ape (version 2.8)

boot.phylo: Tree Bipartition and Bootstrapping Phylogenies

Description

These functions analyse bipartitions found in a series of trees.

prop.part counts the number of bipartitions found in a series of trees given as .... If a single tree is passed, the returned object is a list of vectors with the tips descending from each node (i.e., clade compositions indexed by node number).

prop.clades counts the number of times the bipartitions present in phy are present in a series of trees given as ... or in the list previously computed and given with part.

boot.phylo performs a bootstrap analysis.

Usage

boot.phylo(phy, x, FUN, B = 100, block = 1,
           trees = FALSE, quiet = FALSE)
prop.part(..., check.labels = TRUE)
prop.clades(phy, ..., part = NULL)
## S3 method for class 'prop.part':
print(x, ...)
## S3 method for class 'prop.part':
summary(object, ...)
## S3 method for class 'prop.part':
plot(x, barcol = "blue", leftmar = 4, ...)

Value

prop.part returns an object of class "prop.part" which is a list with an attribute "number". The elements of this list are the observed clades, and the attribute their respective numbers. If the default check.labels = FALSE is used, an attribute "labels" is added, and the vectors of the returned object contains the indices of these labels instead of the labels themselves.

prop.clades and boot.phylo return a numeric vector which ith element is the number associated to the ith node of phy. If trees = TRUE, boot.phylo returns a list whose first element (named "BP") is like before, and the second element ("trees") is a list with the bootstraped trees.

summary returns a numeric vector.

Details

The argument FUN in boot.phylo must be the function used to estimate the tree from the original data matrix. Thus, if the tree was estimated with neighbor-joining (see nj), one maybe wants something like FUN = function(xx) nj(dist.dna(xx)).

block in boot.phylo specifies the number of columns to be resampled altogether. For instance, if one wants to resample at the codon-level, then block = 3 must be used.

Using check.labels = FALSE in prop.part decreases computing times. This requires that (i) all trees have the same tip labels, and (ii) these labels are ordered similarly in all trees (in other words, the element tip.label are identical in all trees).

The plot function represents a contingency table of the different partitions (on the x-axis) in the lower panel, and their observed numbers in the upper panel. Any further arguments (...) are used to change the aspects of the points in the lower panel: these may be pch, col, bg, cex, etc. This function works only if there is an attribute labels in the object.

The print method displays the partitions and their numbers. The summary method extracts the numbers only.

References

Efron, B., Halloran, E. and Holmes, S. (1996) Bootstrap confidence levels for phylogenetic trees. Proceedings of the National Academy of Sciences USA, 93, 13429--13434.

Felsenstein, J. (1985) Confidence limits on phylogenies: an approach using the bootstrap. Evolution, 39, 783--791.

See Also

dist.topo, consensus, nodelabels

Examples

data(woodmouse)
f <- function(x) nj(dist.dna(x))
tr <- f(woodmouse)
### Are bootstrap values stable?
for (i in 1:5)
  print(boot.phylo(tr, woodmouse, f, quiet = TRUE))
### How many partitions in 100 random trees of 10 labels?...
TR <- replicate(100, rtree(10), FALSE)
pp10 <- prop.part(TR)
length(pp10)
### ... and in 100 random trees of 20 labels?
TR <- replicate(100, rtree(20), FALSE)
pp20 <- prop.part(TR)
length(pp20)
plot(pp10, pch = "x", col = 2)
plot(pp20, pch = "x", col = 2)