Is there a way to calculate the number of parsimony informative characters in a morphological dataset in R?

Question

I have a dataset of binary morphological characters and would like to know how many of them are parsimony informative. Is there a way to calculate this with an R package?

Answer 1

The ape library (Analyses of Phylogenetics and Evolution) using the MPR method (Most Parsimonious Reconstruction).

Information on ape is here.

The format

install.packages(“ape”)
library(ape)
MPR(x, phy, outgroup)

x : a vector of integers.

phy : an object of class "phylo"; the tree must be unrooted and fully dichotomous.

outgroup : an integer or a character string giving the tip of phy used as outgroup.

Rooting the tree is recommended. In the example rooting isn't used but an outgroup is used as taxa t1, that will be fine.

Generating random data,

vectx <- rpois(50, 1) # make random data
tree <- rtree(50, rooted = FALSE)  # also rtree(50) make random tree
MPR(vectx, tree, "t1") 

Thats it, there's nothing more. Data, tree = steps.

Clarification the question is number of parsimonious characters. As a strict definition the number of informative characters is the same as the number of steps and thats what MPR will describe ... thats easy.

There are other possible meanings

1. The number of parsimonious characters/steps to make a tree is calculated via MPR as above.
2. if its the actual changes at a given position in the alignment (or vector) for each node in the tree - thats the answer below - but thats complicated.
3. If its simply is this site (not character) is parsimoniously informative and this one isn't then the "alignment" or vector could be used one character position at a time and subject to MPR and the step length would determine how parsimoniously informative each site was ... thus point 3 is easily doable in R

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Note Calculating the actual sites on the alignment and associated changes per node is easily done outside R, but within R I don't know if thats doable.

Wait, I'm wrong it's doable but I would envisage its difficult to implement. Calculating parsimonious characters for each given node in a tree - yes I do that - doing that in R ... errhhh thats not me. There are Pythonic ways in, but I've not implemented them.