Search for most parsimonious trees using the parsimony ratchet and TBR rearrangements, treating inapplicable data as such using the algorithm of Brazeau et al. (2019) .

Tree search will be conducted from a specified or automatically-generated starting tree in order to find a tree with an optimal parsimony score, under implied or equal weights, treating inapplicable characters as such in order to avoid the artefacts of the standard Fitch algorithm (see Maddison 1993; Brazeau et al. 2019) . Tree length is calculated using the MorphyLib C library (Brazeau et al. 2017) .

## Usage

```
MaximizeParsimony(
dataset,
tree,
ratchIter = 7L,
tbrIter = 2L,
startIter = 2L,
finalIter = 1L,
maxHits = NTip(dataset) * 1.8,
maxTime = 60,
quickHits = 1/3,
concavity = Inf,
ratchEW = TRUE,
tolerance = sqrt(.Machine[["double.eps"]]),
constraint,
verbosity = 3L
)
Resample(
dataset,
tree,
method = "jack",
proportion = 2/3,
ratchIter = 1L,
tbrIter = 8L,
finalIter = 3L,
maxHits = 12L,
concavity = Inf,
tolerance = sqrt(.Machine[["double.eps"]]),
constraint,
verbosity = 2L,
...
)
EasyTrees()
EasyTreesy()
```

## Arguments

- dataset
A phylogenetic data matrix of phangorn class

`phyDat`

, whose names correspond to the labels of any accompanying tree.- tree
(optional) A bifurcating tree of class

`phylo`

, containing only the tips listed in`dataset`

, from which the search should begin. If unspecified, an addition tree will be generated from`dataset`

, respecting any supplied`constraint`

. Edge lengths are not supported and will be deleted.- ratchIter
Numeric specifying number of iterations of the parsimony ratchet (Nixon 1999) to conduct.

- tbrIter
Numeric specifying the maximum number of TBR break points to evaluate before concluding each search. The counter is reset to zero each time tree score improves. The counter is reset to zero each time tree score improves. One "iteration" comprises breaking a single branch and evaluating all possible reconnections.

- startIter
Numeric: an initial round of tree search with

`startIter`

×`tbrIter`

TBR break points is conducted in order to locate a local optimum before beginning ratchet searches.- finalIter
Numeric: a final round of tree search will evaluate

`finalIter`

×`tbrIter`

TBR break points, in order to sample the final optimal neighbourhood more intensely.- maxHits
Numeric specifying the maximum times that an optimal parsimony score may be hit before concluding a ratchet iteration or final search concluded.

- maxTime
Numeric: after

`maxTime`

minutes, stop tree search at the next opportunity.- quickHits
Numeric: iterations on subsampled datasets will retain

`quickHits`

×`maxHits`

trees with the best score.- concavity
Numeric specifying concavity constant for implied step weighting. The most appropriate value will depend on the dataset, but values around 10–15 often perform well (Goloboff et al. 2018; Smith 2019) . The character string "profile" employs an approximation of profile parsimony (Faith and Trueman 2001) . Set as

`Inf`

for equal step weights, which underperforms step weighting approaches (Goloboff et al. 2008; Goloboff et al. 2018; Goloboff and Arias 2019; Smith 2019) .- ratchEW
Logical specifying whether to use equal weighting during ratchet iterations, improving search speed whilst still facilitating escape from local optima.

- tolerance
Numeric specifying degree of suboptimality to tolerate before rejecting a tree. The default,

`sqrt(.Machine$double.eps)`

, retains trees that may be equally parsimonious but for rounding errors. Setting to larger values will include trees suboptimal by up to`tolerance`

in search results, which may improve the accuracy of the consensus tree (at the expense of resolution) (Smith 2019) .- constraint
Either an object of class

`phyDat`

, in which case returned trees will be perfectly compatible with each character in`constraint`

; or a tree of class`phylo`

, all of whose nodes will occur in any output tree. See`ImposeConstraint()`

and vignette for further examples.- verbosity
Integer specifying level of messaging; higher values give more detailed commentary on search progress. Set to

`0`

to run silently.- method
Unambiguous abbreviation of

`jackknife`

or`bootstrap`

specifying how to resample characters. Note that jackknife is considered to give more meaningful results.- proportion
Numeric between 0 and 1 specifying what proportion of characters to retain under jackknife resampling.

- ...
Additional parameters to

`MaximizeParsimony()`

.

## Value

`MaximizeParsimony()`

returns a list of trees with class
`multiPhylo`

. This lists all trees found during each search step that
are within `tolerance`

of the optimal score, listed in the sequence that
they were first visited, and named according to the step in which they were
first found; it may contain more than `maxHits`

elements.
Note that the default search parameters may need to be increased in order for
these trees to be the globally optimal trees; examine the messages printed
during tree search to evaluate whether the optimal score has stabilized.

The return value has the attribute `firstHit`

, a named integer vector listing
the number of optimal trees visited for the first time in each stage of
the tree search. Stages are named:

`seed`

: starting trees;`start`

: Initial TBR search;`ratchN`

: Ratchet iteration`N`

;`final`

: Final TBR search. The first tree hit for the first time in ratchet iteration three is named`ratch3_1`

.

`Resample()`

returns a `multiPhylo`

object containing a list of
trees obtained by tree search using a resampled version of `dataset`

.

## Details

Tree search commences with `ratchIter`

iterations of the parsimony ratchet
(Nixon 1999)
, which bootstraps the input dataset
in order to escape local optima.
A final round of tree bisection and reconnection (TBR)
is conducted to broaden the sampling of trees.

This function can be called using the R command line / terminal, or through
the "shiny" graphical user interface app (type `EasyTrees()`

to launch).

For detailed documentation of the "TreeSearch" package, including full instructions for loading phylogenetic data into R and initiating and configuring tree search, see the package documentation.

## Resampling

Note that bootstrap support is a measure of the amount of data supporting a split, rather than the amount of confidence that should be afforded the grouping. "Bootstrap support of 100% is not enough, the tree must also be correct" (Phillips et al. 2004) . See discussion in Egan (2006) ; Wagele et al. (2009) ; (Simmons and Freudenstein 2011) ; Kumar et al. (2012) .

For a discussion of suitable search parameters in resampling estimates, see Muller (2005) . The user should decide whether to start each resampling from the optimal tree (which may be quicker, but result in overestimated support values as searches get stuck in local optima close to the optimal tree) or a random tree (which may take longer as more rearrangements are necessary to find an optimal tree on each iteration).

For other ways to estimate clade concordance, see `SiteConcordance()`

.

## References

Brazeau MD, Guillerme T, Smith MR (2019).
“An algorithm for morphological phylogenetic analysis with inapplicable data.”
*Systematic Biology*, **68**(4), 619–631.
doi:10.1093/sysbio/syy083
.

Brazeau MD, Smith MR, Guillerme T (2017).
“MorphyLib: a library for phylogenetic analysis of categorical trait data with inapplicability.”
doi:10.5281/zenodo.815372
.

Egan MG (2006).
“Support versus corroboration.”
*Journal of Biomedical Informatics*, **39**(1), 72–85.
doi:10.1016/j.jbi.2005.11.007
.

Faith DP, Trueman JWH (2001).
“Towards an inclusive philosophy for phylogenetic inference.”
*Systematic Biology*, **50**(3), 331–350.
doi:10.1080/10635150118627
.

Goloboff PA, Arias JS (2019).
“Likelihood approximations of implied weights parsimony can be selected over the Mk model by the Akaike information criterion.”
*Cladistics*, **35**(6), 695–716.
doi:10.1111/cla.12380
.

Goloboff PA, Carpenter JM, Arias JS, Esquivel DRM (2008).
“Weighting against homoplasy improves phylogenetic analysis of morphological data sets.”
*Cladistics*, **24**(5), 758–773.
doi:10.1111/j.1096-0031.2008.00209.x
.

Goloboff PA, Torres A, Arias JS (2018).
“Weighted parsimony outperforms other methods of phylogenetic inference under models appropriate for morphology.”
*Cladistics*, **34**(4), 407–437.
doi:10.1111/cla.12205
.

Kumar S, Filipski AJ, Battistuzzi FU, Kosakovsky Pond SL, Tamura K (2012).
“Statistics and truth in phylogenomics.”
*Molecular Biology and Evolution*, **29**(2), 457–472.
doi:10.1093/molbev/msr202
.

Maddison WP (1993).
“Missing data versus missing characters in phylogenetic analysis.”
*Systematic Biology*, **42**(4), 576–581.
doi:10.1093/sysbio/42.4.576
.

Muller KF (2005).
“The efficiency of different search strategies in estimating parsimony jackknife, bootstrap, and Bremer support.”
*BMC Evolutionary Biology*, **5**(1), 58.
doi:10.1186/1471-2148-5-58
.

Nixon KC (1999).
“The Parsimony Ratchet, a new method for rapid parsimony analysis.”
*Cladistics*, **15**(4), 407–414.
ISSN 0748-3007, doi:10.1111/j.1096-0031.1999.tb00277.x
.

Phillips MJ, Delsuc F, Penny D (2004).
“Genome-scale phylogeny and the detection of systematic biases.”
*Molecular biology and evolution*, **21**(7), 1455–8.
doi:10.1093/molbev/msh137
.

Simmons MP, Freudenstein JV (2011).
“Spurious 99% bootstrap and jackknife support for unsupported clades.”
*Molecular Phylogenetics and Evolution*, **61**(1), 177–191.
doi:10.1016/j.ympev.2011.06.003
.

Smith MR (2019).
“Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets.”
*Biology Letters*, **15**(2), 20180632.
doi:10.1098/rsbl.2018.0632
.

Wagele JW, Letsch H, Klussmann-Kolb A, Mayer C, Misof B, Wagele H (2009).
“Phylogenetic support values are not necessarily informative: the case of the Serialia hypothesis (a mollusk phylogeny).”
*Frontiers in Zoology*, **6**(1), 12–29.
doi:10.1186/1742-9994-6-12
.

## See also

Tree search *via* graphical user interface: `EasyTrees()`

Other split support functions:
`JackLabels()`

,
`Jackknife()`

,
`SiteConcordance`

## Examples

```
## Only run examples in interactive R sessions
if (interactive()) {
# launch "shiny" point-and-click interface
EasyTrees()
# Here too, use the "continue search" function to ensure that tree score
# has stabilized and a global optimum has been found
}
# Load data for analysis in R
library("TreeTools")
data("congreveLamsdellMatrices", package = "TreeSearch")
dataset <- congreveLamsdellMatrices[[42]]
# A very quick run for demonstration purposes
trees <- MaximizeParsimony(dataset, ratchIter = 0, startIter = 0,
tbrIter = 1, maxHits = 4, maxTime = 1/100,
concavity = 10, verbosity = 4)
#>
#> ── BEGIN TREE SEARCH (k = 10) ──────────────────────────────────────────────────
#> → Initial score: 12.6384
#>
#> ── Sample local optimum ────────────────────────────────────────────────────────
#> → TBR depth 1; keeping 4 trees; k = 10
#> ℹ 2024-08-26 10:07:43: Score: 12.6384
#> ✔ 2024-08-26 10:07:44: Tree search terminated with score 12.1193
names(trees)
#> [1] "final_1"
# In actual use, be sure to check that the score has converged on a global
# optimum, conducting additional iterations and runs as necessary.
if (interactive()) {
# Jackknife resampling
nReplicates <- 10
jackTrees <- replicate(nReplicates,
#c() ensures that each replicate returns a list of trees
c(Resample(dataset, trees, ratchIter = 0, tbrIter = 2, startIter = 1,
maxHits = 5, maxTime = 1 / 10,
concavity = 10, verbosity = 0))
)
# In a serious analysis, more replicates would be conducted, and each
# search would undergo more iterations.
# Now we must decide what to do with the multiple optimal trees from
# each replicate.
# Treat each tree equally
JackLabels(ape::consensus(trees), unlist(jackTrees, recursive = FALSE))
# Take the strict consensus of all trees for each replicate
JackLabels(ape::consensus(trees), lapply(jackTrees, ape::consensus))
# Take a single tree from each replicate (the first; order's irrelevant)
JackLabels(ape::consensus(trees), lapply(jackTrees, `[[`, 1))
}
# Tree search with a constraint
constraint <- MatrixToPhyDat(c(a = 1, b = 1, c = 0, d = 0, e = 0, f = 0))
characters <- MatrixToPhyDat(matrix(
c(0, 1, 1, 1, 0, 0,
1, 1, 1, 0, 0, 0), ncol = 2,
dimnames = list(letters[1:6], NULL)))
MaximizeParsimony(characters, constraint = constraint, verbosity = 0)
#> ✔ Initialized 1 distinct constraints.
#> 1 phylogenetic tree
```