Ratchet uses the parsimony ratchet (Nixon 1999) to search for a more parsimonious tree.
Jackknife(
tree,
dataset,
resampleFreq = 2/3,
InitializeData = PhyDat2Morphy,
CleanUpData = UnloadMorphy,
TreeScorer = MorphyLength,
EdgeSwapper = TBRSwap,
jackIter = 5000L,
searchIter = 4000L,
searchHits = 42L,
verbosity = 1L,
...
)Ratchet(
tree,
dataset,
InitializeData = PhyDat2Morphy,
CleanUpData = UnloadMorphy,
TreeScorer = MorphyLength,
Bootstrapper = MorphyBootstrap,
swappers = list(TBRSwap, SPRSwap, NNISwap),
BootstrapSwapper = if (class(swappers) == "list") swappers[[length(swappers)]] else
swappers,
returnAll = FALSE,
stopAtScore = NULL,
stopAtPeak = FALSE,
stopAtPlateau = 0L,
ratchIter = 100,
ratchHits = 10,
searchIter = 4000,
searchHits = 42,
bootstrapIter = searchIter,
bootstrapHits = searchHits,
verbosity = 1L,
suboptimal = 1e-08,
...
)
ProfileRatchet(
tree,
dataset,
swappers = list(TBRSwap, SPRSwap, NNISwap),
BootstrapSwapper = if (class(swappers) == "list") swappers[[length(swappers)]] else
swappers,
returnAll = FALSE,
stopAtScore = NULL,
stopAtPeak = FALSE,
stopAtPlateau = 0L,
ratchIter = 100,
ratchHits = 10,
searchIter = 2000,
searchHits = 40,
bootstrapIter = searchIter,
bootstrapHits = searchHits,
verbosity = 1L,
suboptimal = 1e-08,
...
)
IWRatchet(
tree,
dataset,
concavity = 10,
swappers = list(TBRSwap, SPRSwap, NNISwap),
BootstrapSwapper = if (class(swappers) == "list") swappers[[length(swappers)]] else
swappers,
returnAll = FALSE,
stopAtScore = NULL,
stopAtPeak = FALSE,
stopAtPlateau = 0L,
ratchIter = 100,
ratchHits = 10,
searchIter = 2000,
searchHits = 40,
bootstrapIter = searchIter,
bootstrapHits = searchHits,
verbosity = 1L,
suboptimal = 1e-08,
...
)
MultiRatchet(
tree,
dataset,
ratchHits = 10,
searchIter = 500,
searchHits = 20,
verbosity = 0L,
swappers = list(RootedNNISwap),
nSearch = 10,
stopAtScore = NULL,
...
)
IWMultiRatchet(
tree,
dataset,
ratchHits = 10,
concavity = 4,
searchIter = 500,
searchHits = 20,
verbosity = 0L,
swappers = list(RootedNNISwap),
nSearch = 10,
suboptimal = suboptimal,
stopAtScore = NULL,
...
)
RatchetConsensus(
tree,
dataset,
ratchHits = 10,
searchIter = 500,
searchHits = 20,
verbosity = 0L,
swappers = list(RootedNNISwap),
nSearch = 10,
stopAtScore = NULL,
...
)
IWRatchetConsensus(
tree,
dataset,
ratchHits = 10,
concavity = 4,
searchIter = 500,
searchHits = 20,
verbosity = 0L,
swappers = list(RootedNNISwap),
nSearch = 10,
suboptimal = suboptimal,
stopAtScore = NULL,
...
)
A tree of class phylo.
a dataset in the format required by TreeScorer().
Double between 0 and 1 stating proportion of characters to resample
Function that sets up data object to prepare for tree search.
The function will be passed the dataset parameter.
Its return value will be passed to TreeScorer() and CleanUpData().
Function to destroy data object on function exit.
The function will be passed the value returned by InitializeData().
function to score a given tree.
The function will be passed three parameters, corresponding to the
parent and child entries of a tree's edge list, and a dataset.
a function that rearranges a parent and child vector,
and returns a list with modified vectors; for example SPRSwap().
Integer specifying number of jackknife iterations to conduct
Integer specifying maximum rearrangements to perform on each bootstrap or
ratchet iteration.
To override this value for a single swapper function, set e.g.
attr(SwapperFunction, 'searchIter') <- 99
Integer specifying maximum times to hit best score before terminating a tree
search within a ratchet iteration.
To override this value for a single swapper function, set e.g.
attr(SwapperFunction, 'searchHits') <- 99
Level of detail to display in console: larger numbers provide more verbose feedback to the user.
further arguments to pass to TreeScorer()
(e.g. TipsAreColumns, dataset).
Function to perform bootstrapped rearrangements of tree.
First arguments will be an edgeList and a dataset, initialized using
InitializeData(). Should return a rearranged edgeList.
A list of functions to use to conduct edge rearrangement during tree search.
Provide functions like NNISwap to shuffle root position,
or RootedTBRSwap if the position of the root should be retained.
You may wish to use extreme swappers (such as TBR) early in the list,
and a more subtle rearranger (such as NNI) later in the list to make
incremental tinkerings once an almost-optimal tree has been found.
Function such as RootedNNISwap to use
to rearrange trees within Bootstrapper().
Set to TRUE to report all MPTs encountered during the
search, perhaps to analyse consensus.
stop search as soon as this score is hit or beaten.
Logical specifying whether to terminate search once a subsequent
iteration recovers a sub-optimal score. Useful with methods that
return all trees one rearrangement from the current tree, such
as AllTBR(). Will be overridden if a passed function
has an attribute stopAtPeak set by
attr(FunctionName, 'stopAtPeak') <- TRUE.
Integer. If > 0, tree search will terminate if the score
has not improved after stopAtPlateau iterations.
Useful with methods that return all trees one rearrangement from the current
tree, such as AllTBR().
Will be overridden if a passed function has an attribute stopAtPlateau set
by attr(FunctionName, 'stopAtPlateau') <- TRUE.
Stop when this many ratchet iterations have been performed.
Stop when this many ratchet iterations have found the same best score.
Integer specifying maximum rearrangements to perform on each bootstrap
iteration (default: searchIter).
Integer specifying maximum times to hit best score on each bootstrap
iteration (default: searchHits).
retain trees that are suboptimal by this score. Defaults to 1e-08 to counter rounding errors.
A numeric value to use as the concavity constant (k)
in implied weighting. Defaults to 10; TNT sets a default of 3, but this
is too low in some circumstances (Smith, 2019).
Number of Ratchet searches to conduct
(for RatchetConsensus())
a list of trees recovered after jackknife iterations
This function returns a tree modified by parsimony ratchet iterations.
MultiRatchet() returns a list of optimal trees
produced by nSearch
ratchet searches, from which a consensus tree can be generated using
ape::consensus() or TreeTools::ConsensusWithout().
IWMultiRatchet returns a list of optimal trees produced by nSearch
Ratchet searches, using implied weighting.
Jackknife: Jackknife resampling. Note that at present this assumes that
InitializeData will return a morphy object; if this doesn't hold for you, please
let me know and I'll make the function more general.
ProfileRatchet: Shortcut for Ratchet search under Profile Parsimony
IWRatchet: Shortcut for Ratchet search using implied weights
RatchetConsensus: deprecated alias for MultiRatchet()
IWRatchetConsensus: deprecated alias for MultiRatchet()
Nixon1999TreeSearch
SmithTernTreeSearch
JackLabels(): Label nodes of a tree with jackknife supports.
Adapted from pratchet in the
phangorn package.
# NOT RUN {
data('Lobo', package='TreeTools')
njtree <- TreeTools::NJTree(Lobo.phy)
# Increase value of ratchIter and searchHits to do a proper search
quickResult <- Ratchet(njtree, Lobo.phy, ratchIter = 2, searchHits = 3)
# Plot result (legibly)
oldPar <- par(mar = rep(0, 4), cex = 0.75)
plot(quickResult)
par(oldPar)
# IW search is currently much slower:
quickIWResult <- IWRatchet(quickResult, Lobo.phy, concavity = 2.5,
ratchIter = 1, searchIter = 25, searchHits = 2,
swappers = RootedTBRSwap, verbosity = 5)
# }
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