phytools (version 0.3-72.1)

ancThresh: Ancestral character estimation under the threshold model using Bayesian MCMC

Description

This function uses Bayesian MCMC to estimate ancestral states and thresholds for a discrete character under the threshold model from quantitative genetics (Felsenstein 2012).

Usage

ancThresh(tree, x, ngen=1000, sequence=NULL, method="mcmc", model=c("BM","OU"),
	control=list(), ...)

Arguments

tree

phylogenetic tree.

x

a named vector containing discrete character states; or a matrix containing the tip species, in rows, and probabilities of being in each state, in columns.

ngen

number of generations to run the MCMC.

sequence

assumed ordering of the discrete character state. If not supplied and x is a vector then numerical/alphabetical order is assumed; if not supplied and x is a matrix, then the column order of x is used.

method

only method currently available is "mcmc".

model

model for the evolution of the liability. Options are "BM" (Brownian motion, the default), or "OU" (Ornstein-Uhlenbeck).

control

list containing the following elements: sample, the sampling interval; propliab variance of the proposal distribution for liabilities; propthresh variance on the proposal distribution for the thresholds; propal

...

additional arguments to be passed to plotThresh (called internally).

Value

• This function returns as list with four elements:
• ace
  posterior probabilities for each character state at each internal node, with the burn-in excluded.
• mcmc
  full posterior sample for the states.
• par
  full posterior sample for the thresholds, the alpha parameter of the "OU" model (if applicable), and the likelihood.
• liab
  full posterior sample of the liabilities at internal and tip nodes.

References

Revell, L. J. (Submitted). Ancestral character estimation under the threshold model from quantitative genetics. Felsenstein, J. (2012) A comparative method for both discrete and continuous characters using the threshold model. American Naturalist, 179, 145-156.

See Also

anc.Bayes, threshBayes