cut_tree {bioregion}  R Documentation 
This functions is designed to work on a hierarchical tree and cut it
at userselected heights. It works on either outputs from
hclu_hierarclust
or hclust
objects. It cuts the tree for the chosen
number(s) of clusters or selected height(s). It also includes a procedure to
automatically return the height of cut for the chosen number(s) of clusters.
cut_tree(
tree,
n_clust = NULL,
cut_height = NULL,
find_h = TRUE,
h_max = 1,
h_min = 0,
dynamic_tree_cut = FALSE,
dynamic_method = "tree",
dynamic_minClusterSize = 5,
dissimilarity = NULL,
...
)
tree 
a 
n_clust 
an integer or a vector of integers indicating the number of
clusters to be obtained from the hierarchical tree, or the output from

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 
find_h 
a boolean indicating if the height of cut should be found for
the requested 
h_max 
a numeric indicating the maximum possible tree height for
finding the height of cut when 
h_min 
a numeric indicating the minimum possible height in the tree
for finding the height of cut when 
dynamic_tree_cut 
a boolean indicating if the dynamic tree cut method
should be used, in which case 
dynamic_method 
a character vector indicating the method to be used
to dynamically cut the tree: either 
dynamic_minClusterSize 
an integer indicating the minimum cluster size to use in the dynamic tree cut method (see dynamicTreeCut::cutreeDynamic()) 
dissimilarity 
only useful if 
... 
further arguments to be passed to dynamicTreeCut::cutreeDynamic() to customize the dynamic tree cut method. 
The function can cut the tree with two main methods. First, it can cut
the entire tree at the same height (either specified by cut_height
or
automatically defined for the chosen n_clust
). Second, it can use
the dynamic tree cut method (Langfelder et al. 2008), in which
case clusters are detected with an adaptive method based on the shape of
branches in the tree (thus cuts happen at multiple heights depending on
cluster positions in the tree).
The dynamic tree cut method has two variants.
The treebased only variant
(dynamic_method = "tree"
) is a topdown approach which relies only
on the tree and follows the order of clustered objects on it
The hybrid variant
(dynamic_method = "hybrid"
) is a bottomup approach which relies on
both the tree and the dissimilarity matrix to build clusters on the basis of
dissimilarity information among sites. This method is useful to detect
outlying members in each cluster.
If tree
is an output from hclu_hierarclust()
, then the same
object is returned with content updated (i.e., args
and clusters
). If
tree
is a hclust
object, then a data.frame
containing the clusters is
returned.
The argument find_h
is ignored if dynamic_tree_cut = TRUE
,
because heights of cut cannot be estimated in this case.
Pierre Denelle (pierre.denelle@gmail.com), Maxime Lenormand (maxime.lenormand@inrae.fr) and Boris Leroy (leroy.boris@gmail.com)
Langfelder P, Zhang B, Horvath S (2008). “Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R.” BIOINFORMATICS, 24(5), 719–720.
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)
simil < similarity(comat, metric = "all")
dissimilarity < similarity_to_dissimilarity(simil)
# Userdefined number of clusters
tree1 < hclu_hierarclust(dissimilarity, n_clust = 5)
tree2 < cut_tree(tree1, cut_height = .05)
tree3 < cut_tree(tree1, n_clust = c(3, 5, 10))
tree4 < cut_tree(tree1, cut_height = c(.05, .1, .15, .2, .25))
tree5 < cut_tree(tree1, n_clust = c(3, 5, 10), find_h = FALSE)
hclust_tree < tree2$algorithm$final.tree
clusters_2 < cut_tree(hclust_tree, n_clust = 10)
cluster_dynamic < cut_tree(tree1, dynamic_tree_cut = TRUE,
dissimilarity = dissimilarity)