geiger (version 2.0.7)

sim.char: simulate character evolution

Description

simulating evolution of discrete or continuous characters on a phylogenetic tree

Usage

sim.char(phy, par, nsim = 1, model = c("BM", "speciational", "discrete"), root = 1)

Arguments

phy

a phylogenetic tree of class 'phylo'

par

matrix describing model (either vcv matrix or Q matrix)

nsim

number of simulations to run

model

a model from which to simulate data

root

starting state (value) at root

Value

An array of simulated data, either two or three-dimensional, is returned. The first dimension is the number of taxa, the second the number of characters, and the third the number of simulated data sets.

Details

This function simulates either discrete or continuous data on a phylogenetic tree. The model variable determines the type of simulation to be run. There are three options: discrete, which evolves characters under a continuous time Markov model, and two continuous models, BM and speciational. The BM model is a constant rate Brownian-motion model, while speciational is a Brownian model on a tree where all branches have the same length. The model.matrix parameter gives the structure of the model, and should be either a transition matrix, Q, for the discrete model, or a trait variance-covariance matrix for BM or speciational models. For discrete models, multiple characters may be simulated if model.matrix is given as a list of Q matrices (see Examples). For continuous models, multivariate characters can be simulated, with their evolution goverened by a covariance matrix specified in the model.matrix.

Examples

# NOT RUN {
geo <- get(data(geospiza))

## Continuous character -- univariate
usims <- sim.char(geo$phy, 0.02, 100)

## Use a simulated dataset in fitContinuous()
fitC <- fitContinuous(geo$phy, usims[,,1], model="BM", control=list(niter=10), ncores=2)

## Continuous character -- multivariate
s <- ratematrix(geo$phy, geo$dat)
csims <- sim.char(geo$phy, s, 100)

## Discrete character -- univariate
q <- list(rbind(c(-.5, .5), c(.5, -.5)))
dsims <- sim.char(geo$phy, q, model="discrete", n=10)

## Use a simulated dataset in fitDiscrete()
fitD <- fitDiscrete(geo$phy, dsims[,,1], model="ER", niter=10, ncores=2)

## Discrete character -- multivariate
qq <- list(rbind(c(-.5, .5), c(.5, -.5)), rbind(c(-.05, .05), c(.05, -.05)))
msims <- sim.char(geo$phy, qq, model="discrete", n=10)

  
# }