sim_fun_on_tree function

Simulate functions on a ginven tree

sim_fun_on_tree(
  tree,
  tip.type,
  node.type,
  psi,
  mu_d,
  mu_s,
  eta,
  Pi,
  P = 1L,
  informative = getOption("aphylo_informative", FALSE),
  maxtries = 20L
)

Arguments

• tree: An object of class phylo
• tip.type, node.type: Integer vectors with values 0 or 1. 0 denotes duplication node and 1 speciation node. This is used in LogLike .
• psi: Numeric vector of length 2. Misclasification probabilities. (see LogLike).
• mu_d, mu_s: Numeric vector of length 2. Gain/loss probabilities (see LogLike).
• eta: Numeric vector of length 2. Annotation bias probabilities (see LogLike).
• Pi: Numeric scalar. Root node probability of having the function (see LogLike).
• P: Integer scalar. Number of functions to simulate.
• informative: Logical scalar. When TRUE (default) the function re-runs the simulation algorithm until both 0s and 1s show in the leaf nodes of the tree.
• maxtries: Integer scalar. If informative = TRUE, then the function will try at most maxtries times.

Returns

An matrix of size length(offspring)*P with values 9, 0 and 1 indicating "no information", "no function" and "function".

Details

Using the model described in the vignette peeling_phylo.html

The optiona informative was created such that when needed the function can be forced to simualte annotations while making sure (or at least trying maxtries times) that the leafs have both 0s and 9s. From what we've learned while conducting simulation studies, using this option may indirectly bias the data generating process.

Examples

# Example 1 ----------------------------------------------------------------
# We need to simulate a tree
set.seed(1231)
newtree <- sim_tree(1e3)

# Preprocessing the data

# Simulating
ans <- sim_fun_on_tree(
  newtree,
  psi  = c(.01, .05),
  mu_d = c(.90, .80),
  mu_s = c(.1, .05),
  Pi   = .5,
  eta  = c(1, 1)
)

# Tabulating results
table(ans)