bmPlot: Simulates and visualizes continuous trait evolution on a...

In liamrevell/phytools: Phylogenetic Tools for Comparative Biology (and Other Things)

Simulates and visualizes continuous trait evolution on a phylogeny

Description

bmPlot conducts a discrete-time Brownian motion simulation on an input tree, plots the outcome, and returns the tip and internal node states to the user as a named vector.

sim.absorbing uses time discretization as well, but to conduct a simulation of either unbounded Brownian motion (if bounds=c(-Inf,Inf)), reflective bounded Brownian motion (if reflective=TRUE), or bounded Brownian motion with absorbing bounds.

sim.reflective is a synonym for sim.absorbing, but with the optional argument reflective=TRUE by default.

Usage

bmPlot(tree, type="BM", anc=0, sig2=1/1000, ngen=1000, ...)
sim.absorbing(tree, x0=0, sig2=1, bounds=c(-Inf,Inf), nsteps=1000, ...)
sim.reflective(tree, x0=0, sig2=1, bounds=c(-Inf,Inf), nsteps=1000, ...)

Arguments

tree	a phylogenetic tree as an object of class "phylo".
type	the type of plot to create in bmPlot. See Description.
anc	the ancestral value for the root node in bmPlot.
sig2	the BM rate (variance of the stochastic diffusion process), \sigma^2.
ngen	number of generations for the simulation: will rescale the tree to this total length in bmPlot.
x0	the ancestral value of the root node in sim.absorbing.
bounds	absorbing or reflecting bounds in sim.absorbing. If bounds=c(-Inf,Inf) then the simulated process is unbounded.
nsteps	in sim.absorbing, the number of steps for the time discretization required for forward-in-time simulation.
...	arguments to be passed to different methods.

Details

bmPlot will first rescale and round the branch lengths to integer length, if they are not already in integer values. If integer branch lengths are provided, the user should also set ngen to be equal to the total height of the tree in generations (and thus avoid rescaling). For type="threshold" the visualization is of the threshold model (Felsenstein 2012), in which the evolving character is liability and the segments of evolution are colored by their value for the threshold trait. If type="threshold" is used, the function requires at least one addition input: thresholds, a vector containing the ordered thresholds between states. The user can also provide the colors for plotting in colors. Note that one more color than threshold should be provided as one threshold implies two states; two thresholds, three states; etc. If no value for colors is provided, the function will recycle a set of four colors up to the number of times required by thresholds. Finally, the optional argument return.tree=TRUE will tell the function to return a list with the tip and note states and an object of class "phylo" with (for type="threshold"), the state for the threshold model through time mapped on the branches of the tree in discrete time.

Optional arguments in sim.absorbing include reflective, which defaults to reflective=FALSE. This function uses discretization to conduct a forward-in-time simulation, but does not rescale the tree. sim.reflective calls sim.absorbing internally, but with the optional argument reflective=TRUE by default.

Value

bmPlot conducts and plots discrete time Brownian simulation and returns a vector containing the simulated states at internal nodes and tips of the tree. It also returns, by default (although this can be turned off) a tree with the branch lengths in discrete time and with a mapped discrete character (for type="threshold").

sim.absorbing returns a named vector of numeric values containing tip states.

Author(s)

Liam Revell [e-mail]

References

Felsenstein, J. (2012) A comparative method for both discrete and continuous characters using the threshold model. American Naturalist, 179, 145-156.

Revell, L. J. (2014) Ancestral character estimation under the threshold model from quantitative genetics. Evolution, 68, 743-759.

Revell, L. J. (2024) phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things). PeerJ, 12, e16505.

See Also

bounded_bm, fastBM, pbtree, phenogram, threshBayes

Examples

## set seed
set.seed(999)

## plot BM simulation on 12 taxon tree
tree<-pbtree(n=12)
par(mfrow=c(1,2),mar=c(5.1,4.1,4.1,0.1))
x<-bmPlot(tree,bty="l")
plotTree(tree,direction="upwards",
    mar=c(5.1,0.1,4.1,1.1),ftype="off")

## reset par to default values
par(mfrow=c(1,1),mar=c(5.1,4.1,4.1,2.1))

## plot simulation of a threshold character
par(mfrow=c(1,2),mar=c(5.1,4.1,4.1,0.1))
tt<-bmPlot(tree,type="threshold",thresholds=c(0,1,2),
    bty="l")
plot(tt$tree,direction="upwards",
    mar=c(5.1,0.1,4.1,1.1),ftype="off",
    colors=setNames(c("black","red","blue"),
    letters[1:3]),lwd=3)

## reset par to default values
par(mfrow=c(1,1),mar=c(5.1,4.1,4.1,2.1))

liamrevell/phytools documentation built on June 14, 2025, 7:58 p.m.