hisse (version 1.7)

hisse.null4: Four state trait-independent Hidden State Speciation and Extinction

Description

Sets up and executes a four state trait-independent HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character set.

Usage

hisse.null4(phy, data, f=c(1,1), turnover.anc=rep(c(1,2,3,4),2), 
eps.anc=rep(c(1,2,3,4),2), trans.type="equal", condition.on.survival=TRUE, 
root.type="madfitz", root.p=NULL,  output.type="turnover", sann=FALSE, 
sann.its=10000, bounded.search=FALSE, max.tol=.Machine$double.eps^.25,
starting.vals=NULL, turnover.upper=50, eps.upper=50, trans.upper=100)

Arguments

phy

a phylogenetic tree, in ape “phylo” format and with internal nodes labeled denoting the ancestral selective regimes.

data

a data matrix containing species information (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.anc

a vector of length 8, indicating the free parameters associated with the net turnover rates. Default setting assumes character independent diversification (see Details).

eps.anc

a vector of length 8, indicating the free parameters associated with the extinction fractions. Default setting assumes character independent diversification (see Details).

trans.type

provides the type of transition rate model. Currently this model allows two types: “equal”, the default, which assumes all transitions are equal, and “three.rate”, that assumes three rates (see Details).

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 prior assumption should based the procedure described by FitzJohn et al. 2009, “madfitz”, assumed equal, “equal”, or set to user, “user”.

root.p

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

output.type

indicates whether the rates should be printed onscreen as the optimized variables, “turnover”, transformed to reflect net diversification, “net.div”, or transformed to reflect $lambda$ and $mu$, “raw”.

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 FALSE.

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 FALSE.

max.tol

supplies the relative optimization tolerance to subplex.

starting.vals

a vector of starting values to be used instead of the default settings. These are just three values given in the following order: turnover (1), extinction fraction (2), and a single transition rate (3)

turnover.upper

sets the upper bound for the turnover parameters.

eps.upper

sets the upper bound for the extinction fraction parameters.

trans.upper

sets the upper bound for the transition rate parameters.

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.

$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

$output.type

the user-specified output.type to be printed on the screen.

$trans.type

the user-specified transition model.

$trans.mat

the index matrix that specifies the free parameters in the transition model.

$max.tol

relative optimization tolerance.

$upper.bounds

the vector of upper limits to the optimization search.

$lower.bounds

the vector of lower limits to the optimization search.

Details

This function sets up and executes a four-state trait independent HiSSE model. The model closely follows hisse.

Like hisse, users input vectors containing values to indicate how many free parameters are to be estimated for each of the variables in the model. However, the null-four model assumes that “turnover.anc” and “eps.anc” are linked between the two observed states. Thus, users are unlikely to alter the inputs much, aside from perhaps fixing “turnover.anc” or “eps.anc” to be equal across the four hidden states, where the “turnover.anc” input vector is set as rep(c(1,1,1,1),2). For a Yule equivalent, the input vector for “eps.anc” would be rep(c(0,0,0,0),2). For how to setup a null-two model see the example code below.

For user-specified “root.p”, you should specify the probability for each state. See help for “hisse” for more on other parameters for this function.

References

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

Examples

## Not run
# 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 null-four HiSSE:
# pp.hisse.null <- hisse.null4(phy, sim.dat, turnover.anc=rep(c(1,2,3,4),2), 
# eps.anc=rep(c(1,2,3,4),2), trans.type="equal")

## Fit null-two HiSSE model:
# trans.rates.hisse <- TransMatMaker(hidden.states=TRUE)
# trans.rates.hisse <- ParDrop(trans.rates.hisse, c(3,5,8,10))
# trans.rates.hisse[!is.na(trans.rates.hisse) & !trans.rates.hisse == 0] = 1
# pp.hisse <- hisse(phy, sim.dat, hidden.states=TRUE, turnover.anc=c(1,1,2,2), 
# eps.anc=c(1,1,2,2), trans.rate=trans.rates.hisse)