hisse: Hidden State Speciation and Extinction

Description Usage Arguments Details Value Author(s) References Examples

View source: R/hisse.R

Description

Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character distribution.

Usage

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hisse(phy, data, f=c(1,1), turnover=c(1,2), eps=c(1,2), 
hidden.states=FALSE, trans.rate=NULL, condition.on.survival=TRUE, 
root.type="madfitz", root.p=NULL, includes.fossils=FALSE, k.samples=NULL, 
strat.intervals=NULL, sann=TRUE, sann.its=1000, bounded.search=TRUE, 
max.tol=.Machine$double.eps^.50, starting.vals=NULL, turnover.upper=10000, eps.upper=3,
trans.upper=100, restart.obj=NULL, ode.eps=0, dt.threads=1)

Arguments

phy

a phylogenetic tree, in ape “phylo” format.

data

a matrix (or dataframe) with three columns. The first column containing the species names and the second and third containing the binary character information. Character "0" is on column 2 and chracter "1" is on column 3. A value of 0 means character absent and a value of 1 character present. The input of data follows a Pagel model. See 'Details'.

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(0.25, 0.5) means that 25 percent of species in state 0 and 50 percent of species in state 1 are included in the phylogeny. By default all species are assumed to be sampled.

turnover

a numeric vector indicating the number of free turnover parameters in the model.

eps

a numeric vector indicating the number of free extinction fraction parameters in the model.

hidden.states

a logical indicating whether the model includes a hidden states. The default is FALSE.

trans.rate

provides the transition rate model. See function TransMatMakerHiSSE.

condition.on.survival

a logical indicating whether the likelihood should be conditioned on the survival of two lineages and the speciation event subtending them (Nee et al. 1994). The default is TRUE.

root.type

indicates whether root summarization follow the procedure described by FitzJohn et al. 2009, “madfitz” or Herrera-Alsina et al. 2018, “herr_als”.

root.p

a vector indicating fixed root state probabilities. The default is NULL.

includes.fossils

a logical indicating whether the tree contains fossil taxa. The default is FALSE.

k.samples

a table of extinct individuals with sampled descendants. See details for how the table must be formatted.

strat.intervals

a table of extinct individuals with sampled descendants. See vignette for how the table must be formatted.

sann

a logical indicating whether a two-step optimization procedure is to be used. The first includes a simulate annealing approach, with the second involving a refinement using subplex. The default is TRUE.

sann.its

a numeric indicating the number of times the simulated annealing algorithm should call the objective function.

bounded.search

a logical indicating whether or not bounds should be enforced during optimization. The default is is TRUE.

max.tol

supplies the relative optimization tolerance to subplex.

starting.vals

a numeric vector of length 3 with starting values for the model for all areas and hidden states. Position [1] sets turnover, [2] sets extinction fraction, and [3] transition rates.

turnover.upper

sets the upper bound for the turnover parameters.

eps.upper

sets the upper bound for the eps parameters.

trans.upper

sets the upper bound for the transition rate parameters.

restart.obj

an object of class that contains everything to restart an optimization.

ode.eps

sets the tolerance for the integration at the end of a branch. Essentially if the sum of compD is less than this tolerance, then it assumes the results are unstable and discards them. The default is set to zero, but in testing a value of 1e-8 can sometimes produce stable solutions for both easy and very difficult optimization problems.

dt.threads

sets the number of threads available to data.table. In practice this need not change from the default of 1 thread, as we have not seen any speedup from allowing more threads.

Details

This function sets up and executes a new and faster version of the HiSSE model. Note that the four-state character-independent model can be called from this command in addition to the two-state BiSSE model and the full character-dependent HiSSE model. See vignette on how to set this up.

The “trans.rate” input is the transition model and has an entirely different setup than turnover rates and extinction fraction. See TransMatMakerHiSSE function for more details.

For the “root.type” option, we are currently maintaining the previous default of “madfitz”. However, it was recently pointed out by Herrera-Alsina et al. (2018) that at the root, the individual likelihoods for each possible state should be conditioned prior to averaging the individual likelihoods across states. This can be set doing “herr_als”. It is unclear to us which is exactly correct, but it does seem that both “madfitz” and “herr_als” behave exactly as they should in the case of character-independent diversification (i.e., reduces to likelihood of tree + likelihood of trait model). We've also tested the behavior and the likelihood differences are very subtle and the parameter estimates in simulation are nearly indistinguishable from the “madfitz” conditioning scheme. We provide both options and encourage users to try both and let us know conditions in which the result vary dramatically under the two root implementations. We suspect they do not.

For user-specified “root.p”, you should specify the probability for each state. If you are doing a hidden model, there will be four states: 0A, 1A, 0B, 1B. So if you wanted to say the root had to be state 0, you would specify “root.p = c(0.5, 0, 0.5, 0)”.

This code will completely replace the original hisse function in the next version.

Value

hisse returns an object of class hisse.fit. This is a list with elements:

$loglik

the maximum negative log-likelihood.

$AIC

Akaike information criterion.

$AICc

Akaike information criterion corrected for sample-size.

$solution

a matrix containing the maximum likelihood estimates of the model parameters.

$index.par

an index matrix of the parameters being estimated.

$f

user-supplied sampling frequencies.

$hidden.states

a logical indicating whether hidden states were included in the model.

$condition.on.surivival

a logical indicating whether the likelihood was conditioned on the survival of two lineages and the speciation event subtending them.

$root.type

indicates the user-specified root prior assumption.

$root.p

indicates whether the user-specified fixed root probabilities.

$phy

user-supplied tree

$data

user-supplied dataset

$trans.matrix

the user-supplied transition matrix

$max.tol

relative optimization tolerance.

$starting.vals

The starting values for the optimization.

$upper.bounds

the vector of upper limits to the optimization search.

$lower.bounds

the vector of lower limits to the optimization search.

$ode.eps

The ode.eps value used for the estimation.

Author(s)

Jeremy M. Beaulieu

References

Beaulieu, J.M, and B.C. O'Meara. 2016. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65:583-601.

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.

Herrera-Alsina, L., P. van Els, and R.S. Etienne. 2018. Detecting the dependence of diversification on multiples traits from phylogenetic trees and trait data. Systematic Biology, 68:317-328.

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

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.

Examples

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library(diversitree)
pars <- c(0.1, 0.2, 0.03, 0.03, 0.01, 0.01)
set.seed(4) 
phy <- tree.bisse(pars, max.t=30, x0=0)
sim.dat <- data.frame(names(phy$tip.state), phy$tip.state)

## Fit BiSSE equivalent:
trans.rates.bisse <-  TransMatMakerHiSSE(hidden.traits=0)
pp.bisse <- hisse(phy, sim.dat, hidden.states=FALSE, turnover=c(1,2), 
eps=c(1,2), trans.rate=trans.rates.bisse)

## Now fit HiSSE equivalent with a hidden state for state 1:
trans.rates.hisse <- TransMatMakerHiSSE(hidden.traits=1)
pp.hisse <- hisse(phy, sim.dat, hidden.states=TRUE, turnover=c(1,2,1,2), 
eps=c(1,2,1,2), trans.rate=trans.rates.hisse)

hisse documentation built on Nov. 16, 2021, 9:09 a.m.