Find MCMC Target Sequence
mcmc.target.seq uses MCMC to find a configuration of DNA positions
to get as close as possible to a given tree.
boot.samples.idxs bootstraps over indices into a DNA matrix.
lookup.samples goes from an index representation of a configuration
of DNA to the actual DNAbin format.
convert.table.to.idx converts a table of counts for positions 1..n
into a list of indices corresponding to positions (i.e. goes from the
tabled form to a vector whose tabling matches the input).
mcmc.target.seq(data, x, F, n) boot.samples.idxs(data, B = 100, block = 1) lookup.samples(data, idxs) convert.table.to.idx(T)
data |
A DNA matrix in DNAbin format. |
x |
A tree of class 'phylo' to estimate. |
F |
A tree estimation function, accepting a DNA matrix in DNAbin format and returning a tree of class 'phylo.' |
n |
The number of MCMC iterations to perform. |
B |
The number of bootstrap replicates. |
block |
The block size to use during bootstrapping. |
idxs |
A list of numeric vectors of indices to use for lookup. |
T |
A table or table-like vector to convert. |
mcmc.target.seq performs an MCMC with simulated annealing to locate
a configuration of DNA positions from the original matrix that gets as
close as possible to a target tree. Propositions for the MCMC replacing
one character with another uniformly at random.
The remaining functions are intended to be used as support functions.
mcmc.target.seq returns a list of 4 elements: a numeric vector of
counts of each position in the original matrix, the best estimated
tree, the best distance from the estimated tree to the target tree, and
a numeric vector of the distances for every iteration of the
simulation.
boot.samples.idxs returns a numeric vector representing the
bootstrapped idices.
lookup.samples returns a list of objects of class DNAbin
corresponding to the DNA sequences generated from indices into the original
DNA matrix.
convert.table.to.idx returns a numeric vector of indices based on
the table counts.
John Chakerian
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
## Not run:
## This example has been excluded from checks:
## copy/paste the code to try it
data(woodmouse)
otree <- root(fastme.ols(dist.dna(woodmouse)), "No305", resolve.root=TRUE)
breps <- 200
trees <- boot.phylo(otree, woodmouse, B=breps, function(x)
root(fastme.ols(dist.dna(x)), "No305", resolve.root=TRUE),
trees = TRUE)
combined.trees <- c(list(otree), trees$trees)
binning <- bin.multiPhylo(combined.trees)
tree.a <- combined.trees[[match(1, binning)]]
i <- 2
max.bin <- max(binning)
tree.b <- combined.trees[[match(2, binning)]]
while(length(distinct.edges(tree.a,tree.b)) > 1 && i < max.bin)
{
i = i + 1
tree.b = combined.trees[[match(i, binning)]]
}
bdy.tree <- orthant.boundary.tree(tree.a, tree.b)
f.est <- function(x) root(nj(dist.dna(x)), "No305", resolve.root=TRUE)
res <- mcmc.target.seq(woodmouse, bdy.tree, f.est, 1000)
par(mfrow=c(2,1))
plot(res$tree)
plot(res$vals)
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