Description Usage Arguments Details Value Author(s) References Examples
View source: R/balance.indices.R
Given a phylogenetic tree, computes Colles-like, Sackin and cophenetic balance indices of that tree.
1 | balance.indices(tree, norm = FALSE, binary.Colless = FALSE)
|
tree |
a single phylogenetic tree. It can be entered as a string in Newick format, as a 'phylo' object ( |
norm |
a logical variable that indicates whether the indices should be normalized or not. |
binary.Colless |
a logical variable FALSE by default. If is TRUE then the classical Colless index is computed (only for binary trees). |
The Colless-like index is the generalization of the Colless' index for non-binary trees (see Mir et al. , 2018).
The Sackin's index is computed as the sum of the number of ancestors for each leave of the tree (see Mir et al. , 2013).
The cophenetic index is computed as the sum of the depths of the least common ancestor (LCA) of every pair of leaves of the tree(see Sackin et al, 1972).
A numeric vector with the three computed balance indices of the tree:
Colless-like
, Sackin
and Cophenetic
values.
Lucia Rotger
A. Mir, F. Rossello, L.Rotger, Sound Colless-like balance indices for multifurcating phylogenetic trees.PloS ONE 13 (2018), e0203401.
A. Mir, F. Rossello, L.Rotger, A new balance index for phylogenetic trees. Mathematical Biosciences 241 (2013), 125-136.
M. J. Sackin, "Good" and "bad" phenograms. Sys. Zool, 21 (1972), 225-226.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Computation of the Colless-Like, Sackin and Cophenetic
# balance indices of trees entered in newick format:
balance.indices("(1,2,3,4,5);")
balance.indices("(1,(2,(3,(4,5))));")
# Computation of the Colless-Like, Sackin and Cophenetic
# balance indices of a tree entered as a phylo object:
require(ape)
random.tree = rtree(5,rooted=TRUE)
balance.indices(random.tree)
# Computation of the Colless-Like, Sackin and Cophenetic
# balance indices of a tree entered as a igraph object.
# The tree is randomly generated from all trees with 5
# leaves following the alpha-gamma model with alpha=0.5
# and gamma=0.3.
a.g.tree = a.g.model(5,0.5,0.3)
balance.indices(a.g.tree)
# All of them can be normalized (values between 0 and 1)
balance.indices("(1,2,3,4,5);",norm=TRUE)
balance.indices("(1,(2,(3,(4,5))));",norm=TRUE)
balance.indices(random.tree,norm=TRUE)
balance.indices(a.g.tree,norm=TRUE)
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