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.
bootstrap.pml(x, bs = 100, trees = TRUE, multicore = FALSE, mc.cores = NULL, ...) bootstrap.phyDat(x, FUN, bs = 100, multicore = FALSE, mc.cores = NULL, jumble = TRUE, ...)
x |
an object of class |
bs |
number of bootstrap samples. |
trees |
return trees only (default) or whole |
multicore |
logical, whether models should estimated in parallel. |
mc.cores |
The number of cores to use during bootstrap. Only supported on UNIX-alike systems. |
... |
further parameters used by |
FUN |
the function to estimate the trees. |
jumble |
logical, jumble the order of the sequences. |
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").
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.
Klaus Schliep klaus.schliep@gmail.com
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
optim.pml
, pml
,
plot.phylo
, maxCladeCred
nodelabels
,consensusNet
and
SOWH.test
for parametric bootstrap
## Not run: 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) ## End(Not run)
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