Islands function

Find islands from distance matrix

Find islands from distance matrix

Islands() assigns a set of objects to islands, such that all elements within an island can form a connected graph in which each edge is no longer than threshold distance units \insertRef Silva2021TreeDist.

Islands(D, threshold, dense = TRUE, smallest = 0)

Arguments

  • D: Square matrix or dist object containing Euclidean distances between data points.
  • threshold: Elements greater than threshold distance units will not be assigned to the same island.
  • dense: Logical; if FALSE, each island will be named according to the index of its lowest-indexed member; if TRUE, each island will be numbered sequentially from 1, ordered by the index of the lowest-indexed member.
  • smallest: Integer; Islands comprising no more than smallest elements will be assigned to island NA.

Returns

Islands() returns a vector listing the island to which each element is assigned.

Examples

library("TreeTools", quietly = TRUE) # Generate a set of trees trees <- as.phylo(as.TreeNumber(BalancedTree(16)) + c(-(40:20), 70:105), 16) # Calculate distances between trees distances <- ClusteringInfoDist(trees) summary(distances) # Assign trees to islands isle <- Islands(distances, quantile(distances, 0.1)) table(isle) # Indicate island membership on 2D mapping of tree distances mapping <- cmdscale(distances, 2) plot(mapping, col = isle + 1, asp = 1, # Preserve aspect ratio - do not distort distances ann = FALSE, axes = FALSE, # Don't label axes: dimensions are meaningless) pch = 16 # Plotting character: Filled circle ) # Compare strict consensus with island consensus trees oPar <- par(mfrow = c(2, 2), mai = rep(0.1, 4)) plot(Consensus(trees), main = "Strict") plot(Consensus(trees[isle == 1]), edge.col = 2, main = "Island 1") plot(Consensus(trees[isle == 2]), edge.col = 3, main = "Island 2") plot(Consensus(trees[isle == 3]), edge.col = 4, main = "Island 3") # Restore graphical parameters par(oPar)

References

\insertAllCited

See Also

Other tree space functions: MSTSegments(), MapTrees(), MappingQuality(), SpectralEigens(), cluster-statistics, median.multiPhylo()

Author(s)

Martin R. Smith

(martin.smith@durham.ac.uk)