sample.congruence.class.posterior: Stochastic exploration of congruent models for all samples in...

In ACDC: Analysis of Congruent Diversification Classes

Description

This function takes a posterior sample as input: a list of ACDC objects. It will then iterate over the samples, and for each posterior sample it will sample from the posterior class. It will sample using the sample.basic.models function, and all additional parameters are passed to sample.basic.models.

Usage

sample.congruence.class.posterior(
  posterior,
  num.samples,
  rate.type = "extinction",
  ...
)

Arguments

posterior	a list of ACDC model objects
num.samples	The pulled diversification rate function (measured in time before present).
rate.type	either "extinction", "speciation", or "both"
...	Arguments passed on to sample.basic.models times the time knots rate0 The rate at present, otherwise drawn randomly. model "MRF" for pure MRF model, otherwise MRF has a trend of type "exponential","linear", or "episodic<n>" direction "increase" or "decrease" (measured in past to present) noisy If FALSE, no MRF noise is added to the trajectory MRF.type "HSMRF" or "GMRF", type for stochastic noise. monotonic Whether the curve should be forced to always move in one direction. fc.mean Determines the average amount of change when drawing from the model. rate0.median When not specified, rate at present is drawn from a lognormal distribution with this median. rate0.logsd When not specified, rate at present is drawn from a lognormal distribution with this sd min.rate The minimum rate (rescaling fone after after drawing rates). max.rate The maximum rate (rescaling fone after after drawing rates).

Value

A named list with congruent rates.

Examples

data(primates_ebd_log)

posterior <- read.RevBayes(primates_ebd_log, max_t = 65, n_samples = 20)

samples <- sample.congruence.class.posterior(posterior, 
                                             num.samples = 20,
                                             rate.type = "extinction",
                                             rate0.median = 0.1,
                                             model = "MRF",
                                             max.rate = 1.0)

print(samples)

ACDC documentation built on Jan. 13, 2022, 1:08 a.m.