prop.part counts the number of bipartitions found in a series
of trees given as ....
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)
prop.part(...)
prop.clades(phy, ..., part = NULL)
## S3 method for class 'prop.part':
print(x, ...)
## S3 method for class 'prop.part':
summary(object, ...)"phylo".boot.phylo: a taxa (rows) by characters
(columns) matrix; this may be presented as a list; in the case of
print: an object of class "prop.part".phy (see details).x that will be resampled
together (see details)."phylo", (ii) a
series of such objects separated by commas, or (iii) a list
containing such objects.prop.part; if
this is used then ... is ignored."prop.part".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. prop.clades and boot.phylo returns a numeric vector
which ith element is the number associated to the ith
node of phy.
FUN in boot.phylo must be the function used
to estimate the tree from the original (resampled) data matrix. Thus,
if the tree is 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.
Felsenstein, J. (1985) Confidence limits on phylogenies: an approach using the bootstrap. Evolution, 39, 783--791.
dist.topo, consensus