diversitree (version 0.10-1)

make.musse.split: Multiple State Speciation and Extinction Model: Split Models

Description

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

Usage

make.musse.split(tree, states, k, nodes, split.t,
                 sampling.f=NULL, strict=TRUE, control=list())

Arguments

tree

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

states

A vector of character states, each of which must be an integer between 1 and k. This vector must have names that correspond to the tip labels in the phylogenetic tree (tree$tip.label). For tips corresponding to unresolved clades, the state should be NA.

k

The number of states.

nodes

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

split.t

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

sampling.f

Vector of length k where sampling.f[i] is the proportion of species in state i that are present in the phylogeny. A value of c(0.5, 0.75, 1) means that half of species in state 1, three quarters of species in state 2, and all species in state 3 are included in the phylogeny. By default all species are assumed to be known

strict

The states vector is always checked to make sure that the values are integers on 1:k. If strict is TRUE (the default), then the additional check is made that every state is present. The likelihood models tend to be poorly behaved where states are missing, but there are cases (missing intermediate states for meristic characters) where allowing such models may be useful.

control

List of control parameters for the ODE solver. See details in make.bisse.

Author

Richard G. FitzJohn

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).

Examples

## This example picks up from the tree used in the ?make.musse example.

## First, simulate the tree:
set.seed(2)
pars <- c(.1,  .15,  .2,  # lambda 1, 2, 3
          .03, .045, .06, # mu 1, 2, 3
          .05, 0,         # q12, q13
          .05, .05,       # q21, q23
          0,   .05)       # q31, q32
phy <- tree.musse(pars, 30, x0=1)

## Here is the phylogeny, with true character history superposed:
h <- history.from.sim.discrete(phy, 1:3)
plot(h, phy, show.node.label=TRUE, font=1, cex=.75, no.margin=TRUE)

## Here is a plain MuSSE function for later comparison:
lik.m <- make.musse(phy, phy$tip.state, 3)
lik.m(pars) # -110.8364

## Split this phylogeny at three points: nd16 and nd25, splitting it
## into three chunks
nodes <- c("nd16", "nd25")
nodelabels(node=match(nodes, phy$node.label) + length(phy$tip.label),
           pch=19, cex=2, col="#FF000099")

## To make a split BiSSE function, pass the node locations and times
## in.  Here, we'll use 'Inf' as the split time to mimick MEDUSA's
## behaviour of placing the split at the base of the branch subtended by
## a node.
lik.s <- make.musse.split(phy, phy$tip.state, 3, nodes, split.t=Inf)

## 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]:
argnames(lik.s)

## Because we have two nodes, there are three sets of parameters.
## Replicate the original list to get a starting point for the analysis:
pars.s <- rep(pars, 3)
names(pars.s) <- argnames(lik.s)

lik.s(pars.s) # -110.8364

## This is basically identical (to acceptable tolerance) to the plain
## MuSSE version:
lik.s(pars.s) - lik.m(pars)

## The resulting likelihood function can be used in ML analyses with
## find.mle.  However, because of the large number of parameters, this
## may take some time (especially with as few species as there are in
## this tree - getting convergence in a reasonable number of iterations
## is difficult).
if (FALSE) {
fit.s <- find.mle(lik.s, pars.s, control=list(maxit=20000))
}

## Bayesian analysis also works, using the mcmc function.  Given the
## large number of parameters, priors will be essential, as there will
## be no signal for several parameters.  Here, I am using an exponential
## distribution with a mean of twice the state-independent
## diversification rate.
if (FALSE) {
prior <- make.prior.exponential(1/(-2*diff(starting.point.bd(phy))))
samples <- mcmc(lik.s, pars.s, 100, prior=prior, w=1, print.every=10)
}