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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.
boot.phylo(phy, x, FUN, B = 100, block = 1,
trees = FALSE, quiet = FALSE,
rooted = is.rooted(phy), jumble = TRUE,
mc.cores = 1)
prop.part(..., check.labels = TRUE)
prop.clades(phy, ..., part = NULL, rooted = FALSE)
# S3 method for prop.part
print(x, ...)
# S3 method for prop.part
summary(object, ...)
# S3 method for prop.part
plot(x, barcol = "blue", leftmar = 4, col = "red", ...)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.
an object of class "phylo".
in the case of boot.phylo: a taxa (rows) by characters
(columns) matrix; in the case of print and plot: an
object of class "prop.part".
the function used to estimate phy (see details).
the number of bootstrap replicates.
the number of columns in x that will be resampled
together (see details).
a logical specifying whether to return the bootstraped
trees (FALSE by default).
a logical: a progress bar is displayed by default.
a logical specifying whether the trees should be treated as rooted or not.
a logical value. By default, the rows of x are
randomized to avoid artificially too large bootstrap values
associated with very short branches.
the number of cores (CPUs) to be used (passed to parallel).
either (i) a single object of class "phylo", (ii) a
series of such objects separated by commas, or (iii) a list
containing such objects. In the case of plot further
arguments for the plot (see details).
a logical specifying whether to check the labels
of each tree. If FALSE, it is assumed that all trees have the
same tip labels, and that they are in the same order (see details).
a list of partitions as returned by prop.part; if
this is used then ... is ignored.
an object of class "prop.part".
the colour used for the bars displaying the number of partitions in the upper panel.
the size of the margin on the left to display the tip labels.
the colour used to visualise the bipartitions.
Emmanuel Paradis
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.
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.
as.bitsplits, dist.topo,
consensus, nodelabels
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 <- rmtree(100, 10)
pp10 <- prop.part(TR)
length(pp10)
### ... and in 100 random trees of 20 labels?
TR <- rmtree(100, 20)
pp20 <- prop.part(TR)
length(pp20)
plot(pp10, pch = "x", col = 2)
plot(pp20, pch = "x", col = 2)
set.seed(2)
tr <- rtree(10) # rooted
## the following used to return a wrong result with ape <= 3.4:
prop.clades(tr, tr)
prop.clades(tr, tr, rooted = TRUE)
tr <- rtree(10, rooted = FALSE)
prop.clades(tr, tr) # correct
### an illustration of the use of prop.clades with bootstrap trees:
fun <- function(x) as.phylo(hclust(dist.dna(x), "average")) # upgma() in phangorn
tree <- fun(woodmouse)
## get 100 bootstrap trees:
bstrees <- boot.phylo(tree, woodmouse, fun, trees = TRUE)$trees
## get proportions of each clade:
clad <- prop.clades(tree, bstrees, rooted = TRUE)
## get proportions of each bipartition:
boot <- prop.clades(tree, bstrees)
layout(1)
par(mar = rep(2, 4))
plot(tree, main = "Bipartition vs. Clade Support Values")
drawSupportOnEdges(boot)
nodelabels(clad)
legend("bottomleft", legend = c("Bipartitions", "Clades"), pch = 22,
pt.bg = c("green", "lightblue"), pt.cex = 2.5)
if (FALSE) {
## an example of double bootstrap:
nrep1 <- 100
nrep2 <- 100
p <- ncol(woodmouse)
DB <- 0
for (b in 1:nrep1) {
X <- woodmouse[, sample(p, p, TRUE)]
DB <- DB + boot.phylo(tr, X, f, nrep2, quiet = TRUE)
}
DB
## to compare with:
boot.phylo(tr, woodmouse, f, 1e4)
}
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