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Divisive hierarchical clustering based on dissimilarity or beta-diversity

Source: R/hclu_diana.R
hclu_diana.Rd

This function computes a divisive hierarchical clustering from a dissimilarity (beta-diversity) data.frame, calculates the cophenetic correlation coefficient, and can generate 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 followed by 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 remaining column(s) contain the dissimilarity indices.

index

The name or number of the dissimilarity column to use. By default, the third column name of dissimilarity is used.

n_clust

An integer vector or a single integer indicating the number of clusters to be obtained from the hierarchical tree, or the output from bioregionalization_metrics. Should not be used concurrently with cut_height.

cut_height

A numeric vector indicating the height(s) at which the tree should be cut. Should not be used concurrently with n_clust.

find_h

A boolean indicating whether the cutting height should be determined for the requested n_clust.

h_max

A numeric value indicating the maximum possible tree height for the chosen index.

h_min

A numeric value indicating the minimum possible height in the tree for the chosen index.

Value

A list of class bioregion.clusters with five slots:

  1. name: A character string containing the name of the algorithm.

  2. args: A list of input arguments as provided by the user.

  3. inputs: A list describing the characteristics of the clustering process.

  4. algorithm: A list containing all objects associated with the clustering procedure, such as the original cluster objects.

  5. clusters: A data.frame containing the clustering results.

Details

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

To find an optimal number of clusters, see bioregionalization_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.

See also

For more details illustrated with a practical example, see the vignette: https://biorgeo.github.io/bioregion/articles/a4_1_hierarchical_clustering.html.

Associated functions: cut_tree

Author

Pierre Denelle (pierre.denelle@gmail.com)
Boris Leroy (leroy.boris@gmail.com)
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 0.51 cophenetic correlation coefficient with the initial dissimilarity matrix