evol.rate.mcmc(tree, x, ngen=10000, control=list())
"phylo"
format.names(x)
should be the species names.sig1
: starting value for $\sigma(1)^2$; sig2
: starting value for $\sigma(2)^2$; a
: starting value for a; sd1
: standard deviation for the normal proposal distribution for $\sigma(1)^2$; sd2
: standard deviation for the normal proposal distribution for $\sigma(2)^2$; kloc
: scaling parameter for tree move proposals - $1/\lambda$ for the reflected exponential distribution; sdlnr
: standard deviation on the log-normal prior on $\sigma(1)^2/\sigma(2)^2$; rand.shift
: probability of proposing a random shift in the tree (improves mixing); print
: print frequency for the MCMC; sample
: sample frequency.control
are given in Revell et al. (2012).
Revell, L. J., D. L. Mahler, P. Peres-Neto, and B. D. Redelings. (2012) A new method for identifying exceptional phenotypic diversification. Evolution, 66, 135-146.
anc.Bayes
, brownie.lite
, evol.vcv
, minSplit
, posterior.evolrate