ancThresh(tree, x, ngen=1000, sequence=NULL, method="mcmc", model=c("BM","OU","lambda"),
control=list(), ...)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."mcmc"."BM" (Brownian motion, the default), "OU" (Ornstein-Uhlenbeck), or "lambda" (the lambda model).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.plotThresh (called internally).Revell, L. J. (2014) Ancestral character estimation under the threshold model from quantitative genetics. Evolution, bold68, 743-759.
anc.Bayes, threshBayes