Parsimony tree.

pratchet implements the parsimony ratchet (Nixon, 1999) and is the preferred way to search for the best parsimony tree. For small number of taxa the function bab can be used to compute all most parsimonious trees.

Usage

acctran(tree, data)

fitch(tree, data, site = "pscore")

random.addition(data, tree = NULL, method = "fitch")

parsimony(tree, data, method = "fitch", cost = NULL, site = "pscore")

optim.parsimony(tree, data, method = "fitch", cost = NULL, trace = 1,
  rearrangements = "SPR", ...)

pratchet(data, start = NULL, method = "fitch", maxit = 1000,
  minit = 100, k = 10, trace = 1, all = FALSE,
  rearrangements = "SPR", perturbation = "ratchet", ...)

sankoff(tree, data, cost = NULL, site = "pscore")

Arguments

tree: tree to start the nni search from.
data: A object of class phyDat containing sequences.
site: return either 'pscore' or 'site' wise parsimony scores.
method: one of 'fitch' or 'sankoff'.
cost: A cost matrix for the transitions between two states.
trace: defines how much information is printed during optimization.
rearrangements: SPR or NNI rearrangements.
...: Further arguments passed to or from other methods (e.g. model="sankoff" and cost matrix).
start: a starting tree can be supplied.
maxit: maximum number of iterations in the ratchet.
minit: minimum number of iterations in the ratchet.
k: number of rounds ratchet is stopped, when there is no improvement.
all: return all equally good trees or just one of them.
perturbation: whether to use "ratchet", "random_addition" or "stochastic" (nni) for shuffling the tree.

Value

parsimony returns the maximum parsimony score (pscore). optim.parsimony returns a tree after NNI rearrangements. pratchet returns a tree or list of trees containing the best tree(s) found during the search. acctran returns a tree with edge length according to the ACCTRAN criterion.

Details

parsimony returns the parsimony score of a tree using either the sankoff or the fitch algorithm. optim.parsimony optimizes the topology using either Nearest Neighbor Interchange (NNI) rearrangements or sub tree pruning and regrafting (SPR) and is used inside pratchet. random.addition can be used to produce starting trees and is an option for the argument perturbation in pratchet.

The "SPR" rearrangements are so far only available for the "fitch" method, "sankoff" only uses "NNI". The "fitch" algorithm only works correct for binary trees.

References

Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates, Sunderland.

Nixon, K. (1999) The Parsimony Ratchet, a New Method for Rapid Parsimony Analysis. Cladistics 15, 407-414

Author

Klaus Schliep klaus.schliep@gmail.com

Examples


set.seed(3)
data(Laurasiatherian)
dm <- dist.hamming(Laurasiatherian)
tree <- NJ(dm)
parsimony(tree, Laurasiatherian)
#> [1] 9796
treeRA <- random.addition(Laurasiatherian)
treeSPR <- optim.parsimony(tree, Laurasiatherian)
#> Final p-score 9715 after  11 nni operations 

# lower number of iterations for the example (to run less than 5 seconds),
# keep default values (maxit, minit, k) or increase them for real life
# analyses.
treeRatchet <- pratchet(Laurasiatherian, start=tree, maxit=100,
                        minit=5, k=5, trace=0)
# assign edge length (number of substitutions)
treeRatchet <- acctran(treeRatchet, Laurasiatherian)
# remove edges of length 0
treeRatchet <- di2multi(treeRatchet)

plot(midpoint(treeRatchet))
add.scale.bar(0,0, length=100)


parsimony(c(tree,treeSPR, treeRatchet), Laurasiatherian)
#> [1] 9796 9715 9713