nhclu_pam {bioregion} | R Documentation |
This function performs non hierarchical clustering on the basis of dissimilarity with partitioning around medoids.
nhclu_pam(
dissimilarity,
index = names(dissimilarity)[3],
n_clust = NULL,
nstart = if (variant == "faster") 1 else NA,
variant = "faster",
cluster_only = FALSE,
...
)
dissimilarity |
the output object from |
index |
name or number of the dissimilarity column to use. By default,
the third column name of |
n_clust |
an |
nstart |
an |
variant |
a |
cluster_only |
a |
... |
you can add here further arguments to be passed to |
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).
A list
of class bioregion.clusters
with five slots:
name: character string
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
Boris Leroy (leroy.boris@gmail.com), Pierre Denelle (pierre.denelle@gmail.com) and Maxime Lenormand (maxime.lenormand@inrae.fr)
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")