sim_mte: Simulate MTE process
In bite: Bayesian Integrative Models of Trait Evolution

Description Usage Arguments Details Value Author(s) Examples

Generate random values of trait mean simulated under a MTE process along a phylogenetic tree

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sim_mte(phy, map = NULL, model = "OU", pars = c(root = 2, theta = 1,
  sigma_sq = 0.1, alpha = 1), sampling = c(1, 7), bounds = c(-Inf,
  Inf))

`phy`	Phylogenetic tree
`map`	list containing the mapping of regimes over each edge (see details).
`model`	model specification for the simulation of trait mean evolution. Supported models are c("OU", "BM", "WN")
`pars`	parameters used for the simulation of trait mean evolution (see details).
`sampling`	vector of size 2 giving the min and max number of individual per species
`bounds`	vector of size 2 giving the bounds of the mean

map : the list must be ordered in the same order than phy$edge. Each element represents an edge and contains a vector indicating the time spent under each regime in the branch. The name of the regimes must appear on the map pars : list containing parameters depending on the chosen model. Elements of that lists must be vectors of size 1 or n, with n = number of regimes in the map. Each element of pars must be named with the corresponding parameter abbreviation. Parameters used in the different models:

White Noise model (WN):

root: root value
sigma_sq: evolutionary rate, n regimes if "sigma" is specified in models

Brownian Motion model (BM):

root: root value
sigma_sq: evolutionary rate, n regimes if "sigma" is specified in models

Ornstein Uhlenbeck model (OU):

root: root value. Only used if "root" is specified in models
sigma_sq: evolutionary rate, n regimes if "sigma" is specified in models
theta: optimal value, n regimes if "theta" is specified in models
alpha: strength of selection, n regimes if "alpha" is specified in models

returns a numeric vector giving the simulated mean value of the trait for each species of the tree.

Theo Gaboriau

library(phytools)
phy <- pbtree(n = 50)
Q <- cbind(c(-.002, .002), c(.002, -.002))
phy <- sim.history(phy, Q = Q)
# MBM and VOU
mte_phy <- sim_mte(phy, phy$maps)