su.bayes: A function to compute results of QIRP, QIHP, and QIPP across...
In carolinafishes/PhyInformR: Rapid Calculation of Phylogenetic Information Content

Description Usage Arguments Details Value Author(s) References Examples

This function tracks a four-taxon tree over a user defined distribution of topologies to computer QIRP, QIHP, and QIPP while allowing for a user defined substitution model and base frequencies.

1	su.bayes(a, b, c, d, e, f, Pi_T, Pi_C, Pi_A, Pi_G, rate_vector, quart, tree)

`a`	substitution model parameter. a=rTC
`b`	substitution model parameter. b=rAT
`c`	substitution model parameter. c=rGT
`d`	substitution model parameter. d=rCA
`e`	substitution model parameter. e=rGC
`f`	substitution model parameter. e=rGA
`Pi_T`	Base Frequency of T
`Pi_C`	Base Frequency of C
`Pi_A`	Base Frequency of A
`Pi_G`	Base Frequency of G
`rate_vector`	site rate vector, converted to class 'matrix'
`quart`	Four-taxa that comprise the phylogenetic tree quartet of interest quart<-c('taxonA','taxonB','taxonC','taxonD')
`tree`	A distribution of phylogenetic trees

Please note that base frequencies must sum to 1.

Function returns a matrix of branch lenghts, internode lengths, QIRP, QIHP. and QIPP values

A. Dornburg

Townsend, J. P., Su, Z., and Tekle, Y. I. “Phylogenetic Signal and Noise: Predicting the Power of a Data Set to Resolve Phylogeny” Systematic biology 61, no. 5 (2012): 835–849. Su, Z., Zhuo, S., Zheng, W., Francesc, L.-G., and Townsend, J. P. “The Impact of Incorporating Molecular Evolutionary Model into Predictions of Phylogenetic Signal and Noise” Frontiers in Ecology and Evolution 2, (2014): doi:10.3389/fevo.2014.00011, Available at http://dx.doi.org/10.3389/fevo.2014.00011 Su, Z. and Townsend, J. P. “Utility of Characters Evolving at Diverse Rates of Evolution to Resolve Quartet Trees with Unequal Branch Lengths: Analytical Predictions of Long-Branch Effects” BMC evolutionary biology 15, (2015): 86.

library("ape")
read.tree(system.file("extdata","polypterus_trees.phy",package="PhyInformR"))->trees
trees<-trees[1:10]
as.matrix(rag1)->rates
quart<-c("Polypterus_congicus", "Polypterus_bichir", 
"Polypterus_ansorgii", "Polypterus_endlicheri")
a<-1
b<-1
c<-1
d<-1
e<-1
f<-1
Pi_T<-.25
Pi_C<-.25
Pi_A<-.25
Pi_G<-.25
su.bayes(a,b,c,d,e,f, Pi_T, Pi_C, Pi_A, Pi_G, rates, quart, trees)->final