Rphylopars (version 0.2.9)

simtraits: Simulate traits for phylopars estimation

Description

Simulates traits for codephylopars estimation.

Usage

simtraits(ntaxa = 15, ntraits = 4, nreps = 1, nmissing = 0, tree, v, anc, 
intraspecific, model="BM", parameters, nsim, return.type="data.frame")

Arguments

ntaxa

Either number of taxa (ntaxa) or a tree can be supplied.

ntraits

Number of traits to be simulated.

nreps

Number of replicates per trait per species to simulate.

nmissing

Number of randomly missing trait values.

tree

Either number of taxa (ntaxa) or a tree can be supplied.

v

Trait covariance (v) can be optionally supplied; otherwise off-diagonal elements are set to 0.8.

anc

Value for ancestral state at root node.

intraspecific

Optional value for within-species variance.

model

Model of evolution (default="BM"). Other options include "OUfixedRoot", "OUrandomRoot", "lambda", "kappa", "delta", "EB".

parameters

List of parameters for the model. alpha for the selection strength in the OU model, lambda, kappa, delta, or rate for the EB model.

nsim

Number of simulations to perform (default is 1)

return.type

Default is "data.frame". Can also specify "matrix" if nreps=1.

Value

trait_data

Data for phylopars()

tree

The original phylogenetic tree (either provided to the function or generated internally)

sim_tree

The transformed tree on which trait simulations were performed (identical to tree if model="BM")

original_X

If within-species variation is simulated, original_X is the original species mean values before adding within-species variation.

References

Bruggeman J, Heringa J and Brandt BW. (2009) PhyloPars: estimation of missing parameter values using phylogeny. Nucleic Acids Research 37: W179-W184.

Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.

Examples

# simulate data
sim_data <- simtraits(ntaxa = 15,ntraits = 4,nreps = 3,nmissing = 10)

# estimate parameters under Brownian motion
# pheno_error = TRUE assumes intraspecific variation
# pheno_correlated = FALSE assumes intraspecific variation is not correlated
# phylo_correlated = TRUE assumed traits are correlated

PPE <- phylopars(trait_data = sim_data$trait_data,tree = sim_data$tree,
  pheno_error = TRUE,phylo_correlated = TRUE,pheno_correlated = FALSE)

PPE