BBMV (version 2.1)

lnL_FPK: Creation of the likelihood function

Description

Functions that builds the likelihood function of the FPK or BBMV model

Usage

lnL_FPK(tree, trait, a = NULL, b = NULL, c = NULL, Npts)
lnL_BBMV(tree, trait,bounds, a = NULL, b = NULL, c = NULL, Npts)

Arguments

tree

A phylogenetic tree in 'phylo' format

trait

A named vector of trait values for the tips of the tree. It should match tip labels in the phylogeny. Alternatively, a named list with one element per tip in the tree, each element being in turn a numeric vector with multiple measurements of the trait for this tip.

bounds

The two bounds that constrain trait values when fitting the BBMV model. Specified by a numeric vector containing the minimum and maximum bound of the trait interval as the first and second element, respectively.

a

The value of the x^4 term in the evolutionary potential. If set to NULL (the default), this parameter will be estimated. If a numeric value is provided, this parameter will be fixed to the value specified.

b

The value of the quadratic term in the evolutionary potential. If set to NULL (the default), this parameter will be estimated. If a numeric value is provided, this parameter will be fixed to the value specified.

c

The value of the linear term in the evolutionary potential. If set to NULL (the default), this parameter will be estimated. If a numeric value is provided, this parameter will be fixed to the value specified.

Npts

The number of points used in the discretization procedure.

Value

A list of several items, including the data and model call, but most importantly the likelihood function ($fun element).

See Also

find.mle_FPK

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)) 
# create a likelihood function for the FPK model: 
ll_FPK4=lnL_FPK(tree,TRAIT,Npts=25,a=NULL,b=NULL,c=NULL) # the full model
# }