get.landscape.FPK: Plot macroevolutionary landscapes estimated by the FPK or...

In BBMV: Models for Continuous Traits Evolving in Macroevolutionary Landscapes of any Shape

Description

Plots a line representing the adaptive landscape estimated in a BBM+V or an FPK model.

Usage

get.landscape.FPK(fit, Npts = 100, main = "Macroevolutionary landscape"
  , ylab = "N.exp(-V)", xlab = "Trait", xlim = NULL, ylim = NULL)
add.ML.landscape.FPK(fit,Npts=100,COLOR=1,LTY='dashed')

Arguments

fit	An FPK model fit, as returned by find.mle_FPK.
Npts	The number of points used to discretize the trait interval for plotting.
main	Title for the plot.
ylim	The upper limit of the plotting region when multiple adaptive landscapes are plotted together.
xlim	The limits of thex-axis.
ylab	Label of the y-axis.
xlab	Label of the x-axis.
COLOR	The color of the line when added to a plot of the posterior of a MCMC run.
LTY	The type of the line when added to a plot of the posterior of a MCMC run.

Value

A plot of the adaptive landscape across the trait interval.

Author(s)

F. C. Boucher

Examples

## Not run: 
# Simulate data: tree + continuous trait
library(geiger)
tree=sim.bdtree(stop='taxa',n=10) # tree with few tips for quick tests
tree$edge.length=100*tree$edge.length/max(branching.times(tree)) # rescale the tree 
# Simulate trait evolving on a macroevolutionary landscape with two peaks of equal heights
x=seq(from=-1.5,to=1.5,length.out=100)
bounds=c(min(x),max(x)) # the bounds we use for simulating
V6=10*(x^4-0.5*(x^2)+0.*x) # this is the evolutionary potential: it has two wells
TRAIT= Sim_FPK(tree,x0=0,V=V6,sigma=10,bounds=c(-5, 5)) 
# fit the FPK model: 
ll_FPK4=lnL_FPK(tree,TRAIT,Npts=25,a=NULL,b=NULL,c=NULL) # the full model
fit4=find.mle_FPK(model=ll_FPK4)
# Plot the landscape estimated
get.landscape.FPK(fit=fit4)

## End(Not run)

BBMV documentation built on May 1, 2019, 10:26 p.m.