nhclu_pam {bioregion}

R Documentation

Non hierarchical clustering: partitioning around medoids

Description

This function performs non hierarchical clustering on the basis of dissimilarity with partitioning around medoids.

Usage

nhclu_pam(
  dissimilarity,
  index = names(dissimilarity)[3],
  n_clust = NULL,
  nstart = if (variant == "faster") 1 else NA,
  variant = "faster",
  cluster_only = FALSE,
  ...
)

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 a vector of integers specifying the requested number(s) of clusters.
nstart	an integer specifying the number of random “starts” for the pam algorithm. By default, 1 (for the "faster" variant).
variant	a character string specifying the variant of pam to use, by default "faster". Available options are original, o_1, o_2, f_3, f_4, f_5 or fasterSee cluster::pam() for more details.
cluster_only	a boolean specifying if only the clustering should be returned from the cluster::pam() function (more efficient).
...	you can add here further arguments to be passed to pam() (see cluster::pam())

Details

This method partitions data into the chosen number of cluster on the basis of the input dissimilarity matrix. It is more robust than k-means because it minimizes the sum of dissimilarity between cluster centres and points assigned to the cluster - whereas the k-means approach minimizes the sum of squared euclidean distances (thus k-means cannot be applied directly on the input dissimilarity matrix if the distances are not euclidean).

Value

A list of class bioregion.clusters with five slots:

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

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

3. inputs: list of characteristics of the clustering process

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

5. clusters: data.frame containing the clustering results

Author(s)

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

See Also

nhclu_kmeans

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)

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

clust1 <- nhclu_pam(dissim, n_clust = 2:10, index = "Simpson")
clust2 <- nhclu_pam(dissim, n_clust = 2:15, index = "Simpson")
partition_metrics(clust2, dissimilarity = dissim,
eval_metric = "pc_distance")
partition_metrics(clust2, net = comnet, species_col = "Node2",
                   site_col = "Node1", eval_metric = "avg_endemism")
   

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