simulate_internal: Simulate a Stochastic Process on a tree

Description

simulate_internal simulate a stochastic process on a tree.

Usage

simulate_internal(
  phylo,
  process = c("BM", "OU", "scOU", "OUBM", "StudentOU"),
  p = 1,
  root.state = list(random = FALSE, stationary.root = FALSE, value.root = NA, exp.root =
    NA, var.root = NA),
  shifts = list(edges = NULL, values = NULL, relativeTimes = NULL),
  eps = 10^(-6),
  selection.strength = NULL,
  variance = NULL,
  optimal.value = NULL,
  checks = TRUE,
  simulate_random = TRUE,
  U_tree = NULL,
  times_shared = NULL,
  df = 1
)

Value

paramSimu An array with dimensions p x Nnode x 2 (BM) or p x Nnode x 3 (OU). For each trait t, 1 <= t <= p, paramSimu[t, , ] has tree columns, containing respectively the simulated state, expected value and optimal value for all the nodes.

Arguments

phylo

a phylogenetic tree, class phylo.

process

The model used for the simulation. One of "BM" (for a full BM model, univariate or multivariate); "OU" (for a full OU model, univariate or multivariate); or "scOU" (for a "scalar OU" model).

p

Dimension of the simulated trait

root.state

List describing the state of the root, with:

random: random state (TRUE) or deterministic state (FALSE)

value.root

if deterministic, value of the character at the root

exp.root

if random, expectation of the character at the root

var.root

if random, variance of the character at the root (pxp matrix)

shifts

List with position and values of the shifts :

edges: vector of the K id of edges where the shifts are

values

matrix p x K of values of the shifts on the edges (one column = one shift)

relativeTimes

vector of dimension K of relative time of the shift from the parent node of edges

eps

Tolerance for the value of the norm 1 of the selection strength matrix for OU

selection.strength

Matrix of selection strength size p x p (OU)

variance

Variance-covariance matrix size p x p

optimal.value

Vector of p optimal values at the root (OU)

checks

whether to check the entry parameters for consistency. Default to TRUE.

simulate_random

set to FALSE if only the expected values are needed (and not the random sample). Default to TRUE.

U_tree

optional, full incidence matrix of the tree, result of function incidence.matrix.full.

times_shared

optional, times of shared ancestry of all nodes and tips, result of function compute_times_ca. Can be specified to avoid extra computations.

if the process is "StudentOU", the number of degree of freedom of the chosen student law. default to 1.