Package 'TreeTools'

Description

AddTip() adds a tip to a phylogenetic tree at a specified location.

Usage

AddTip(
  tree,
  where = sample.int(tree[["Nnode"]] * 2 + 2L, size = 1) - 1L,
  label = "New tip",
  nodeLabel = "",
  edgeLength = 0,
  lengthBelow = NULL,
  nTip = NTip(tree),
  nNode = tree[["Nnode"]],
  rootNode = RootNode(tree)
)

AddTipEverywhere(tree, label = "New tip", includeRoot = FALSE)

Arguments

Details

AddTip() extends bind.tree, which cannot handle single-taxon trees.

AddTipEverywhere() adds a tip to each edge in turn.

Value

AddTip() returns a tree of class phylo with an additional tip at the desired location.

AddTipEverywhere() returns a list of class multiPhylo containing the trees produced by adding label to each edge of tree in turn.

Author(s)

Martin R. Smith ([email protected])

See Also

Add one tree to another: bind.tree()

Other tree manipulation: CollapseNode(), ConsensusWithout(), DropTip(), ImposeConstraint(), KeptPaths(), KeptVerts(), LeafLabelInterchange(), MakeTreeBinary(), Renumber(), RenumberTips(), RenumberTree(), RootTree(), SortTree(), Subtree(), TipTimedTree(), TrivialTree

Examples

tree <- BalancedTree(10)

# Add a leaf below an internal node
plot(tree)
ape::nodelabels() # Identify node numbers 
node <- 15        # Select location to add leaf
ape::nodelabels(bg = ifelse(NodeNumbers(tree) == node, "green", "grey"))

plot(AddTip(tree, 15, "NEW_TIP"))

# Add edge lengths for an ultrametric tree
tree$edge.length <- rep(c(rep(1, 5), 2, 1, 2, 2), 2)

# Add a leaf to an external edge
leaf <- 5
plot(tree)
ape::tiplabels(bg = ifelse(seq_len(NTip(tree)) == leaf, "green", "grey"))

plot(AddTip(tree, 5, "NEW_TIP", edgeLength = NULL))

# Create a polytomy, rather than a new node
plot(AddTip(tree, 5, "NEW_TIP", edgeLength = NA))

# Set up multi-panel plot
oldPar <- par(mfrow = c(2, 4), mar = rep(0.3, 4), cex = 0.9)

# Add leaf to each edge on a tree in turn
backbone <- BalancedTree(4)
# Treating the position of the root as instructive:
additions <- AddTipEverywhere(backbone, includeRoot = TRUE)
xx <- lapply(additions, plot)

par(mfrow = c(2, 3))
# Don't treat root edges as distinct:
additions <- AddTipEverywhere(backbone, includeRoot = FALSE)
xx <- lapply(additions, plot)

# Restore original plotting parameters
par(oldPar)

Description

ApeTime() reads the time that a tree written with "ape" was modified, based on the comment in the Nexus file.

Usage

ApeTime(filepath, format = "double")

Arguments

Value

ApeTime() returns the time that the specified file was created by ape, in the format specified by format.

Author(s)

Martin R. Smith ([email protected])

Description

Remove tokens that do not occur in a fossil "template" taxon from a living taxon, to simulate the process of fossilization in removing data from a phylogenetic dataset.

Usage

ArtificialExtinction(
  dataset,
  subject,
  template,
  replaceAmbiguous = "ambig",
  replaceCoded = "original",
  replaceAll = TRUE,
  sampleFrom = NULL
)

## S3 method for class 'matrix'
ArtificialExtinction(
  dataset,
  subject,
  template,
  replaceAmbiguous = "ambig",
  replaceCoded = "original",
  replaceAll = TRUE,
  sampleFrom = NULL
)

## S3 method for class 'phyDat'
ArtificialExtinction(
  dataset,
  subject,
  template,
  replaceAmbiguous = "ambig",
  replaceCoded = "original",
  replaceAll = TRUE,
  sampleFrom = NULL
)

ArtEx(
  dataset,
  subject,
  template,
  replaceAmbiguous = "ambig",
  replaceCoded = "original",
  replaceAll = TRUE,
  sampleFrom = NULL
)

Arguments

Details

Further details are provided in Asher and Smith (2022).

Note: this simple implementation does not account for character contingency, e.g. characters whose absence imposes inapplicable or absent tokens on dependent characters.

Value

A dataset with the same class as dataset in which entries that are ambiguous in template are made ambiguous in subject.

Author(s)

Martin R. Smith ([email protected])

References

Asher R, Smith MR (2022). “Phylogenetic signal and bias in paleontology.” Systematic Biology, 71(4), 986–1008. doi:10.1093/sysbio/syab072.

Examples

set.seed(1)
dataset <- matrix(c(sample(0:2, 4 * 8, TRUE),
                    "0", "0", rep("?", 6)), nrow = 5,
                    dimnames = list(c(LETTERS[1:4], "FOSSIL"),
                                    paste("char", 1:8)), byrow = TRUE)
artex <- ArtificialExtinction(dataset, c("A", "C"), "FOSSIL")

Description

Converts representations of phylogenetic trees to an object of the "ape" class multiPhylo.

Usage

as.multiPhylo(x)

## S3 method for class 'phylo'
as.multiPhylo(x)

## S3 method for class 'list'
as.multiPhylo(x)

## S3 method for class 'phyDat'
as.multiPhylo(x)

## S3 method for class 'Splits'
as.multiPhylo(x)

Arguments

Value

as.multiPhylo returns an object of class multiPhylo

as.multiPhylo.phyDat() returns a list of trees, each corresponding to the partitions implied by each non-ambiguous character in x.

See Also

Other utility functions: ClusterTable, ClusterTable-methods, Hamming(), MSTEdges(), SampleOne(), TipTimedTree(), UnshiftTree(), match,phylo,phylo-method, sapply64(), sort.multiPhylo()

Examples

as.multiPhylo(BalancedTree(8))
as.multiPhylo(list(BalancedTree(8), PectinateTree(8)))
data("Lobo")
as.multiPhylo(Lobo.phy)

Description

as.Newick() creates a character string representation of a phylogenetic tree, in the Newick format, using R's internal tip numbering. Use RenumberTips() to ensure that the internal numbering follows the order you expect.

Usage

as.Newick(x)

## S3 method for class 'phylo'
as.Newick(x)

## S3 method for class 'list'
as.Newick(x)

## S3 method for class 'multiPhylo'
as.Newick(x)

Arguments

Value

as.Newick() returns a character string representing tree in Newick format.

Author(s)

Martin R. Smith ([email protected])

See Also

• Retain leaf labels: NewickTree()

• Change R's internal numbering of leaves: RenumberTips()

• Write tree to text or file: ape::write.tree()

Examples

trees <- list(BalancedTree(1:8), PectinateTree(8:1))
trees <- lapply(trees, RenumberTips, 1:8)
as.Newick(trees)

Description

A list of eleven Brewer palettes containing one to eleven colours that are readily distinguished by colourblind viewers, followed by a twelfth 12-colour palette adapted for colour blindness.

Usage

brewer

Format

An object of class list of length 12.

Source

• ColourBrewer2.org

• Martin Krzywinski

Examples

data("brewer", package = "TreeTools")
plot(0, type = "n", xlim = c(1, 12), ylim = c(12, 1),
     xlab = "Colour", ylab="Palette")
for (i in seq_along(brewer)) text(seq_len(i), i, col = brewer[[i]])

Description

CharacterInformation() calculates the cladistic information content (Steel and Penny 2006) of a given character, in bits. The total information in all characters gives a measure of the potential utility of a dataset (Cotton and Wilkinson 2008), which can be compared with a profile parsimony score (Faith and Trueman 2001) to evaluate the degree of homoplasy within a dataset.

Usage

CharacterInformation(tokens)

Arguments

Value

CharacterInformation() returns a numeric specifying the phylogenetic information content of the character (sensu Steel and Penny 2006), in bits.

Author(s)

Martin R. Smith ([email protected])

References

Cotton JA, Wilkinson M (2008). “Quantifying the potential utility of phylogenetic characters.” Taxon, 57(1), 131–136.

Faith DP, Trueman JWH (2001). “Towards an inclusive philosophy for phylogenetic inference.” Systematic Biology, 50(3), 331–350. doi:10.1080/10635150118627.

Steel MA, Penny D (2006). “Maximum parsimony and the phylogenetic information in multistate characters.” In Albert VA (ed.), Parsimony, Phylogeny, and Genomics, 163–178. Oxford University Press, Oxford.

See Also

Other split information functions: SplitInformation(), SplitMatchProbability(), TreesMatchingSplit(), UnrootedTreesMatchingSplit()

Description

Cherries() counts the number of vertices in a binary tree whose children are both leaves.

Usage

Cherries(tree, nTip)

## S3 method for class 'phylo'
Cherries(tree, nTip = NTip(tree))

## S3 method for class 'numeric'
Cherries(tree, nTip)

Arguments

Value

Cherries() returns an integer specifying the number of nodes whose children are both leaves.

Equations for the number of cherries expected under uniform and Yule tree models are derived by McKenzie and Steel (2000).

Author(s)

Martin R. Smith ([email protected])

References

McKenzie A, Steel M (2000). “Distributions of Cherries for Two Models of Trees.” Mathematical Biosciences, 164(1), 81–92. doi:10.1016/S0025-5564(99)00060-7.

See Also

Other tree properties: ConsensusWithout(), EdgeRatio(), LongBranch(), MatchEdges(), NSplits(), NTip(), NodeNumbers(), PathLengths(), SplitsInBinaryTree(), TipLabels(), TreeIsRooted(), Treeness()

Description

CladeSizes() reports the number of nodes in each clade in a tree.

Usage

CladeSizes(tree, internal = FALSE, nodes = NULL)

Arguments

Value

CladeSizes() returns the number of nodes (including leaves) that are descended from each node, not including the node itself.

See Also

Other tree navigation: AncestorEdge(), DescendantEdges(), EdgeAncestry(), EdgeDistances(), ListAncestors(), MRCA(), MatchEdges(), NDescendants(), NodeDepth(), NodeNumbers(), NodeOrder(), PaintTree(), RootNode()

Examples

tree <- BalancedTree(6)
plot(tree)
ape::nodelabels()
CladeSizes(tree, nodes = c(1, 8, 9))

Description

CladisticInfo() calculates the cladistic (phylogenetic) information content of a phylogenetic object, sensu Thorley et al. (1998).

Usage

CladisticInfo(x)

## S3 method for class 'phylo'
CladisticInfo(x)

## S3 method for class 'Splits'
CladisticInfo(x)

## S3 method for class 'list'
CladisticInfo(x)

## S3 method for class 'multiPhylo'
CladisticInfo(x)

CladisticInformation(x)

Arguments

Details

The CIC is the logarithm of the number of binary trees that include the specified topology. A base two logarithm gives an information content in bits.

The CIC was originally proposed by Rohlf (1982), and formalised, with an information-theoretic justification, by Thorley et al. (1998). Steel and Penny (2006) term the equivalent quantity "phylogenetic information content" in the context of individual characters.

The number of binary trees consistent with a cladogram provides a more satisfactory measure of the resolution of a tree than simply counting the number of edges resolved (Page 1992).

Value

CladisticInfo() returns a numeric giving the cladistic information content of the input tree(s), in bits. If passed a Splits object, it returns the information content of each split in turn.

Author(s)

Martin R. Smith ([email protected])

References

Page RD (1992). “Comments on the information content of classifications.” Cladistics, 8(1), 87–95. doi:10.1111/j.1096-0031.1992.tb00054.x.

Rohlf FJ (1982). “Consensus indices for comparing classifications.” Mathematical Biosciences, 59(1), 131–144. doi:10.1016/0025-5564(82)90112-2.

Steel MA, Penny D (2006). “Maximum parsimony and the phylogenetic information in multistate characters.” In Albert VA (ed.), Parsimony, Phylogeny, and Genomics, 163–178. Oxford University Press, Oxford.

Thorley JL, Wilkinson M, Charleston M (1998). “The information content of consensus trees.” In Rizzi A, Vichi M, Bock H (eds.), Advances in Data Science and Classification, 91–98. Springer, Berlin. ISBN 978-3-540-64641-9. doi:10.1007/978-3-642-72253-0.

See Also

Other tree information functions: NRooted(), TreesMatchingTree()

Other tree characterization functions: Consensus(), J1Index(), Stemwardness, TotalCopheneticIndex()

Description

as.ClusterTable() converts a phylogenetic tree to a ClusterTable object, which is an internal representation of its splits suitable for rapid tree distance calculation (per Day, 1985).

Usage

as.ClusterTable(x, tipLabels = NULL, ...)

## S3 method for class 'phylo'
as.ClusterTable(x, tipLabels = NULL, ...)

## S3 method for class 'list'
as.ClusterTable(x, tipLabels = NULL, ...)

## S3 method for class 'multiPhylo'
as.ClusterTable(x, tipLabels = NULL, ...)

Arguments

Details

Each row of a cluster table relates to a clade on a tree rooted on tip 1. Tips are numbered according to the order in which they are visited in preorder: i.e., if plotted using plot(x), from the top of the page downwards. A clade containing the tips 2 .. 5 would be denoted by the entry ⁠2, 5⁠, in either row 2 or row 5 of the cluster table.

Value

as.ClusterTable() returns an object of class ClusterTable, or a list thereof.

Author(s)

Martin R. Smith ([email protected])

References

Day WHE (1985). “Optimal algorithms for comparing trees with labeled leaves.” Journal of Classification, 2(1), 7–28. doi:10.1007/BF01908061.

See Also

S3 methods for ClusterTable objects.

Other utility functions: ClusterTable-methods, Hamming(), MSTEdges(), SampleOne(), TipTimedTree(), UnshiftTree(), as.multiPhylo(), match,phylo,phylo-method, sapply64(), sort.multiPhylo()

Examples

tree1 <- ape::read.tree(text = "(A, (B, (C, (D, E))));");
tree2 <- ape::read.tree(text = "(A, (B, (D, (C, E))));");
ct1 <- as.ClusterTable(tree1)
summary(ct1)
as.matrix(ct1)

# Tip label order must match ct1 to allow comparison
ct2 <- as.ClusterTable(tree2, tipLabels = LETTERS[1:5])

# It can thus be safer to use
ctList <- as.ClusterTable(c(tree1, tree2))
ctList[[2]]

Description

S3 methods for ClusterTable objects.

Usage

## S3 method for class 'ClusterTable'
as.matrix(x, ...)

## S3 method for class 'ClusterTable'
print(x, ...)

## S3 method for class 'ClusterTable'
summary(object, ...)

Arguments

Author(s)

Martin R. Smith ([email protected])

See Also

Other utility functions: ClusterTable, Hamming(), MSTEdges(), SampleOne(), TipTimedTree(), UnshiftTree(), as.multiPhylo(), match,phylo,phylo-method, sapply64(), sort.multiPhylo()

Examples

clustab <- as.ClusterTable(TreeTools::BalancedTree(6))
as.matrix(clustab)
print(clustab)
summary(clustab)

Description

Collapses specified nodes or edges on a phylogenetic tree, resulting in polytomies.

Usage

CollapseNode(tree, nodes)

## S3 method for class 'phylo'
CollapseNode(tree, nodes)

CollapseEdge(tree, edges)

Arguments

Value

CollapseNode() and CollapseEdge() return a tree of class phylo, corresponding to tree with the specified nodes or edges collapsed. The length of each dropped edge will (naively) be added to each descendant edge.

Author(s)

Martin R. Smith

See Also

Other tree manipulation: AddTip(), ConsensusWithout(), DropTip(), ImposeConstraint(), KeptPaths(), KeptVerts(), LeafLabelInterchange(), MakeTreeBinary(), Renumber(), RenumberTips(), RenumberTree(), RootTree(), SortTree(), Subtree(), TipTimedTree(), TrivialTree

Examples

oldPar <- par(mfrow = c(3, 1), mar = rep(0.5, 4))

tree <- as.phylo(898, 7)
tree$edge.length <- 11:22
plot(tree)
nodelabels()
edgelabels()
edgelabels(round(tree$edge.length, 2),
           cex = 0.6, frame = "n", adj = c(1, -1))

# Collapse by node number
newTree <- CollapseNode(tree, c(12, 13))
plot(newTree)
nodelabels()
edgelabels(round(newTree$edge.length, 2),
           cex = 0.6, frame = "n", adj = c(1, -1))

# Collapse by edge number
newTree <- CollapseEdge(tree, c(2, 4))
plot(newTree)

par(oldPar)

Description

Consensus() calculates the majority-rule or strict consensus of a set of trees, using the cluster-table approach of (Day 1985).

Usage

Consensus(trees, p = 1, check.labels = TRUE, hash = TRUE)

Arguments

Details

The strict consensus (p = 1) compares the clusters of the first tree against every other tree in linear time. The majority-rule and threshold consensus (⁠0.5 <= p < 1⁠) instead count the frequency of every split across all trees in a single pass and retain those occurring in a proportion p or more of trees (i.e. in at least ceiling(p * length(trees)) trees); this runs in time linear in the number of trees, after (Jansson et al. 2016). The majority threshold p = 0.5 is strict: a split is retained only if it occurs in more than half the trees, so that two conflicting splits can never both be reported. By default the count uses a 128-bit hash, whose results are exact with overwhelming probability; set hash = FALSE for a slower but guaranteed-exact count.

Value

Consensus() returns an object of class phylo, rooted as in the first entry of trees.

Author(s)

Martin R. Smith ([email protected])

References

Day WHE (1985). “Optimal algorithms for comparing trees with labeled leaves.” Journal of Classification, 2(1), 7–28. doi:10.1007/BF01908061.

Jansson J, Shen C, Sung W (2016). “Improved algorithms for constructing consensus trees.” Journal of the ACM, 63(3), 28:1–28:24. doi:10.1145/2898436.

See Also

• ConsTree implements other consensus tree algorithms.

• Rogue increases the resolution of consensus trees by dropping wildcard taxa.

• TreeDist::ConsensusInfo() calculates the information content of a consensus tree.

Other consensus tree functions: ConsensusWithout(), RoguePlot()

Other tree characterization functions: CladisticInfo(), J1Index(), Stemwardness, TotalCopheneticIndex()

Examples

Consensus(as.phylo(0:2, 8))

Description

ConsensusWithout() displays a consensus plot with specified taxa excluded, which can be a useful way to increase the resolution of a consensus tree when a few wildcard taxa obscure a consistent set of relationships. MarkMissing() adds missing taxa as loose leaves on the plot.

Usage

ConsensusWithout(trees, tip = character(0), ...)

## S3 method for class 'phylo'
ConsensusWithout(trees, tip = character(0), ...)

## S3 method for class 'multiPhylo'
ConsensusWithout(trees, tip = character(0), ...)

## S3 method for class 'list'
ConsensusWithout(trees, tip = character(0), ...)

MarkMissing(tip, position = "bottomleft", ...)

Arguments

Value

ConsensusWithout() returns a consensus tree (of class phylo) without the excluded taxa.

MarkMissing() provides a null return, after plotting the specified tips as a legend.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree manipulation: AddTip(), CollapseNode(), DropTip(), ImposeConstraint(), KeptPaths(), KeptVerts(), LeafLabelInterchange(), MakeTreeBinary(), Renumber(), RenumberTips(), RenumberTree(), RootTree(), SortTree(), Subtree(), TipTimedTree(), TrivialTree

Other tree properties: Cherries(), EdgeRatio(), LongBranch(), MatchEdges(), NSplits(), NTip(), NodeNumbers(), PathLengths(), SplitsInBinaryTree(), TipLabels(), TreeIsRooted(), Treeness()

Other consensus tree functions: Consensus(), RoguePlot()

Examples

oldPar <- par(mfrow = c(1, 2), mar = rep(0.5, 4))

# Two trees differing only in placement of tip 2:
trees <- as.phylo(c(0, 53), 6)
plot(trees[[1]])
plot(trees[[2]])

# Strict consensus (left panel) lacks resolution:
plot(ape::consensus(trees))

# But omitting tip two (right panel) reveals shared structure in common:
plot(ConsensusWithout(trees, "t2"))
MarkMissing("t2")

par(oldPar)

Description

Constructs an approximation to a neighbour-joining tree, modified in order to be consistent with a constraint. Zero-length branches are collapsed at random.

Usage

ConstrainedNJ(dataset, constraint, weight = 1L, ratio = TRUE, ambig = "mean")

Arguments

Value

ConstrainedNJ() returns a tree of class phylo.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree generation functions: GenerateTree, NJTree(), TreeNumber, TrivialTree

Examples

dataset <- MatrixToPhyDat(matrix(
  c(0, 1, 1, 1, 0, 1,
    0, 1, 1, 0, 0, 1), ncol = 2,
  dimnames = list(letters[1:6], NULL)))
constraint <- MatrixToPhyDat(
  c(a = 0, b = 0, c = 0, d = 0, e = 1, f = 1))
plot(ConstrainedNJ(dataset, constraint))

Description

Decompose() decomposes additive characters into a series of binary characters, which is mathematically equivalent when analysed under equal weights parsimony. (This equivalence is not exact under implied weights or under probabilistic tree inference methods.)

Usage

Decompose(dataset, indices)

Arguments

Details

An ordered (additive) character can be rewritten as a mathematically equivalent hierarchy of binary neomorphic characters (Farris et al. 1970). Two reasons to prefer the latter approach are:

• It makes explicit the evolutionary assumptions underlying an ordered character, whether the underlying ordering is linear, reticulate or branched (Mabee 1989).

• It avoids having to identify characters requiring special treatment to phylogenetic software, which requires the maintenance of an up-to-date log of which characters are treated as additive and which sequence their states occur in, a step that may be overlooked by re-users of the data.

Careful consideration is warranted when evaluating whether a group of related characteristics ought to be treated as ordered (Wilkinson 1992). On the one hand, the 'principle of indifference' states that we should treat all transformations as equally probable (/ surprising / informative); ordered characters fail this test, as larger changes are treated as less probable than smaller ones. On the other hand, ordered characters allow more opportunities for homology of different character states, and might thus be defended under the auspices of Hennig's Auxiliary Principle (Wilkinson 1992).

For a case study of how ordering phylogenetic characters can affect phylogenetic outcomes in practice, see Brady et al. (2024).

Value

Decompose() returns a phyDat object in which the specified ordered characters have been decomposed into binary characters. The attribute originalIndex lists the index of the character in dataset to which each element corresponds.

Author(s)

Martin R. Smith ([email protected])

References

Brady PL, Castrellon Arteaga A, López-Torres S, Springer MS (2024). “The Effects of Ordered Multistate Morphological Characters on Phylogenetic Analyses of Eutherian Mammals.” Journal of Mammalian Evolution, 31(3), 28. doi:10.1007/s10914-024-09727-2.

Farris JS, Kluge AG, Eckardt MJ (1970). “A Numerical Approach to Phylogenetic Systematics.” Systematic Biology, 19(2), 172–189. doi:10.2307/2412452.

Mabee PM (1989). “Assumptions Underlying the Use of Ontogenetic Sequences for Determining Character State Order.” Transactions of the American Fisheries Society, 118(2), 151–158. doi:10.1577/1548-8659(1989)118<0151:AUTUOO>2.3.CO;2.

Wilkinson M (1992). “Ordered versus Unordered Characters.” Cladistics, 8(4), 375–385. doi:10.1111/j.1096-0031.1992.tb00079.x.

See Also

Other phylogenetic matrix conversion functions: MatrixToPhyDat(), NexusTokensToInteger(), Reweight(), StringToPhyDat()

Examples

data("Lobo")

# Identify character 11 as additive
# Character 11 will be replaced with two characters
# The present codings 0, 1 and 2 will be replaced with 00, 10, and 11.
decomposed <- Decompose(Lobo.phy, 11)

NumberOfChars <- function(x) sum(attr(x, "weight"))
NumberOfChars(Lobo.phy)   # 115 characters in original
NumberOfChars(decomposed) # 116 characters in decomposed

Description

DescendantEdges() efficiently identifies edges that are "descended" from edges in a tree.

DescendantTips() efficiently identifies leaves (external nodes) that are "descended" from edges in a tree.

Usage

DescendantEdges(
  parent,
  child,
  edge = NULL,
  node = NULL,
  nEdge = length(parent),
  includeSelf = TRUE
)

DescendantTips(parent, child, edge = NULL, node = NULL, nEdge = length(parent))

Arguments

Value

DescendantEdges() returns a logical vector stating whether each edge in turn is the specified edge (if includeSelf = TRUE) or one of its descendants.

DescendantTips() returns a logical vector stating whether each leaf in turn is a descendant of the specified edge.

See Also

Other tree navigation: AncestorEdge(), CladeSizes(), EdgeAncestry(), EdgeDistances(), ListAncestors(), MRCA(), MatchEdges(), NDescendants(), NodeDepth(), NodeNumbers(), NodeOrder(), PaintTree(), RootNode()

Examples

tree <- as.phylo(0, 6)
plot(tree)
desc <- DescendantEdges(tree$edge[, 1], tree$edge[, 2], edge = 5)
which(desc)
ape::edgelabels(bg = 3 + desc)
tips <- DescendantTips(tree$edge[, 1], tree$edge[, 2], edge = 5)
which(tips)
tiplabels(bg = 3 + tips)

Description

Calculate the double factorial of a number, or its logarithm.

Usage

DoubleFactorial(n)

DoubleFactorial64(n)

LnDoubleFactorial(n)

Log2DoubleFactorial(n)

LogDoubleFactorial(n)

LnDoubleFactorial.int(n)

LogDoubleFactorial.int(n)

Arguments

Value

Returns the double factorial, n * (n - 2) * (n - 4) * (n - 6) * ...

Functions

• DoubleFactorial64(): Returns the exact double factorial as a 64-bit integer64, for n < 34.

• LnDoubleFactorial(): Returns the logarithm of the double factorial.

• Log2DoubleFactorial(): Returns the logarithm of the double factorial.

• LnDoubleFactorial.int(): Slightly faster, when x is known to be length one and below 50001

Author(s)

Martin R. Smith ([email protected])

See Also

Other double factorials: doubleFactorials, logDoubleFactorials

Examples

DoubleFactorial (-4:0) # Return 1 if n < 2
DoubleFactorial (2) # 2
DoubleFactorial (5) # 1 * 3 * 5
exp(LnDoubleFactorial.int (8)) # log(2 * 4 * 6 * 8)
DoubleFactorial64(31)

Description

A vector with pre-calculated values of double factorials up to 300!!, and the logarithms of double factorials up to 50 000!!.

Usage

doubleFactorials

Format

An object of class numeric of length 300.

Details

301!! is too large to store as an integer; use logDoubleFactorials instead.

See Also

Other double factorials: DoubleFactorial(), logDoubleFactorials

Description

DropTip() removes specified leaves from a phylogenetic tree, collapsing incident branches.

Usage

DropTip(tree, tip, preorder = TRUE, check = TRUE)

KeepTip(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class 'phylo'
DropTip(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class 'phylo'
KeepTip(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class 'Splits'
KeepTip(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class 'Splits'
DropTip(tree, tip, preorder, check = TRUE)

DropTipPhylo(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class 'multiPhylo'
DropTip(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class 'multiPhylo'
KeepTip(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class 'list'
DropTip(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class 'list'
KeepTip(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class ''NULL''
DropTip(tree, tip, preorder = TRUE, check = TRUE)

## S3 method for class ''NULL''
KeepTip(tree, tip, preorder = TRUE, check = TRUE)

KeepTipPreorder(tree, tip)

KeepTipPostorder(tree, tip)

Arguments

Details

This function differs from ape::drop.tip(), which roots unrooted trees, and which can crash when trees' internal numbering follows unexpected schema.

Value

DropTip() returns a tree of class phylo, with the requested leaves removed. The edges of the tree will be numbered in preorder, but their sequence may not conform to the conventions of Preorder().

KeepTip() returns tree with all leaves not in tip removed, in preorder.

Functions

• DropTipPhylo(): Direct call to DropTip.phylo(), to avoid overhead of querying object's class.

• KeepTipPreorder(): Faster version with no checks. Does not retain labels or edge weights. Edges must be listed in preorder. May crash if improper input is specified.

• KeepTipPostorder(): Faster version with no checks. Does not retain labels or edge weights. Edges must be listed in postorder. May crash if improper input is specified.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree manipulation: AddTip(), CollapseNode(), ConsensusWithout(), ImposeConstraint(), KeptPaths(), KeptVerts(), LeafLabelInterchange(), MakeTreeBinary(), Renumber(), RenumberTips(), RenumberTree(), RootTree(), SortTree(), Subtree(), TipTimedTree(), TrivialTree

Other split manipulation functions: SplitConsistent(), Subsplit(), TrivialSplits()

Examples

tree <- BalancedTree(9)
plot(tree)
plot(DropTip(tree, c("t5", "t6")))

unrooted <- UnrootTree(tree)
plot(unrooted)
plot(DropTip(unrooted, 4:5))

summary(DropTip(as.Splits(tree), 4:5))

Description

Wrapper for internal C++ function for maximum efficiency. Improper input may crash R. Behaviour not guaranteed. It is advisable to contact the package maintainers before relying on this function.

Usage

edge_to_splits(
  edge,
  edgeOrder,
  tipLabels = NULL,
  asSplits = TRUE,
  nTip = NTip(edge),
  ...
)

Arguments

Value

edge_to_splits() uses the same return format as as.Splits().

See Also

as.Splits() offers a safe access point to this function that should be suitable for most users.

Description

Quickly identify edges that are "ancestral" to a particular edge in a tree.

Usage

EdgeAncestry(edge, parent, child, stopAt = (parent == min(parent)))

Arguments

Value

EdgeAncestry() returns a logical vector stating whether each edge in turn is a descendant of the specified edge.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeDistances(), ListAncestors(), MRCA(), MatchEdges(), NDescendants(), NodeDepth(), NodeNumbers(), NodeOrder(), PaintTree(), RootNode()

Examples

tree <- PectinateTree(6)
plot(tree)
ape::edgelabels()
parent <- tree$edge[, 1]
child <- tree$edge[, 2]
EdgeAncestry(7, parent, child)
which(EdgeAncestry(7, parent, child, stopAt = 4))

Description

Number of nodes that must be traversed to navigate from each edge to each other edge within a tree

Usage

EdgeDistances(tree)

Arguments

Value

EdgeDistances() returns a symmetrical matrix listing the number of edges that must be traversed to travel from each numbered edge to each other. The two edges straddling the root of a rooted tree are treated as a single edge. Add a "root" tip using AddTip() if the position of the root is significant.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeAncestry(), ListAncestors(), MRCA(), MatchEdges(), NDescendants(), NodeDepth(), NodeNumbers(), NodeOrder(), PaintTree(), RootNode()

Examples

tree <- BalancedTree(5)
plot(tree)
ape::edgelabels()

EdgeDistances(tree)

Description

Reports the ratio of tree length associated with external edges (i.e. edges whose child is a leaf) and internal edges. Where tree length is dominated by internal edges, variation between tips is dominantly controlled by phylogenetic history.

Usage

EdgeRatio(x)

## S3 method for class 'phylo'
EdgeRatio(x)

Arguments

Value

EdgeRatio() returns a numeric specifying the ratio of external to internal edge length (> 1 means the length of a tree is predominantly in external edges), with attributes external, internal, and total specifying the total length associated with edges of that nature.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree properties: Cherries(), ConsensusWithout(), LongBranch(), MatchEdges(), NSplits(), NTip(), NodeNumbers(), PathLengths(), SplitsInBinaryTree(), TipLabels(), TreeIsRooted(), Treeness()

Description

Add full stop to end of a sentence

Usage

EndSentence(string)

Arguments

Value

EndSentence() returns string, punctuated with a final full stop (period).'

Author(s)

Martin R. Smith

See Also

Other string parsing functions: MatchStrings(), MorphoBankDecode(), RightmostCharacter(), Unquote()

Examples

EndSentence("Hello World") # "Hello World."

Description

RandomTree(), PectinateTree(), BalancedTree() and StarTree() generate trees with the specified shapes and leaf labels.

Usage

RandomTree(tips, root = FALSE, nodes, lengths = NULL)

YuleTree(tips, addInTurn = FALSE, root = TRUE, lengths = NULL)

PectinateTree(tips, lengths = NULL)

BalancedTree(tips, lengths = NULL)

StarTree(tips, lengths = NULL)

Arguments

Value

Each function returns an unweighted binary tree of class phylo with the specified leaf labels. Trees are rooted unless root = FALSE.

RandomTree() returns a topology drawn at random from the uniform distribution (i.e. each binary tree is drawn with equal probability). Trees are generated by inserting each tip in term at a randomly selected edge in the tree. Random numbers are generated using a Mersenne Twister. If root = FALSE, the tree will be unrooted, with the first tip in a basal position. Otherwise, the tree will be rooted on root.

YuleTree() returns a topology generated by the Yule process (Steel and McKenzie 2001), i.e. adding leaves in turn adjacent to a randomly-chosen existing leaf.

PectinateTree() returns a pectinate (caterpillar) tree.

BalancedTree() returns a balanced (symmetrical) tree, in preorder.

StarTree() returns a completely unresolved (star) tree.

Author(s)

Martin R. Smith ([email protected])

References

Steel MA, McKenzie A (2001). “Properties of Phylogenetic Trees Generated by Yule-type Speciation Models.” Mathematical Biosciences, 170(1), 91–112. doi:10.1016/S0025-5564(00)00061-4.()

See Also

Other tree generation functions: ConstrainedNJ(), NJTree(), TreeNumber, TrivialTree

Examples

# Set random seed for reproducibility
set.seed(10)

# Generate a tree from a phylogenetic dataset
data("Lobo")
RandomTree(Lobo.phy, lengths = runif)

# Generate trees on letters A-J
plot(RandomTree(LETTERS[1:10], root = TRUE))

plot(YuleTree(LETTERS[1:10]))

plot(PectinateTree(LETTERS[1:10]))

plot(BalancedTree(LETTERS[1:10], lengths = 1:18))

plot(StarTree(LETTERS[1:10]))

Description

The Hamming distance between a pair of taxa is the number of characters with a different coding, i.e. the smallest number of evolutionary steps that must have occurred since their common ancestor.

Usage

Hamming(
  dataset,
  ratio = TRUE,
  ambig = c("median", "mean", "zero", "one", "na", "nan")
)

Arguments

Details

Tokens that contain the inapplicable state are treated as requiring no steps to transform into any applicable token.

Value

Hamming() returns an object of class dist listing the Hamming distance between each pair of taxa.

Author(s)

Martin R. Smith ([email protected])

See Also

Used to construct neighbour joining trees in NJTree().

dist.hamming() in the phangorn package provides an alternative implementation.

Other utility functions: ClusterTable, ClusterTable-methods, MSTEdges(), SampleOne(), TipTimedTree(), UnshiftTree(), as.multiPhylo(), match,phylo,phylo-method, sapply64(), sort.multiPhylo()

Examples

tokens <- matrix(c(0, 0, "0", 0, "?",
                   0, 0, "1", 0, 1,
                   0, 0, "1", 0, 1,
                   0, 0, "2", 0, 1,
                   1, 1, "-", "?", 0,
                   1, 1, "2", 1, "{01}"),
                   nrow = 6, ncol = 5, byrow = TRUE,
                   dimnames = list(
                     paste0("Taxon_", LETTERS[1:6]),
                     paste0("Char_", 1:5)))

dataset <- MatrixToPhyDat(tokens)
Hamming(dataset)

Description

Modify a tree such that it matches a specified constraint. This is at present a somewhat crude implementation that attempts to retain much of the structure of tree whilst guaranteeing compatibility with each entry in constraint.

Usage

ImposeConstraint(tree, constraint)

AddUnconstrained(constraint, toAdd, asPhyDat = TRUE)

Arguments

Value

ImposeConstraint() returns a tree of class phylo, consistent with constraint.

Functions

• AddUnconstrained(): Expand a constraint to include unconstrained taxa.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree manipulation: AddTip(), CollapseNode(), ConsensusWithout(), DropTip(), KeptPaths(), KeptVerts(), LeafLabelInterchange(), MakeTreeBinary(), Renumber(), RenumberTips(), RenumberTree(), RootTree(), SortTree(), Subtree(), TipTimedTree(), TrivialTree

Examples

tips <- letters[1:9]
tree <- as.phylo(1, 9, tips)
plot(tree)

constraint <- StringToPhyDat("0000?1111 000111111 0000??110", tips, FALSE)
plot(ImposeConstraint(tree, constraint))

Description

Is an object a TreeNumber object?

Usage

is.TreeNumber(x)

Arguments

Value

is.TreeNumber() returns a logical vector of length one specifying whether x inherits the class "TreeNumber".

Author(s)

Martin R. Smith ([email protected])

See Also

Other 'TreeNumber' utilities: TreeNumber, print.TreeNumber()

Examples

is.TreeNumber(FALSE) # FALSE 
is.TreeNumber(as.TreeNumber(BalancedTree(5))) # TRUE

Description

Calculate tree balance index J¹ (when nonRootDominance = FALSE) or J^1c (when nonRootDominance = TRUE) from (Lemant et al. 2022).

Usage

J1Index(tree, q = 1, nonRootDominance = FALSE)

JQIndex(tree, q = 1, nonRootDominance = FALSE)

Arguments

Details

If population sizes are not provided, then the function assigns size 0 to internal nodes, and size 1 to leaves.

Value

J1Index() returns a numeric specifying the J¹ index of tree. $J^1(T) = 1$ for a perfectly balanced tree; $J^1(T) = 0$ for a pectinate (linear / caterpillar) tree.

Author(s)

Rob Noble, adapted by Martin R. Smith

References

Lemant J, Le Sueur C, Manojlović V, Noble R (2022). “Robust, Universal Tree Balance Indices.” Systematic Biology, 71(5), 1210–1224. doi:10.1093/sysbio/syac027.

See Also

Other tree characterization functions: CladisticInfo(), Consensus(), Stemwardness, TotalCopheneticIndex()

Examples

# Using phylo object as input:
phylo_tree <- read.tree(text="((a:0.1)A:0.5,(b1:0.2,b2:0.1)B:0.2);")
J1Index(phylo_tree)
phylo_tree2 <- read.tree(text='((A, B), ((C, D), (E, F)));')
J1Index(phylo_tree2)

# Using edges lists as input:
tree1 <- data.frame(Parent = c(1, 1, 1, 1, 2, 3, 4),
                    Identity = 1:7,
                    Population = c(1, rep(5, 6)))
J1Index(tree1)
tree2 <- data.frame(Parent = c(1, 1, 1, 1, 2, 3, 4),
                    Identity = 1:7,
                    Population = c(rep(0, 4), rep(1, 3)))
J1Index(tree2)
tree3 <- data.frame(Parent = c(1, 1, 1, 1, 2, 3, 4),
                    Identity = 1:7,
                    Population = c(0, rep(1, 3), rep(0, 3)))
J1Index(tree3)
cat_tree <- data.frame(Parent = c(1, 1:14, 1:15, 15),
                       Identity = 1:31,
                       Population = c(rep(0, 15), rep(1, 16)))
J1Index(cat_tree)

# If population sizes are omitted then internal nodes are assigned population
# size zero and leaves are assigned population size one:
sym_tree1 <- data.frame(Parent = c(1, rep(1:15, each = 2)),
                       Identity = 1:31,
                       Population = c(rep(0, 15), rep(1, 16)))
# Equivalently:                        
sym_tree2 <- data.frame(Parent = c(1, rep(1:15, each = 2)),
                       Identity = 1:31)
J1Index(sym_tree1)
J1Index(sym_tree2)

Description

Lists which paths present in a master tree are present when leaves are dropped.

Usage

KeptPaths(paths, keptVerts, all = TRUE)

## S3 method for class 'data.frame'
KeptPaths(paths, keptVerts, all = TRUE)

## S3 method for class 'matrix'
KeptPaths(paths, keptVerts, all = TRUE)

Arguments

Value

KeptPaths() returns a logical vector specifying whether each path in paths occurs when keptVerts vertices are retained.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree manipulation: AddTip(), CollapseNode(), ConsensusWithout(), DropTip(), ImposeConstraint(), KeptVerts(), LeafLabelInterchange(), MakeTreeBinary(), Renumber(), RenumberTips(), RenumberTree(), RootTree(), SortTree(), Subtree(), TipTimedTree(), TrivialTree

Examples

master <- BalancedTree(9)
paths <- PathLengths(master)
keptTips <- c(1, 5, 7, 9)
keptVerts <- KeptVerts(master, keptTips)
KeptPaths(paths, keptVerts)
paths[KeptPaths(paths, keptVerts, all = FALSE), ]

Description

Identify vertices retained when leaves are dropped

Usage

KeptVerts(tree, keptTips, tipLabels = TipLabels(tree))

## S3 method for class 'phylo'
KeptVerts(tree, keptTips, tipLabels = TipLabels(tree))

## S3 method for class 'numeric'
KeptVerts(tree, keptTips, tipLabels = TipLabels(tree))

Arguments

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree manipulation: AddTip(), CollapseNode(), ConsensusWithout(), DropTip(), ImposeConstraint(), KeptPaths(), LeafLabelInterchange(), MakeTreeBinary(), Renumber(), RenumberTips(), RenumberTree(), RootTree(), SortTree(), Subtree(), TipTimedTree(), TrivialTree

Examples

master <- BalancedTree(12)
master <- Preorder(master) # Nodes must be listed in Preorder sequence
plot(master)
nodelabels()

allTips <- master[["tip.label"]]
keptTips <- sample(allTips, 8)
plot(KeepTip(master, keptTips))
kept <- KeptVerts(master, allTips %in% keptTips)

map <- which(kept)
# Node `i` in the reduced tree corresponds to node `map[i]` in the original.

Description

Labels the edges associated with each split on a plotted tree.

Usage

LabelSplits(tree, labels = NULL, unit = "", ...)

Arguments

Details

As the two root edges of a rooted tree denote the same split, only the rightmost (plotted at the bottom, by default) edge will be labelled. If the position of the root is significant, add a tip at the root using AddTip().

Value

LabelSplits() returns invisible(), after plotting labels on each relevant edge of a plot (which should already have been produced using plot(tree)).

See Also

Calculate split support: SplitFrequency()

Colour labels according to value: SupportColour()

Other Splits operations: NSplits(), NTip(), PolarizeSplits(), SplitFrequency(), Splits, SplitsInBinaryTree(), TipLabels(), TipsInSplits(), match,Splits,Splits-method, xor()

Examples

tree <- BalancedTree(LETTERS[1:5])
splits <- as.Splits(tree)
plot(tree)
LabelSplits(tree, as.character(splits), frame = "none", pos = 3L)
LabelSplits(tree, TipsInSplits(splits), unit = " tips", frame = "none",
            pos = 1L)

# An example forest of 100 trees, some identical
forest <- as.phylo(c(1, rep(10, 79), rep(100, 15), rep(1000, 5)), nTip = 9)

# Generate an 80% consensus tree
cons <- ape::consensus(forest, p = 0.8)
plot(cons)

# Calculate split frequencies
splitFreqs <- SplitFrequency(cons, forest)

# Optionally, colour edges by corresponding frequency.
# Note that not all edges are associated with a unique split
# (and two root edges may be associated with one split - not handled here)
edgeSupport <- rep(1, nrow(cons$edge)) # Initialize trivial splits to 1
childNode <- cons$edge[, 2]
edgeSupport[match(names(splitFreqs), childNode)] <- splitFreqs / 100

plot(cons, edge.col = SupportColour(edgeSupport), edge.width = 3)

# Annotate nodes by frequency 
LabelSplits(cons, splitFreqs, unit = "%",
            col = SupportColor(splitFreqs / 100),
            frame = "none", pos = 3L)

Description

LeafLabelInterchange() exchanges the position of leaves within a tree.

Usage

LeafLabelInterchange(tree, n = 2L)

Arguments

Details

Modifies a tree by switching the positions of n leaves. To avoid later swaps undoing earlier exchanges, all n leaves are guaranteed to change position. Note, however, that no attempt is made to avoid swapping equivalent leaves, for example, a pair that are each others' closest relatives. As such, the relationships within a tree are not guaranteed to be changed.

Value

LeafLabelInterchange() returns a tree of class phylo on which the position of n leaves have been exchanged. The tree's internal topology will not change.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree manipulation: AddTip(), CollapseNode(), ConsensusWithout(), DropTip(), ImposeConstraint(), KeptPaths(), KeptVerts(), MakeTreeBinary(), Renumber(), RenumberTips(), RenumberTree(), RootTree(), SortTree(), Subtree(), TipTimedTree(), TrivialTree

Examples

tree <- PectinateTree(8)
plot(LeafLabelInterchange(tree, 3L))

Description

ListAncestors() reports all ancestors of a given node.

Usage

ListAncestors(parent, child, node = NULL)

AllAncestors(parent, child)

Arguments

Details

Note that if node = NULL, the tree's edges must be listed such that each internal node (except the root) is listed as a child before it is listed as a parent, i.e. its index in child is less than its index in parent. This will be true of trees listed in Preorder.

Value

If node = NULL, ListAncestors() returns a list. Each entry i contains a vector containing, in order, the nodes encountered when traversing the tree from node i to the root node. The last entry of each member of the list is therefore the root node, with the exception of the entry for the root node itself, which is a zero-length integer.

If node is an integer, ListAncestors() returns a vector of the numbers of the nodes ancestral to the given node, including the root node.

Functions

• AllAncestors(): Alias for ListAncestors(node = NULL).

Author(s)

Martin R. Smith ([email protected])

See Also

Implemented less efficiently in phangorn:::Ancestors, on which this code is based.

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeAncestry(), EdgeDistances(), MRCA(), MatchEdges(), NDescendants(), NodeDepth(), NodeNumbers(), NodeOrder(), PaintTree(), RootNode()

Examples

tree <- PectinateTree(5)
edge <- tree[["edge"]]

# Identify desired node with:
plot(tree)
nodelabels()
tiplabels()

# Ancestors of specific nodes:
ListAncestors(edge[, 1], edge[, 2], 4L)
ListAncestors(edge[, 1], edge[, 2], 8L)

# Ancestors of each node, if tree numbering system is uncertain:
lapply(seq_len(max(edge)), ListAncestors,
       parent = edge[, 1], child = edge[, 2])

# Ancestors of each node, if tree is in preorder:
ListAncestors(edge[, 1], edge[, 2])

# Alias:
AllAncestors(edge[, 1], edge[, 2])

Description

Phylogenetic data from Zhang et al. (2016) in raw (Lobo.data) and phyDat (Lobo.phy) formats.

Usage

Lobo.data

Lobo.phy

Format

An object of class list of length 48.

An object of class phyDat of length 48.

Source

Zhang et al. (2016)

References

Zhang X, Smith MR, Yang J, Hou J (2016). “Onychophoran-like musculature in a phosphatized Cambrian lobopodian.” Biology Letters, 12(9), 20160492. doi:10.1098/rsbl.2016.0492.

Examples

data("Lobo", package = "TreeTools")
Lobo.data
Lobo.phy

Description

logDoubleFactorials is a numeric vector with pre-calculated values of double factorials up to 50 000!!.

Usage

logDoubleFactorials

Format

An object of class numeric of length 50000.

See Also

Other double factorials: DoubleFactorial(), doubleFactorials

Description

The long branch (LB) score (Struck 2014) measures the deviation of the average pairwise patristic distance of a leaf from all other leaves in a tree, relative to the average leaf-to-leaf distance.

Usage

LongBranch(tree)

Arguments

Details

Struck (2014) proposes the standard deviation of LB scores as a measure of heterogeneity that can be compared between trees; and the upper quartile of LB scores as "a representative value for the taxa with the longest branches".

Value

LongBranch() returns a vector giving the long branch score for each leaf in tree, or a list of such vectors if tree is a list. Results are given as raw deviations, without multiplying by 100 as proposed by Struck (2014).

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree properties: Cherries(), ConsensusWithout(), EdgeRatio(), MatchEdges(), NSplits(), NTip(), NodeNumbers(), PathLengths(), SplitsInBinaryTree(), TipLabels(), TreeIsRooted(), Treeness()

Examples

tree <- BalancedTree(8, lengths = c(rep(2, 4), 5:7, rep(2, 4), rep(1, 3)))
lb <- LongBranch(tree)
tree$tip.label <- paste(tree$tip.label, signif(lb, 3), sep = ": ")
plot(tree, tip.col = SupportColour((1 - lb) / 2), font = 2)

# Standard deviation of LB scores allows comparison with other trees
sd(lb)
evenLengths <- BalancedTree(8, lengths = jitter(rep(1, 14)))
sd(LongBranch(evenLengths))

# Upper quartile identifies taxa with longest branches
threshold <- quantile(lb, 0.75)
tree$tip.label[lb > threshold]

Description

MakeTreeBinary() resolves, at random, all polytomies in a tree or set of trees, such that all trees compatible with the input topology are drawn with equal probability. Edge lengths are not yet supported, so are removed.

Usage

MakeTreeBinary(tree)

Arguments

Value

MakeTreeBinary() returns a rooted binary tree of class phylo, corresponding to tree uniformly selected from all those compatible with the input tree topologies.

Author(s)

Martin R. Smith ([email protected])

See Also

Since ape v5.5, this functionality is available through ape::multi2di(); previous versions of "ape" did not return topologies in equal frequencies. MakeTreeBinary() is often somewhat faster; multi2di() retains edge lengths.

Other tree manipulation: AddTip(), CollapseNode(), ConsensusWithout(), DropTip(), ImposeConstraint(), KeptPaths(), KeptVerts(), LeafLabelInterchange(), Renumber(), RenumberTips(), RenumberTree(), RootTree(), SortTree(), Subtree(), TipTimedTree(), TrivialTree

Examples

MakeTreeBinary(CollapseNode(PectinateTree(7), c(9, 11, 13)))
UnrootTree(MakeTreeBinary(StarTree(5)))

Description

match() returns a vector of the positions of (first) matches of trees in its first argument in its second. %in% is a more intuitive interface as a binary operator, which returns a logical vector indicating whether there is a match or not for each tree in its left operand.

Usage

## S4 method for signature 'phylo,phylo'
match(x, table, nomatch = NA_integer_, incomparables = NULL)

## S4 method for signature 'multiPhylo,phylo'
match(x, table, nomatch = NA_integer_, incomparables = NULL)

## S4 method for signature 'phylo,multiPhylo'
match(x, table, nomatch = NA_integer_, incomparables = NULL)

## S4 method for signature 'multiPhylo,multiPhylo'
match(x, table, nomatch = NA_integer_, incomparables = NULL)

## S4 method for signature 'multiPhylo,multiPhylo'
x %in% table

## S4 method for signature 'multiPhylo,phylo'
x %in% table

## S4 method for signature 'phylo,multiPhylo'
x %in% table

## S4 method for signature 'phylo,phylo'
x %in% table

Arguments

Value

match() returns an integer vector specifying the position in table that matches each element in x, or nomatch if no match is found.

See Also

Corresponding base functions are documented in match().

Other utility functions: ClusterTable, ClusterTable-methods, Hamming(), MSTEdges(), SampleOne(), TipTimedTree(), UnshiftTree(), as.multiPhylo(), sapply64(), sort.multiPhylo()

Examples

tree1 <- BalancedTree(7)
trees <- c(PectinateTree(7), BalancedTree(7))

match(tree1, trees)

Description

match() returns a vector of the positions of (first) matches of splits in its first argument in its second. %in% is a more intuitive interface as a binary operator, which returns a logical vector indicating whether there is a match or not for each split in its left operand.

Usage

## S4 method for signature 'Splits,Splits'
match(x, table, nomatch = NA_integer_, incomparables = NULL)

match(x, table, nomatch = NA_integer_, incomparables = NULL)

## S4 method for signature 'Splits,Splits'
x %in% table

FirstMatchingSplit(x, table, nomatch, return = c("x", "table", "both"))

Arguments

Value

match() returns an integer vector specifying the position in table that matches each element in x, or nomatch if no match is found.

FirstMatchingSplit() returns an integer (or length-2 integer if return = "both") specifying the first split in x to have a match in table (return = "x"), or the index of that match (return = "table"). nomatch (default 0) is returned in the absence of a match.

See Also

Corresponding base functions are documented in match().

Other Splits operations: LabelSplits(), NSplits(), NTip(), PolarizeSplits(), SplitFrequency(), Splits, SplitsInBinaryTree(), TipLabels(), TipsInSplits(), xor()

Examples

splits1 <- as.Splits(BalancedTree(7))
splits2 <- as.Splits(PectinateTree(7))

match(splits1, splits2)

Description

MatchNodes() and MatchEdges() matches nodes or edges in one tree to entries in the second that denote a clade with identical tip labels.

Usage

MatchEdges(x, table, nomatch = NA_integer_)

MatchNodes(x, table, nomatch = NA_integer_, tips = FALSE)

Arguments

Details

The current implementation is potentially inefficient. Please contact the maintainer to request a more efficient implementation if this function is proving a bottleneck.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeAncestry(), EdgeDistances(), ListAncestors(), MRCA(), NDescendants(), NodeDepth(), NodeNumbers(), NodeOrder(), PaintTree(), RootNode()

Other tree properties: Cherries(), ConsensusWithout(), EdgeRatio(), LongBranch(), NSplits(), NTip(), NodeNumbers(), PathLengths(), SplitsInBinaryTree(), TipLabels(), TreeIsRooted(), Treeness()

Examples

MatchNodes(BalancedTree(8), RootTree(BalancedTree(8)))

Description

Checks that entries in one character vector occur in another, suggesting corrections for mismatched elements.

Usage

MatchStrings(x, table, Fail = stop, max.distance = 0.5, ...)

Arguments

Value

MatchStrings() returns the elements of x that occur in table.

Author(s)

Martin R. Smith ([email protected])

See Also

Other string parsing functions: EndSentence(), MorphoBankDecode(), RightmostCharacter(), Unquote()

Examples

tree <- BalancedTree(8)
MatchStrings(c("t1", "tip2", "t3"), TipLabels(tree), Fail = message)

Description

MatrixToPhyDat() converts a matrix of tokens to a phyDat object; PhyDatToMatrix() converts a phyDat object to a matrix of tokens.

Usage

MatrixToPhyDat(tokens, tipLabels = rownames(tokens))

PhyDatToMatrix(
  dataset,
  ambigNA = FALSE,
  inappNA = ambigNA,
  parentheses = c("{", "}"),
  sep = ""
)

Arguments

Value

MatrixToPhyDat() returns an object of class phyDat.

PhyDatToMatrix() returns a matrix corresponding to the uncompressed character states within a phyDat object.

Author(s)

Martin R. Smith ([email protected])

See Also

Other phylogenetic matrix conversion functions: Decompose(), NexusTokensToInteger(), Reweight(), StringToPhyDat()

Examples

tokens <- matrix(c(0, 0, "0", 0, 0,
                   0, 0, "1", 0, 1,
                   0, 0, "1", 0, 1,
                   0, 0, "2", 0, 1,
                   1, 1, "-", 1, 0,
                   1, 1, "2", 1, "{01}"),
                   nrow = 6, ncol = 5, byrow = TRUE,
                   dimnames = list(
                     paste0("Taxon_", LETTERS[1:6]),
                     paste0("Char_", 1:5)))
                   
MatrixToPhyDat(tokens)
data("Lobo", package = "TreeTools")
head(PhyDatToMatrix(Lobo.phy)[, 91:93])

Description

Converts strings from MorphoBank notes into a Latex-compatible format.

Usage

MorphoBankDecode(string)

Arguments

Value

MorphoBankDecode() returns a string with new lines and punctuation reformatted.

Author(s)

Martin R. Smith

See Also

Other string parsing functions: EndSentence(), MatchStrings(), RightmostCharacter(), Unquote()

Description

MRCA() calculates the last common ancestor of specified nodes.

Usage

MRCA(x1, x2, ancestors)

Arguments

Details

MRCA() requires that node values within a tree increase away from the root, which will be true of trees listed in Preorder. No warnings will be given if trees do not fulfil this requirement.

Value

MRCA() returns an integer specifying the node number of the last common ancestor of x1 and x2.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeAncestry(), EdgeDistances(), ListAncestors(), MatchEdges(), NDescendants(), NodeDepth(), NodeNumbers(), NodeOrder(), PaintTree(), RootNode()

Examples

tree <- BalancedTree(7)

# Verify that node numbering increases away from root
plot(tree)
nodelabels()

# ListAncestors expects a tree in Preorder
tree <- Preorder(tree)
edge <- tree$edge
ancestors <- ListAncestors(edge[, 1], edge[, 2])
MRCA(1, 4, ancestors)

# If a tree must be in postorder, use:
tree <- Postorder(tree)
edge <- tree$edge
ancestors <- lapply(seq_len(max(edge)), ListAncestors,
                    parent = edge[, 1], child = edge[, 2])

Description

Calculate or plot the minimum spanning tree (Gower and Ross 1969) of a distance matrix.

Usage

MSTEdges(distances, plot = FALSE, x = NULL, y = NULL, ...)

MSTLength(distances, mst = NULL)

Arguments

Value

MSTEdges() returns a matrix in which each row corresponds to an edge of the minimum spanning tree, listed in non-decreasing order of length. The two columns contain the indices of the entries in distances that each edge connects, with the lower value listed first.

MSTLength() returns the length of the minimum spanning tree.

Author(s)

Martin R. Smith ([email protected])

References

Gower JC, Ross GJS (1969). “Minimum spanning trees and single linkage cluster analysis.” Journal of the Royal Statistical Society. Series C (Applied Statistics), 18(1), 54–64. doi:10.2307/2346439.

See Also

Slow implementation returning the association matrix of the minimum spanning tree: ape::mst().

Other utility functions: ClusterTable, ClusterTable-methods, Hamming(), SampleOne(), TipTimedTree(), UnshiftTree(), as.multiPhylo(), match,phylo,phylo-method, sapply64(), sort.multiPhylo()

Examples

# Corners of an almost-regular octahedron
points <- matrix(c(0, 0, 2, 2, 1.1, 1,
                   0, 2, 0, 2, 1, 1.1,
                   0, 0, 0, 0, 1, -1), 6)
distances <- dist(points)
mst <- MSTEdges(distances)
MSTLength(distances, mst)
plot(points[, 1:2], ann = FALSE, asp = 1)
MSTEdges(distances, TRUE, x = points[, 1], y = points[, 2], lwd = 2)

Description

N1Spr() calculates the number of trees one subtree prune-and-regraft operation away from a binary input tree using the formula given by Allen and Steel (2001); IC1Spr() calculates the information content of trees at this distance: i.e. the entropy corresponding to the proportion of all possible n-tip trees whose SPR distance is at most one from a specified tree.

Usage

N1Spr(n)

IC1Spr(n)

Arguments

Value

N1Spr() returns an integer vector denoting the number of trees one SPR rearrangement away from the input tree..

IC1Spr() returns an numeric vector giving the phylogenetic information content of trees 0 or 1 SPR rearrangement from an n-leaf tree, in bits.

References

Allen BL, Steel MA (2001). “Subtree transfer operations and their induced metrics on evolutionary trees.” Annals of Combinatorics, 5(1), 1–15. doi:10.1007/s00026-001-8006-8.

Examples

N1Spr(4:6)
IC1Spr(5)

Description

NDescendants() counts the number of nodes (including leaves) directly descended from each node in a tree.

Usage

NDescendants(tree)

Arguments

Value

NDescendants() returns an integer listing the number of direct descendants (leaves or internal nodes) for each node in a tree.

Author(s)

Martin R. Smith ([email protected])

See Also

CladeSizes() for the count of descendant leaves (rather than direct children) at each node.

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeAncestry(), EdgeDistances(), ListAncestors(), MRCA(), MatchEdges(), NodeDepth(), NodeNumbers(), NodeOrder(), PaintTree(), RootNode()

Examples

tree <- CollapseNode(BalancedTree(8), 12:15)
NDescendants(tree)
plot(tree)
nodelabels(NDescendants(tree))

Description

NewickTree() encodes a tree as a Newick-format string. This differs from write.tree() in the encoding of spaces as spaces, rather than underscores.

Usage

NewickTree(tree)

Arguments

Value

NewickTree() returns a character string denoting tree in Newick format.

See Also

Use tip numbers, rather than leaf labels: as.Newick

Examples

NewickTree(BalancedTree(LETTERS[4:9]))

Description

NexusTokensToInteger() converts the character matrix returned by ReadCharacters() to an integer matrix, mapping polymorphic, ambiguous (⁠?⁠), and inapplicable (-) tokens to NA_integer_ or to the first/last state listed in the polymorphism, depending on polymorphism.

Usage

NexusTokensToInteger(tokens, polymorphism = c("?", "first", "last"))

Arguments

Details

Only digit states 0..9 are recognised; non-digit symbols (and any token whose interior contains no digits) become NA_integer_. Polymorphism extraction (polymorphism = "first"/"last") likewise considers digits only.

If tokens is a phyDat object it is first converted via PhyDatToMatrix() with ⁠ambigNA = TRUE, inappNA = TRUE⁠, so that fully-ambiguous and inapplicable rows become NA_integer_ and only true partial polymorphisms are subject to the polymorphism rule.

Value

An integer matrix (or vector) with the same dimensions and dimnames as tokens.

Author(s)

Martin R. Smith ([email protected])

See Also

Other phylogenetic matrix conversion functions: Decompose(), MatrixToPhyDat(), Reweight(), StringToPhyDat()

Examples

tokens <- matrix(c("0", "(12)", "1", "?", "-"),
                 nrow = 1,
                 dimnames = list("Taxon_A", paste0("C", 1:5)))
NexusTokensToInteger(tokens)
NexusTokensToInteger(tokens, polymorphism = "first")

Description

NJTree() generates a rooted neighbour joining tree from a phylogenetic dataset.

Usage

NJTree(dataset, edgeLengths = FALSE, ratio = TRUE, ambig = "mean")

Arguments

Value

NJTree returns an object of class phylo.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree generation functions: ConstrainedNJ(), GenerateTree, TreeNumber, TrivialTree

Examples

data("Lobo")
NJTree(Lobo.phy)

Description

NodeDepth() evaluates how "deep" each node is within a tree.

Usage

NodeDepth(x, shortest = FALSE, includeTips = TRUE)

Arguments

Details

For a rooted tree, the depth of a node is the minimum (if shortest = TRUE) or maximum (shortest = FALSE) number of edges that must be traversed, moving away from the root, to reach a leaf.

Unrooted trees are treated as if a root node occurs in the "middle" of the tree, meaning the position that will minimise the maximum node depth.

Value

NodeDepth() returns an integer vector specifying the depth of each external and internal node in x.

Author(s)

Martin R. Smith ([email protected])

See Also

ape::node.depth returns the number of tips descended from a node.

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeAncestry(), EdgeDistances(), ListAncestors(), MRCA(), MatchEdges(), NDescendants(), NodeNumbers(), NodeOrder(), PaintTree(), RootNode()

Examples

tree <- CollapseNode(BalancedTree(10), c(12:13, 19))
plot(tree)
nodelabels(NodeDepth(tree, includeTips = FALSE))

Description

Numeric index of each node in a tree NodeNumbers() returns a sequence corresponding to the nodes in a tree

Usage

NodeNumbers(tree, tips = FALSE)

Arguments

Value

NodeNumbers() returns an integer vector corresponding to the indices of nodes within a tree.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree properties: Cherries(), ConsensusWithout(), EdgeRatio(), LongBranch(), MatchEdges(), NSplits(), NTip(), PathLengths(), SplitsInBinaryTree(), TipLabels(), TreeIsRooted(), Treeness()

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeAncestry(), EdgeDistances(), ListAncestors(), MRCA(), MatchEdges(), NDescendants(), NodeDepth(), NodeOrder(), PaintTree(), RootNode()

Description

NodeOrder() calculates the order of each node: the number of edges incident to it in a tree. This value includes the root edge in rooted trees.

Usage

NodeOrder(x, includeAncestor = TRUE, internalOnly = FALSE)

Arguments

Value

NodeOrder() returns an integer listing the order of each node; entries are named with the number of each node.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeAncestry(), EdgeDistances(), ListAncestors(), MRCA(), MatchEdges(), NDescendants(), NodeDepth(), NodeNumbers(), PaintTree(), RootNode()

Examples

tree <- CollapseNode(BalancedTree(8), 12:15)
NodeOrder(tree)
plot(tree)
nodelabels(NodeOrder(tree, internalOnly = TRUE))

Description

NPartitionPairs() calculates the number of terminal arrangements matching a specified configuration of two splits.

Usage

NPartitionPairs(configuration)

Arguments

Details

Consider splits that divide eight terminals, labelled A to H.

This can be represented by an association matrix:

The cells in this matrix contain 2, 1, 1 and 3 terminals respectively; this four-element vector (c(2, 1, 1, 3)) is the configuration implied by this pair of bipartition splits.

Value

The number of ways to distribute sum(configuration) taxa according to the specified pattern.

Author(s)

Martin R. Smith ([email protected])

Examples

NPartitionPairs(c(2, 1, 1, 3))

Description

These functions return the number of rooted or unrooted binary trees consistent with a given pattern of splits.

Usage

NRooted(tips)

NUnrooted(tips)

NRooted64(tips)

NUnrooted64(tips)

LnUnrooted(tips)

LnUnrooted.int(tips)

Log2Unrooted(tips)

Log2Unrooted.int(tips)

LnRooted(tips)

LnRooted.int(tips)

Log2Rooted(tips)

Log2Rooted.int(tips)

LnUnrootedSplits(...)

Log2UnrootedSplits(...)

NUnrootedSplits(...)

LnUnrootedMult(...)

Log2UnrootedMult(...)

NUnrootedMult(...)

Arguments

Details

Functions starting N return the number of rooted or unrooted trees. Replace this initial N with Ln for the natural logarithm of this number; or Log2 for its base 2 logarithm.

Calculations follow Cavalli-Sforza and Edwards (1967) and Carter et al. (1990), Theorem 2.

Functions

• NUnrooted(): Number of unrooted trees

• NRooted64(): Exact number of rooted trees as 64-bit integer (13 < nTip < 19)

• NUnrooted64(): Exact number of unrooted trees as 64-bit integer (14 < nTip < 20)

• LnUnrooted(): Log Number of unrooted trees

• LnUnrooted.int(): Log Number of unrooted trees (as integer)

• LnRooted(): Log Number of rooted trees

• LnRooted.int(): Log Number of rooted trees (as integer)

• NUnrootedSplits(): Number of unrooted trees consistent with a bipartition split.

• NUnrootedMult(): Number of unrooted trees consistent with a multi-partition split.

Author(s)

Martin R. Smith ([email protected])

References

Carter M, Hendy M, Penny D, Székely LA, Wormald NC (1990). “On the distribution of lengths of evolutionary trees.” SIAM Journal on Discrete Mathematics, 3(1), 38–47. doi:10.1137/0403005.

Cavalli-Sforza LL, Edwards AWF (1967). “Phylogenetic analysis: models and estimation procedures.” Evolution, 21(3), 550–570. ISSN 00143820. doi:10.1111/j.1558-5646.1967.tb03411.x.

See Also

Other tree information functions: CladisticInfo(), TreesMatchingTree()

Examples

NRooted(10)
NUnrooted(10)
LnRooted(10)
LnUnrooted(10)
Log2Unrooted(10)
# Number of trees consistent with a character whose states are
# 00000 11111 222
NUnrootedMult(c(5,5,3))

NUnrooted64(18)
LnUnrootedSplits(c(2,4))
LnUnrootedSplits(3, 3)
Log2UnrootedSplits(c(2,4))
Log2UnrootedSplits(3, 3)
NUnrootedSplits(c(2,4))
NUnrootedSplits(3, 3)

Description

nRootedShapes and nUnrootedShapes give the number of (un)rooted binary trees on n unlabelled leaves.

Usage

nRootedShapes

nUnrootedShapes

Format

An object of class integer64 of length 55.

An object of class integer64 of length 60.

Source

nRootedShapes corresponds to the Wedderburn-Etherington numbers, OEIS A001190

nUnrootedShapes is OEIS A000672

Description

NSplits() counts the unique bipartition splits in a tree or object.

Usage

NSplits(x)

NPartitions(x)

## S3 method for class 'phylo'
NSplits(x)

## S3 method for class 'list'
NSplits(x)

## S3 method for class 'multiPhylo'
NSplits(x)

## S3 method for class 'Splits'
NSplits(x)

## S3 method for class 'numeric'
NSplits(x)

## S3 method for class ''NULL''
NSplits(x)

## S3 method for class 'ClusterTable'
NSplits(x)

## S3 method for class 'character'
NSplits(x)

Arguments

Value

NSplits() returns an integer specifying the number of bipartitions in the specified objects, or in a binary tree with x tips.

Author(s)

Martin R. Smith ([email protected])

See Also

Other tree properties: Cherries(), ConsensusWithout(), EdgeRatio(), LongBranch(), MatchEdges(), NTip(), NodeNumbers(), PathLengths(), SplitsInBinaryTree(), TipLabels(), TreeIsRooted(), Treeness()

Other Splits operations: LabelSplits(), NTip(), PolarizeSplits(), SplitFrequency(), Splits, SplitsInBinaryTree(), TipLabels(), TipsInSplits(), match,Splits,Splits-method, xor()

Examples

NSplits(8L)
NSplits(PectinateTree(8))
NSplits(as.Splits(BalancedTree(8)))

Description

NTip() extends ape::Ntip() to handle objects of class Splits and list, and edge matrices (equivalent to tree$edge).

Usage

NTip(phy)

## Default S3 method:
NTip(phy)

## S3 method for class 'Splits'
NTip(phy)

## S3 method for class 'list'
NTip(phy)

## S3 method for class 'phylo'
NTip(phy)

## S3 method for class 'multiPhylo'
NTip(phy)

## S3 method for class 'phyDat'
NTip(phy)

## S3 method for class 'matrix'
NTip(phy)

Arguments

Value

NTip() returns an integer specifying the number of tips in each object in phy.

See Also

Other tree properties: Cherries(), ConsensusWithout(), EdgeRatio(), LongBranch(), MatchEdges(), NSplits(), NodeNumbers(), PathLengths(), SplitsInBinaryTree(), TipLabels(), TreeIsRooted(), Treeness()

Other Splits operations: LabelSplits(), NSplits(), PolarizeSplits(), SplitFrequency(), Splits, SplitsInBinaryTree(), TipLabels(), TipsInSplits(), match,Splits,Splits-method, xor()

Description

PaintTree() assigns a colour to every edge, leaf, and internal node of a tree such that sister clades occupy adjacent hue bands proportional to their tip counts, and saturation grows from zero at the root to one at every tip. The result is a list of vectors that correspond to plot.phylo()'s edge.color, tip.color, and node.color arguments.

Usage

PaintTree(tree, palette = "default")

Arguments

Details

Hue is allocated recursively from a 0–360° budget at the root. At each internal node, the parent's budget is partitioned across its descendant edges in proportion to the number of leaves descended from each child; the colour reported for an edge or node is the midpoint of its assigned range. Saturation s = (nTip - nDesc) / (nTip - 1), where nDesc is the number of leaves descended from the node (including itself for tips), so the root is achromatic (s = 0) and every leaf is fully saturated (s = 1).

Value

A list with three character vectors of hex colours:

edgeCol

One colour per edge in tree$edge, taken from the child node's ⁠(hue, saturation)⁠.

tipCol

One colour per leaf, indexed 1:NTip(tree).

nodeCol

One colour per internal node, indexed so that entry i corresponds to node NTip(tree) + i; the root (entry 1) is grey because its saturation is zero.

Author(s)

Martin R. Smith ([email protected])

See Also

CladeSizes(), DescendantEdges()

Other tree navigation: AncestorEdge(), CladeSizes(), DescendantEdges(), EdgeAncestry(), EdgeDistances(), ListAncestors(), MRCA(), MatchEdges(), NDescendants(), NodeDepth(), NodeNumbers(), NodeOrder(), RootNode()

Examples

tree <- BalancedTree(1:8) + PectinateTree(9:14)
tree <- ape::bind.tree(BalancedTree(1:8), PectinateTree(9:12), 8, 4)
paint <- PaintTree(tree)
plot(tree,
     edge.color = paint$edgeCol,
     tip.color = paint$tipCol,
     edge.width = 3)

# Colour-blind-safe variants
paintP <- PaintTree(tree, "protanopia")
plot(tree,
     edge.color = paintP$edgeCol,
     tip.color = paintP$tipCol,
     edge.width = 3)

paintT <- PaintTree(tree, "tritanopia")
plot(tree,
     edge.color = paintT$edgeCol,
     tip.color = paintT$tipCol,
     edge.width = 3)

# User-supplied palette function (greyscale)
grey_pal <- function(h, s) grey(1 - s * 0.8)
paintG <- PaintTree(tree, grey_pal)
plot(tree, edge.color = paintG$edgeCol, edge.width = 3)