simulate_phenotype_on_tree: Simulate a stochastic process on top of a phylogeny.

In pcmabc: Approximate Bayesian Computations for Phylogenetic Comparative Methods

Simulate a stochastic process on top of a phylogeny.

Description

The function simulates phenotypic dataset under user defined models trait evolution. The phenotype evolves on top of a fixed phylogeny.

Usage

simulate_phenotype_on_tree(phyltree, fsimulphenotype, simul.params, X0, step)

Arguments

phyltree	The phylogeny in phylo format. The tree can be obtained from e.g. a nexus file by the read.nexus() function from the ape package. The "standard" ape node indexing is assumed: for a tree with n tips, the tips should have indices 1:n and the root index n+1.
fsimulphenotype	The name of a function to simulate the phenotype over a period of time. The function has to have four parameters (in the following order not by name): time, params, X0 and step. The parameter time is the duration of the simulation, i.e. the length of the branch. The parameter params is a list of the parameters governing the simulation, what will be passed here is the list phenotype.model.params, without the fields fixed, abstepsd and positivevars. It is up to the function in phenotype.model to interpret the provided parameters. X0 stands for the value at the start of the branch and step is a control parameter governing the time step size of the simulation. The pcmabc package has inbuilt support for simulating the trait as as stochastic differential equation by the yuima package with the function simulate_sde_on_branch(). However the user needs to write the phenotype.model function that translates the vector of parameters into an object that is understandable by yuima and then call simulate_sde_on_branch(), see the Example. The phenotype is simulated prior to the simulation of the speciation/extinction events on a lineage. Hence, it is not possible (at the moment) to include some special event (e.g. a cladogenetic jump) at branching. Such dynamics are only possible at the start of the lineage, i.e. when the new lineage is separated from the main lineage. Hence, cladogenetic change can only be included as an event connected with a lineage (subpopulation) breaking off.
simul.params	The parameters of the stochastic model to simulate the phenotype. They should be passed as a named list.
X0	The value of the ancestral state at the start of the tree.
step	The time step size for simulating the phenotype. If not provided, then calculated as min(0.001,tree.height/1000).

Value

tree	The simulated tree in phylo format.
phenotype	A list of the trajectory of the simulated phenotype. Each entry corresponds to a lineage that started at some point of the tree. Each entry is a matrix with first row equalling time (relative to start of the lineage, hence if absolute time since tree's origin is needed it needs to be recalculated, see draw_phylproc()'s code) and the next rows correspond to the trait value(s).
root.branch.phenotype	The simulation on the root branch. A matrix with first row being time and next rows the simulated trait(s).

Author(s)

Krzysztof Bartoszek

References

Bartoszek, K. and Lio', P (2019). Modelling trait dependent speciation with Approximate Bayesian Computation. Acta Physica Polonica B Proceedings Supplement 12(1):25-47.

See Also

simulate_phylproc

Examples

## simulate 3d OUBM model
## This example requires the R package ape
## (to generate the tree in phylo format).
set.seed(12345)

phyltree<-ape::rphylo(n=5,birth=1,death=0)
simulate_mvsl_sde<-function(time,params,X0,step){
    A <- c(paste("(-",params$a11,")*(x1-(",params$psi1,"))
    -(",params$a12,")*(x2-(",params$psi2,"))-(",params$b11,")*x3",sep=""),
    paste("(-",params$a21,")*(x1-(",params$psi1,"))
    -(",params$a22,")*(x2-(",params$psi2,"))-(",params$b21,")*x3",sep=""),0)
    S <- matrix( c( params$s11, params$s12, 0, 0, params$s22 
    , 0, 0, 0, params$s33), 3, 3,byrow=TRUE)
    yuima.3d <- yuima::setModel(drift = A, diffusion = S,
    state.variable=c("x1","x2","x3"),solve.variable=c("x1","x2","x3") )
    simulate_sde_on_branch(time,yuima.3d,X0,step)
}

birth.params<-list(scale=1,maxval=10000)

sde.params<-list(a11=2.569531,a12=0,a21=0,a22=28.2608,b11=-5.482939,
b21=-34.806936,s11=0.5513215,s12=1.059831,s22=1.247302,s33=1.181376,
psi1=-2.4590422,psi2=-0.6197838)
X0<-c(5.723548,4.103157,3.834698)
step<-0.5 ## for keeping example's running time short <5s as CRAN policy, 
          ## in reality should be much smaller e.g. step<-0.001                      
simres<-simulate_phenotype_on_tree(phyltree, simulate_mvsl_sde, sde.params, X0, step)

pcmabc documentation built on May 9, 2022, 9:09 a.m.