Description Usage Arguments Value Author(s) References Examples
View source: R/tess.sim.taxa.R
tess.sim.taxa simulates a reconstructed phylogenetic tree under a global, time-dependent birth-death process conditioned on the number of taxa sampled. The rates may be any positive function of time or a constant. The process starts at time 0 and goes forward in time, hence the rates and events should be interpreted in the time after the origin. Additionally, mass-extinction event can be provided and a uniform taxon sampling probability. It is possible to start either with the origin (1 species) or with the most recent common ancestor (2 species).
1 2 3  | 
n | 
 Number of simulations.  | 
nTaxa | 
 Number of species sampled.  | 
max | 
 Maximum time/height of the tree.  | 
lambda | 
 The speciation rate function or constant.  | 
mu | 
 The extinction rate function or constant.  | 
massExtinctionTimes | 
 The set of mass-extinction times after the start of the process.  | 
massExtinctionSurvivalProbabilities | 
 The set of survival probabilities for each speciation event. The set must have the same length as the set of mass-extinction times.  | 
samplingProbability | 
 The probability for a species to be included in the sample.  | 
samplingStrategy | 
 The strategy how samples were obtained. Options are: uniform|diversified.  | 
SURVIVAL | 
 Do you want to condition on survival of the process?  | 
MRCA | 
 Does the process start with the most recent common ancestor?  | 
t_crit | 
 The critical time points when a jump in the rate function occurs. Only a help for the numerical integration routine.  | 
Returns a tree in 'phylo' format.
Sebastian Hoehna
S. Hoehna: Fast simulation of reconstructed phylogenies under global, time-dependent birth-death processes. 2013, Bioinformatics, 29:1367-1374
1 2 3 4 5 6 7  | l <- function(x) { if (x > 0.5 || x < 0.3) { return (1) } else { return (2) } }
e <- function(x) { if (x > 0.5 || x < 0.3) { return (0.95) } else { return (0.5) } }
tess.sim.taxa(n=1,nTaxa=10,max=10,l,e,MRCA=TRUE)
# simulation under constant rates
tess.sim.taxa(n=1,nTaxa=10,max=10,2.0,1.0,MRCA=TRUE)
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