phytools (version 0.5-38)

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","lambda"),
	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), "OU" (Ornstein-Uhlenbeck), or "lambda" (the lambda model).

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; propalpha variance on the proposal distribution for alpha (for model="OU"); pr.anc prior probability distribution on the ancestral states for each node, in a matrix - not all nodes need to be supplied; pr.th prior density on the thresholds; burnin number of generations to exclude for burn-in when plotting posterior probabilities on the tree; plot logical value indicating whether or not to plot the posterior probabilities; print logical value indicating whether or not to print the state of the MCMC; piecol colors for the posterior probabilities plotted as pie charts at internal nodes; and tipcol which indicates whether the tip colors should be based on the input data ("input") or sampled tip liabilities ("estimated"). These will only differ if there is uncertainty in the tip states.

...

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

Felsenstein, J. (2012) A comparative method for both discrete and continuous characters using the threshold model. American Naturalist, 179, 145-156.

Revell, L. J. (2014) Ancestral character estimation under the threshold model from quantitative genetics. Evolution, bold68, 743-759.

See Also

anc.Bayes, threshBayes