Divisive hierarchical clustering based on dissimilarity or beta-diversity

This function computes a divisive hierarchical clustering from a dissimilarity (beta-diversity) data.frame, calculates the cophenetic correlation coefficient, and can get clusters from the tree if requested by the user. The function implements randomization of the dissimilarity matrix to generate the tree, with a selection method based on the optimal cophenetic correlation coefficient. Typically, the dissimilarity data.frame is a bioregion.pairwise.metric object obtained by running similarity or similarity and then similarity_to_dissimilarity.

Usage

hclu_diana(
  dissimilarity,
  index = names(dissimilarity)[3],
  n_clust = NULL,
  cut_height = NULL,
  find_h = TRUE,
  h_max = 1,
  h_min = 0
)

Arguments

dissimilarity: the output object from dissimilarity() or similarity_to_dissimilarity(), or a dist object. If a data.frame is used, the first two columns represent pairs of sites (or any pair of nodes), and the next column(s) are the dissimilarity indices.
index: name or number of the dissimilarity column to use. By default, the third column name of dissimilarity is used.
n_clust: an integer or an integer vector indicating the number of clusters to be obtained from the hierarchical tree, or the output from partition_metrics. Should not be used at the same time as cut_height.
cut_height: a numeric vector indicating the height(s) at which the tree should be cut. Should not be used at the same time as n_clust.
find_h: a boolean indicating if the height of cut should be found for the requested n_clust.
h_max: a numeric indicating the maximum possible tree height for the chosen index.
h_min: a numeric indicating the minimum possible height in the tree for the chosen index.

Value

A list of class bioregion.clusters with five slots:

name: character containing the name of the algorithm
args: list of input arguments as provided by the user
inputs: list of characteristics of the clustering process
algorithm: list of all objects associated with the clustering procedure, such as original cluster objects
clusters: data.frame containing the clustering results

Details

The function is based on diana. Chapter 6 of Kaufman and Rousseeuw (1990) fully details the functioning of the diana algorithm.

To find an optimal number of clusters, see partition_metrics()

References

Kaufman L, Rousseeuw PJ (2009). “Finding groups in data: An introduction to cluster analysis.” In & Sons. JW (ed.), Finding groups in data: An introduction to cluster analysis..

Author

Pierre Denelle (pierre.denelle@gmail.com), Boris Leroy (leroy.boris@gmail.com) and Maxime Lenormand (maxime.lenormand@inrae.fr)

Examples

comat <- matrix(sample(0:1000, size = 500, replace = TRUE, prob = 1/1:1001),
20, 25)
rownames(comat) <- paste0("Site",1:20)
colnames(comat) <- paste0("Species",1:25)

dissim <- dissimilarity(comat, metric = "all")

data("fishmat")
fishdissim <- dissimilarity(fishmat)
fish_diana <- hclu_diana(fishdissim, index = "Simpson")
#> Output tree has a 0.55 cophenetic correlation coefficient with the initial
#>                    dissimilarity matrix