make.bisseness {diversitree}

Binary State Speciation and Extinction (Node Enhanced State Shift) Model

Description

Prepare to run BiSSE-ness (Binary State Speciation and Extinction (Node Enhanced State Shift)) on a phylogenetic tree and character distribution. This function creates a likelihood function that can be used in maximum likelihood or Bayesian inference.

Usage

make.bisseness(tree, states, unresolved=NULL, sampling.f=NULL,
               nt.extra=10, 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 0 or 1, or NA if the state is unknown. 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.

unresolved

Unresolved clade information: see section below for structure.

sampling.f

Vector of length 2 with the estimated proportion of extant species in state 0 and 1 that are included in the phylogeny. A value of c(0.5, 0.75) means that half of species in state 0 and three quarters of species in state 1 are included in the phylogeny. By default all species are assumed to be known.

nt.extra

The number of "extra" species to include in the unresolved clade calculations. This is in addition to the largest included unresolved clade.

control

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

strict

The states vector is always checked to make sure that the values are 0 and 1 only. If strict is TRUE (the default), then the additional check is made that every state is present at least once in the tree. The likelihood models tend to be poorly behaved where a state is not represented on the tree.

Details

make.bisse returns a function of class bisse. This function has argument list (and default values) [RICH: Update to BiSSEness?]

    f(pars, condition.surv=TRUE, root=ROOT.OBS, root.p=NULL,
      intermediates=FALSE)
  

The arguments are interpreted as

• pars A vector of 10 parameters, in the order lambda0, lambda1, mu0, mu1, q01, q10, p0c, p0a, p1c, p1a.

• condition.surv (logical): should the likelihood calculation condition on survival of two lineages and the speciation event subtending them? This is done by default, following Nee et al. 1994. For BiSSE-ness, equation (A5) in Magnuson-Ford and Otto describes how conditioning on survival alters the likelihood of observing the data.

• root: Behaviour at the root (see Maddison et al. 2007, FitzJohn et al. 2009). The possible options are

  • ROOT.FLAT: A flat prior, weighting D_0 and D_1 equally.

  • ROOT.EQUI: Use the equilibrium distribution of the model, as described in Maddison et al. (2007) using equation (A6) in Magnuson-Ford and Otto.

  • ROOT.OBS: Weight D_0 and D_1 by their relative probability of observing the data, following FitzJohn et al. 2009:

    D = D_0\frac{D_0}{D_0 + D_1} + D_1\frac{D_1}{D_0 + D_1}

  • ROOT.GIVEN: Root will be in state 0 with probability root.p[1], and in state 1 with probability root.p[2].

  • ROOT.BOTH: Don't do anything at the root, and return both values. (Note that this will not give you a likelihood!).

• root.p: Root weightings for use when root=ROOT.GIVEN. sum(root.p) should equal 1.

• intermediates: Add intermediates to the returned value as attributes:

  • cache: Cached tree traversal information.

  • intermediates: Mostly branch end information.

  • vals: Root D values.

  At this point, you will have to poke about in the source for more information on these.

Unresolved clade information

Since 0.10.10 this is no longer supported. See the package README for more information.

This must be a data.frame with at least the four columns

• tip.label, giving the name of the tip to which the data applies

• Nc, giving the number of species in the clade

• n0, n1, giving the number of species known to be in state 0 and 1, respectively.

These columns may be in any order, and additional columns will be ignored. (Note that column names are case sensitive).

An alternative way of specifying unresolved clade information is to use the function make.clade.tree to construct a tree where tips that represent clades contain information about which species are contained within the clades. With a clade.tree, the unresolved object will be automatically constructed from the state information in states. (In this case, states must contain state information for the species contained within the unresolved clades.)

Author(s)

Karen Magnuson-Ford

References

FitzJohn R.G., Maddison W.P., and Otto S.P. 2009. Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Syst. Biol. 58:595-611.

Maddison W.P., Midford P.E., and Otto S.P. 2007. Estimating a binary character's effect on speciation and extinction. Syst. Biol. 56:701-710.

Magnuson-Ford, K., and Otto, S.P. 2012. Linking the investigations of character evolution and species diversification. American Naturalist, in press.

Nee S., May R.M., and Harvey P.H. 1994. The reconstructed evolutionary process. Philos. Trans. R. Soc. Lond. B Biol. Sci. 344:305-311.

See Also

make.bisse for the model with no state change at nodes.

tree.bisseness for simulating trees under the BiSSE-ness model.

constrain for making submodels, find.mle for ML parameter estimation, mcmc for MCMC integration, and make.bd for state-independent birth-death models.

The help pages for find.mle has further examples of ML searches on full and constrained BiSSE models.

Examples

## Due to a change in sample() behaviour in newer R it is necessary to
## use an older algorithm to replicate the previous examples
if (getRversion() >= "3.6.0") {
  RNGkind(sample.kind = "Rounding")
}

## First we simulate a 50 species tree, assuming cladogenetic shifts in
## the trait (i.e., the trait only changes at speciation).
## Red is state '1', black is state '0', and we let red lineages
## speciate at twice the rate of black lineages.
## The simulation starts in state 0.
set.seed(3)
pars <- c(0.1, 0.2, 0.03, 0.03, 0, 0, 0.1, 0, 0.1, 0)
phy <- tree.bisseness(pars, max.taxa=50, x0=0)
phy$tip.state

h <- history.from.sim.discrete(phy, 0:1)
plot(h, phy)

## This builds the likelihood of the data according to BiSSEness:
lik <- make.bisseness(phy, phy$tip.state)
## e.g., the likelihood of the true parameters is:
lik(pars) # -174.7954

## ML search:  First we make hueristic guess at a starting point, based
## on the constant-rate birth-death model assuming anagenesis (uses
## \link{make.bd}).
startp <- starting.point.bisse(phy)

## We then take the total amount of anagenetic change expected across
## the tree and assign half of this change to anagenesis and half to
## cladogenetic change at the nodes as a heuristic starting point:
t <- branching.times(phy)
tryq <- 1/2 * startp[["q01"]] * sum(t)/length(t)
p <- c(startp[1:4], startp[5:6]/2, p0c=tryq, p0a=0.5, p1c=tryq, p1a=0.5)