstick"),
  k.max = NULL,
  graph = FALSE,
  pct = 0.15
)

Arguments

x	an object of class 'chac'
type	model selection approach between slope heuristic ("capushe") and broken stick approach ("bstick")
k.max	maximum number of clusters that can be selected. Default to NULL, in which case it is set to \min(\max(100, \frac{n}{\log(n)}), \frac{n}{2}) where n is the number of objects to be clustered for capushe and to n for the broken stick model
graph	logical. Whether the diagnostic plot for the capushe selection is displayed or not. Default to FALSE
pct	minimum percentage of points for the plateau selection in capushe selection. See DDSE for further details

Value

The function returns the clustering selected by the slope heuristic, as implemented in the R package capushe.

References

Baudry J.P., Maugis C., and Michel B. (2012). Slope heuristics: overview and implementation. Statistics and Computing, 22(2), 355-470. MacArthur, R.H. (1957) On the relative abundance of bird species. Proceedings of the National Academy of Sciences, 43, 293-295.

Examples

## Not run: if (require("HiTC", quietly = TRUE)) {
  load(system.file("extdata", "hic_imr90_40_XX.rda", package = "adjclust"))
  res <- hicClust(hic_imr90_40_XX, log = TRUE)
  selected.capushe <- select(res)
  table(selected.capushe)
  selected.bs <- select(res, type = "bstick")
  table(selected.bs)
}
## End(Not run)

res <- adjClust(dist(iris[, 1:4]))
select.clust <- select(res, "bs")
table(select.clust)

adjclust documentation built on Oct. 8, 2024, 9:07 a.m.