SplitFrequency {TreeTools}

Frequency of splits

Description

SplitFrequency() provides a simple way to count the number of times that bipartition splits, as defined by a reference tree, occur in a forest of trees. May be used to calculate edge ("node") support for majority consensus or bootstrap trees.

Usage

SplitFrequency(reference, forest)

SplitNumber(tips, tree, tipIndex, powersOf2)

ForestSplits(forest, powersOf2)

TreeSplits(tree)

Arguments

reference

A tree of class phylo, a Splits object.

forest

a list of trees of class phylo, or a multiPhylo object; or a Splits object. See vignette for possible methods of loading trees into R.

tips

Integer vector specifying the tips of the tree within the chosen split.

tree

A tree of class phylo.

tipIndex

Character vector of tip names, in a fixed order.

powersOf2

Integer vector of same length as tipIndex, specifying a power of 2 to be associated with each tip in turn.

Details

If multiple calculations are required, some time can be saved by using the constituent functions (see examples)

Value

SplitFrequency() returns the number of trees in forest that contain each split in reference. If reference is a tree of class phylo, then the sequence will correspond to the order of nodes (use ape::nodelabels() to view). Note that the three nodes at the root of the tree correspond to a single split; see the example for how these might be plotted on a tree.

Functions

• SplitNumber(): Assign a unique integer to each split

• ForestSplits(): Frequency of splits in a given forest of trees

• TreeSplits(): Deprecated. Listed the splits in a given tree. Use as.Splits instead.

Author(s)

Martin R. Smith ([e-mail])

See Also

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

Examples

# 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)