OUwie (version 3.0.3)

hOUwie.sim: Simulate a discrete and continuous character following a Markov and Ornstein-Uhlenbeck model.

Description

Simulates a discrete and continuous character following the hOUwie model. The function first evolves a discrete character based on the Q matrix provided. Next, it will evolve a continuous character following the given OU parameters and simulated discrete character. Like the hOUwie model, transitions between discrete states are assumed to take place half-way between nodes.

Usage

hOUwie.sim(phy, 
           Q, 
           root.freqs, 
           alpha, 
           sigma.sq, 
           theta0, 
           theta)

Value

data

a dataframe of sp (species), reg (discrete character states), and x (continuous character values).

simmap

the history of the discrete character presented as a stochastic map.

Arguments

phy

A phylogenetic tree, in ape “phylo” format.

Q

A transition rate matrix with dimensions nStates by nStates describing rates of change between discrete states.

root.freqs

A vector nStates long with probabilities of the root being in a particular state. For example, for a binary discrete character a root prior of c(1, 0) would fix the root state in state 1.

alpha

A vector nStates long which gives alpha parameter of the OU model. For a BM model set this to be near 0.

sigma.sq

A vector nStates long which gives the evolutionary rate parameter of the OU model.

theta0

A numeric value giving the starting value of the continuous character at the root.

theta

A vector nStates long which gives the phenotypic optima for each regime state.

Author

James D. Boyko

Details

The simulation protocol follows the hOUwie model where stochastic maps being produced are based on a node-by-node simulation. I.e., we first simulate the node states given the parameters and then branches are paintined based on transitions occuring half-way between the nodes.

Examples

# \donttest{
data(tworegime)
# simulate an OUM model
Q <- matrix(c(-1,1,1,-1), 2, 2)
root.freqs <- c(1, 0)
alpha <- c(2, 2)
sigma.sq <- c(1,1)
theta0 <- 5
theta <- c(5, 10)

simulated_data <- hOUwie.sim(tree, Q, root.freqs, alpha, sigma.sq, theta0, theta)
# }