TreeTools (version 2.4.1)

SplitFrequency: 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 = NULL, hash = TRUE)

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.

If reference is NULL, then SplitFrequency() returns a list of splits (in the order encountered in forest) with attribute "count" stating the number of times each split occurs in forest.

Arguments

reference

A tree of class phylo, a Splits object. If NULL, the frequencies of all splits in forest will be returned.

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.

hash

Logical; if TRUE (default), counts the frequencies of all splits (i.e. when reference = NULL) using a faster 128-bit hashing approach whose results are exact with overwhelming probability (a hash collision conflating two distinct splits is vanishingly unlikely). Set hash = FALSE for a slower but guaranteed-exact count. Ignored when reference is a tree or Splits object.

Author

Martin R. Smith ()

Details

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

See Also

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