Bootstrap

bootstrap.pml performs (non-parametric) bootstrap analysis and bootstrap.phyDat produces a list of bootstrapped data sets. plotBS plots a phylogenetic tree with the bootstrap values assigned to the (internal) edges.

Usage

bootstrap.pml(x, bs = 100, trees = TRUE, multicore = FALSE,
  mc.cores = NULL, tip.dates = NULL, ...)

bootstrap.phyDat(x, FUN, bs = 100, multicore = FALSE, mc.cores = NULL,
  jumble = TRUE, ...)

Arguments

x: an object of class pml or phyDat.
bs: number of bootstrap samples.
trees: return trees only (default) or whole pml objects.
multicore: logical, whether models should estimated in parallel.
mc.cores: The number of cores to use during bootstrap. Only supported on UNIX-alike systems.
tip.dates: A named vector of sampling times associated to the tips/sequences. Leave empty if not estimating tip dated phylogenies.
...: further parameters used by optim.pml or plot.phylo.
FUN: the function to estimate the trees.
jumble: logical, jumble the order of the sequences.

Value

bootstrap.pml returns an object of class multi.phylo or a list where each element is an object of class pml. plotBS returns silently a tree, i.e. an object of class phylo with the bootstrap values as node labels. The argument BStrees is optional and if not supplied the tree with labels supplied in the node.label slot.

Details

It is possible that the bootstrap is performed in parallel, with help of the multicore package. Unfortunately the multicore package does not work under windows or with GUI interfaces ("aqua" on a mac). However it will speed up nicely from the command line ("X11").

References

Felsenstein J. (1985) Confidence limits on phylogenies. An approach using the bootstrap. Evolution 39, 783–791

Lemoine, F., Entfellner, J. B. D., Wilkinson, E., Correia, D., Felipe, M. D., De Oliveira, T., & Gascuel, O. (2018). Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature, 556(7702), 452–456.

Penny D. and Hendy M.D. (1985) Testing methods evolutionary tree construction. Cladistics 1, 266–278

Penny D. and Hendy M.D. (1986) Estimating the reliability of evolutionary trees. Molecular Biology and Evolution 3, 403–417

Author

Klaus Schliep klaus.schliep@gmail.com

Examples


if (FALSE) { # \dontrun{
data(Laurasiatherian)
dm <- dist.hamming(Laurasiatherian)
tree <- NJ(dm)
# NJ
set.seed(123)
NJtrees <- bootstrap.phyDat(Laurasiatherian,
     FUN=function(x)NJ(dist.hamming(x)), bs=100)
treeNJ <- plotBS(tree, NJtrees, "phylogram")

# Maximum likelihood
fit <- pml(tree, Laurasiatherian)
fit <- optim.pml(fit, rearrangement="NNI")
set.seed(123)
bs <- bootstrap.pml(fit, bs=100, optNni=TRUE)
treeBS <- plotBS(fit$tree,bs)

# Maximum parsimony
treeMP <- pratchet(Laurasiatherian)
treeMP <- acctran(treeMP, Laurasiatherian)
set.seed(123)
BStrees <- bootstrap.phyDat(Laurasiatherian, pratchet, bs = 100)
treeMP <- plotBS(treeMP, BStrees, "phylogram")
add.scale.bar()

# export tree with bootstrap values as node labels
# write.tree(treeBS)
} # }