convSig: Uses simulations to assess the significance of C1-C4 measures...

In convevol: Analysis of Convergent Evolution

Uses simulations to assess the significance of C1-C4 measures of convergent evolution as described in Stayton (2015). Code written by Jonathan S. Mitchell for Zelditch et al. (2017)

Description

convSig calculates the significance of measures of convergent evolution.

Usage

convSig(phy, traits, focaltaxa, nsim = 1000)

Arguments

phy	The phylogeny of interest in phylo format
traits	Phenotypic data for all tips
focaltaxa	A list consisting of the names of all putatively convergent taxa
nsim	The number of simulations to use to assess significance

Details

This script simulates data according to a Brownian motion model of evolution, and then assesses convergene on that simulated data. The number of times that the simulated data produces greater convergence than that observed in the empirical data is used to calculate a P-value.

Value

C1-C4 convergence measures for all pairs of putatively convergent taxa and their associated p-values.

References

Stayton, C.T. 2015. The definition, recognition, and interpretation of convergent evolution, and two new measures for quantifying and assessing the significance of convergence. Evolution 69:2140-2453.

Zelditch, M.L., J. Ye, J.S. Mitchell, and D.L. Swiderski. 2017. Rare ecomorphological convergence on a complex adaptive landscape: Body size and diet mediate evolution of jaw shape in squirrels (Sciuridae). Evolution 71:633-649.

Examples

phy<-rtree(100)
traits<-fastBM(phy,nsim=3)
focaltaxa<-c("t1","t50","t100")
answer<-convSig(phy,traits,focaltaxa,nsim=10)

convevol documentation built on July 9, 2023, 5:07 p.m.