simtraits: Simulate traits for phylopars estimation

In Rphylopars: Phylogenetic Comparative Tools for Missing Data and Within-Species Variation

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.

Author(s)

Eric W. Goolsby [e-mail]

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

Rphylopars documentation built on May 9, 2022, 9:08 a.m.