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Consensus tree that minimizes quartet distance

Source: R/Quartet.R
Quartet.Rd

Quartet() constructs a consensus tree that minimizes the sum of symmetric quartet distances to a set of input trees, using a greedy add-and-prune heuristic.

Usage

Quartet(
  trees,
  init = c("majority", "star", "extended"),
  greedy = c("best", "first")
)

Arguments

trees

Object of class multiPhylo specifying the input trees. All trees must share the same tip labels.

init

Character string specifying the initial tree:

  • "majority": the majority-rule consensus.

  • "star": a fully unresolved star tree.

  • "extended": the extended (greedy) majority-rule consensus.

greedy

Character string specifying the greedy strategy:

  • "best": evaluate all candidates and pick the best action at each step.

  • "first": pick the first improving action encountered (faster; may give a slightly worse result).

Value

Quartet() returns a consensus tree, an object of class phylo, unrooted.

Details

Where the majority-rule consensus minimizes the sum of Robinson-Foulds distances to the input trees (Barthélemy and McMorris 1986) , Quartet() finds an approximate median tree under the symmetric quartet distance (Estabrook et al. 1985; Takazawa et al. 2026) , which counts both false-positive and false-negative resolved quartets equally.

Because the quartet distance gives greater weight to deep branches (which resolve more quartets), quartet consensus trees tend to be more resolved than majority-rule trees, especially when phylogenetic signal is low.

The function supports trees with up to 100 tips.

References

Barthélemy J, McMorris FR (1986). “The median procedure for n-trees.” Journal of Classification, 3(2), 329–334. doi:10.1007/BF01894194 .

Estabrook GF, McMorris FR, Meacham CA (1985). “Comparison of undirected phylogenetic trees based on subtrees of four evolutionary units.” Systematic Zoology, 34(2), 193–200. doi:10.2307/2413326 .

Takazawa Y, Takeda A, Hayamizu M, Gascuel O (2026). “Outperforming the majority-rule consensus tree using fine-grained dissimilarity measures.” bioRxiv. doi:10.64898/2026.03.16.712085 .

See also

Closely related: Strict(), Majority(), Greedy().

Other consensus methods: Adams(), Average(), Frequency(), Greedy(), Local(), Loose(), Majority(), MajorityPlus(), RStar(), Strict(), Transfer()

Examples

library("TreeTools", quietly = TRUE)

# Generate bootstrap-like trees
trees <- as.phylo(1:30, nTip = 8)

# Quartet consensus
qc <- Quartet(trees)
plot(qc)


# Compare resolution with majority-rule
mr <- UnrootTree(Consensus(trees, p = 0.5))
cat("Majority-rule splits:", NSplits(mr), "\n")
#> Majority-rule splits: 2 
cat("Quartet consensus splits:", NSplits(qc), "\n")
#> Quartet consensus splits: 3