Tree Search with Profile Parsimony

Martin R. Smith


Profile Parsimony (Faith & Trueman, 2001) finds the tree that is most faithful to the information contained within a given dataset. It is the ‘exact solution’ that implied weights parsimony approximates.

Profile Parsimony is currently implemented in ‘TreeSearch’ only for binary characters with no ambiguous tokens.

Getting started

A companion vignette gives details on installing the package and getting up and running.

Once installed, load the inapplicable package into R using


In order to reproduce the random elements of this document, set a random seed:

# Set a random seed so that random functions in this document are reproducible
suppressWarnings(RNGversion("3.5.0")) # Until we can require R3.6.0

View the results

In parsimony search, it is good practice to consider trees that are slightly suboptimal (Smith, 2019).

Here, we’ll take a consensus that includes all trees that are suboptimal by up to 1.5 bits. To sample this region of tree space well, the trick is to use large values of ratchHits and ratchIter, and small values of searchHits and searchiter, so that many runs don’t quite hit the optimal tree. In a serious study, you would want to sample many more than the 25 Ratchet hits (ratchHits) we’ll settle for here, probably using many more Ratchet iterations.

suboptimals <- ProfileRatchet(better.tree, my.prepdata, 
                              swappers = list(RootedTBRSwap),
                              returnAll = TRUE, suboptimal = 5, 
                              ratchHits = 25, ratchIter = 500, 
                              bootstrapHits = 15, bootstrapIter = 450,
                              searchHits = 10, searchIter = 100)

The consensus of these slightly suboptimal trees provides a less resolved, but typically more reliable, summary of the signal with the phylogenetic dataset (Smith, 2019):

par(mar=rep(0.25, 4), cex=0.75)
plot(my.consensus <- ape::consensus(suboptimals))


Faith, D. P., & Trueman, J. W. H. (2001). Towards an inclusive philosophy for phylogenetic inference. Systematic Biology, 50(3), 331–350. doi:10.1080/10635150118627

Nixon, K. C. (1999). The Parsimony Ratchet, a new method for rapid parsimony analysis. Cladistics, 15(4), 407–414. doi:10.1111/j.1096-0031.1999.tb00277.x

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