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Frequency of splits

Source: R/Support.R
SplitFrequency.Rd

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.

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.

Details

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

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.

See also

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

Author

Martin R. Smith (martin.smith@durham.ac.uk)

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)