Plot concordance table

ConcordanceTable() plots a concordance table (Smith 2026) .

Usage

ConcordanceTable(
  tree,
  dataset,
  Col = QACol,
  largeClade = 0,
  xlab = "Edge",
  ylab = "Character",
  normalize = TRUE,
  ...
)

Arguments

tree

A tree of class phylo.

dataset

A phylogenetic data matrix of phangorn class phyDat, whose names correspond to the labels of any accompanying tree. Perhaps load into R using ReadAsPhyDat(). Additive (ordered) characters can be handled using Decompose().

Col

Function that takes vectors amount and quality and returns a vector of colours. QCol colours by data quality (concordance); QACol by quality and amount of information.

largeClade

Integer; if greater than 1, vertical lines will be drawn at edges whose descendants are both contain more than largeClade leaves.

xlab

Character giving a label for the x axis.

ylab

Character giving a label for the y axis.

normalize

Controls how the expected mutual information (the zero point of the scale) is determined.

• FALSE: no chance correction; MI is scaled only by its maximum.

• TRUE: subtract the analytical expected MI for random association.

• <integer>: subtract an empirical expected MI estimated from that number of random trees.

In all cases, 1 corresponds to the maximal attainable MI for the pair (hBest), and 0 corresponds to the chosen expectation.

...

Arguments to abline, to control the appearance of vertical lines marking important edges.

Value

ConcordanceTable() invisibly returns an named list containing:

• "info": The amount of information in each character-edge pair, in bits;

• "relInfo": The information, normalized to the most information-rich pair;

• "quality": The normalized mutual information of the pair;

• "col": The colours used to plot the table.

References

Smith MR (2026). “Which characters support which clades? Exploring the distribution of phylogenetic signal using concordant information.” Forthcoming.

See also

• SiteConcordance(): compute underlying concordance values.

Other split support functions: JackLabels(), Jackknife(), MaximizeParsimony(), MostContradictedFreq(), PresCont(), SiteConcordance

Examples

# Load data and tree
data("congreveLamsdellMatrices", package = "TreeSearch")
dataset <- congreveLamsdellMatrices[[1]][, 1:20]
tree <- referenceTree

# Plot tree and identify nodes
library("TreeTools", quietly = TRUE)
plot(tree)
nodeIndex <- as.integer(rownames(as.Splits(tree)))
nodelabels(seq_along(nodeIndex), nodeIndex, adj = c(2, 1),
           frame = "none", bg = NULL)
QALegend(where = c(0.1, 0.4, 0.1, 0.3))


# View information shared by characters and edges
ConcordanceTable(tree, dataset, largeClade = 3, col = 2, lwd = 3)
axis(1)
axis(2)


# Visualize dataset
image(t(`mode<-`(PhyDatToMatrix(dataset), "numeric")), axes = FALSE,
      xlab = "Leaf", ylab = "Character")