phytools (version 2.1-1)

anc.trend: Ancestral character estimation with a trend

Description

This function estimates the evolutionary parameters and ancestral states for Brownian evolution with a directional trend.

Usage

anc.trend(tree, x, maxit=2000)

Value

An object of class "anc.trend" with the following components:

ace

a vector with the ancestral states.

mu

a trend parameter per unit time.

sig2

the variance of the BM process, \(\sigma^2\).

logL

the log-likelihood.

convergence

the value of $convergence returned by optim() (0 is good).

Arguments

tree

an object of class "phylo".

x

a vector of tip values for species; names(x) should be the species names.

maxit

an optional integer value indicating the maximum number of iterations for optimization.

Author

Liam Revell

Details

Note that this will generally only work and produce sensible results for a phylogeny with some non-contemporaneous tips (i.e., a tree with some fossil species).

The function uses optim with method= "L-BFGS-B" internally; however, optimization is only constrained for the sig2 which must be > 0.

References

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

See Also

ace, anc.Bayes, anc.ML, optim

Examples

## simulate tree & data using fastBM with a trend (m!=0)
tree<-rtree(n=26,tip.label=LETTERS)
x<-fastBM(tree,mu=4,internal=TRUE)
a<-x[as.character(1:tree$Nnode+Ntip(tree))]
x<-x[tree$tip.label]
## fit no trend model
fit.bm<-anc.ML(tree,x,model="BM")
print(fit.bm)
## fit trend model
fit.trend<-anc.trend(tree,x)
print(fit.trend)
## compare trend vs. no-trend models & estimates
AIC(fit.bm,fit.trend)
layout(matrix(c(3,4,1,2,5,6),3,2,byrow=TRUE),
    heights=c(1.5,3,1.5),widths=c(3,3))
xlim<-ylim<-range(c(a,fit.bm$ace,
    fit.trend$ace))
plot(a,fit.bm$ace,pch=19,
    col=make.transparent("blue",0.5),
    xlab="true ancestral states",
    ylab="ML estimates",
    main=paste("Comparison of true and estimated",
    "\nstates under a no-trend model"),font.main=3,
    cex.main=1.2,bty="l",cex=1.5,
    xlim=xlim,ylim=ylim)
lines(xlim,ylim,lty="dotted")
plot(a,fit.trend$ace,pch=19,
    col=make.transparent("blue",0.5),
    xlab="true ancestral states",
    ylab="ML estimates",
    main=paste("Comparison of true and estimated",
    "\nstates under a trend model"),font.main=3,
    cex.main=1.2,bty="l",cex=1.5,
    xlim=xlim,ylim=ylim)
lines(xlim,ylim,lty="dotted")
par(mfrow=c(1,1))