get_phylogenetic_sample: Retrieve contemporary sample

In pcmabc: Approximate Bayesian Computations for Phylogenetic Comparative Methods

Retrieve contemporary sample

Description

The function retrieved the contemporary sample from an object simulated by pcmabc.

Usage

get_phylogenetic_sample(pcmabc_simulobj, bOnlyContemporary=FALSE, 
tol=.Machine$double.eps^0.5)

Arguments

pcmabc_simulobj	The output simulated object by simulate_phylproc() or simulate_phenotype_on_tree().
bOnlyContemporary	Logical, should all tip measurements be extracted (FALSE) or only those corresponding to non-extinct tips (TRUE). In the latter case be careful with the output (see Value).
tol	The tolerance to check if the tip is contemporary, i.e. the tip is distant from the root by tree.height +/- tol.

Value

The function returns a matrix with rows corresponding to tips. If there are extinct species, but bOnlyContemporary was set to TRUE, then the extinct lineages have to be removed from the tree before any further analysis. Also after removing extinct lineages one has to make sure that the order of the contemporary nodes did not change in the tree. Otherwise, the rows of the output matrix will not correspond to the indices of the tree's tips.

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.

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)
}
phenotype.model<-simulate_mvsl_sde

birth.params<-list(scale=1,maxval=10000,abcstepsd=1,positivevars=c(TRUE),
fixed=c(FALSE,TRUE,TRUE,TRUE,TRUE))

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)
mPhylSamp<-get_phylogenetic_sample(simres)

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