Constant Rate Birth-Death Models: Split Models

Share:

Description

Create a likelihood function for a birth-death model where the tree is partitioned into regions with different parameters.

Usage

1
make.bd.split(tree, nodes, split.t, sampling.f=NULL, unresolved=NULL)

Arguments

tree

An ultrametric bifurcating phylogenetic tree, in ape “phylo” format.

nodes

Vector of nodes that will be split (see Details).

split.t

Vector of split times, same length as nodes (see Details).

sampling.f

Probability of an extant species being included in the phylogeny (sampling fraction). By default, all extant species are assumed to be included.

unresolved

Unresolved clade information. This is a named vector, with the number of species as the value and names corresponding to tip labels. Tips that represent a single species should not be included in this vector. For example sp1=10, sp2=2, would mean that sp1 represents 10 species, while sp2 represents two. These labels must exist in tree$tip.label and all other tips are assumed to represent one species.

Details

Branching times can be controlled with the split.t argument. If this is Inf, split at the base of the branch (as in MEDUSA). If 0, split at the top (closest to the present, as in the new option for MEDUSA). If 0 < split.t < Inf then we split at that time on the tree (zero is the present, with time growing backwards).

This function is related to MEDUSA (Alfaro et al. 2009), but does not include any of the code for efficiently moving between different splits (split creation here is fairly slow). The primary use for this model is for generating starting points for state dependent split models (e.g., make.bisse.split) and testing a priori splits.

Author(s)

Richard G. FitzJohn

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
set.seed(1)
pars <- c(.1, .03)
phy <- trees(pars, "bd", max.taxa=30)[[1]]

## Here is the phylogeny:
plot(phy, show.node.label=TRUE, label.offset=.1, font=1, cex=.75,
     no.margin=TRUE)

## Construct the plain likelihood function as a benchmark:
lik <- make.bd(phy)
lik(pars) # -21.74554

## Split this phylogeny at three points: nd11, nd13 and nd26
nodes <- c("nd11", "nd13", "nd26")

## This is the index in ape's node indexing:
nodes.i <- match(nodes, phy$node.label) + length(phy$tip.label)

nodelabels(node=nodes.i, pch=19, cex=2, col="#FF000099")

## To make a split likelihood function, pass the node locations and times in:
lik.s <- make.bd.split(phy, nodes)

## The parameters must be a list of the same length as the number of
## partitions.  Partition '1' is the root partition, and partition i is
## the partition rooted at the node[i-1]
pars4 <- rep(pars, 4)
names(pars4) <- argnames(lik.s)

## Run the likelihod calculation:
lik.s(pars4) # -21.74554

## These are basically identical (to acceptable tolerance)
lik.s(pars4) - lik(pars)

## You can use the labelled nodes rather than indices:
lik.s2 <- make.bd.split(phy, nodes)
identical(lik.s(pars4), lik.s2(pars4))

## All the usual ML/MCMC functions work as before:
fit <- find.mle(lik.s, pars4)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.