xml_monitors: Generate the monitor block in a 'BEAST' XML file.
In jsigao/prioritree: A utility for improving phylodynamic analyses in BEAST

xml_monitors

R Documentation

Generate the monitor block in a 'BEAST' XML file.

Description

Generate the monitor block in a 'BEAST' XML file.

Usage

xml_monitors(
  discrete_trait_name,
  currentTree_output,
  file_name,
  bssvs = T,
  complete_history = T,
  lheat = 1,
  ctmc = T,
  clockrate_mean_stochastic = T,
  symmetry = T,
  delta_prior = c("Poisson", "Uniform", "Beta-Binomial"),
  under_prior = F,
  mcmc_samplingfreq = 1,
  ml_numstones = 0,
  ml_chainlengthperstone = 0,
  ml_samplingfreq = 0,
  ml_alphaofbeta = 0.3
)

Arguments

`discrete_trait_name`	name of the column containing tip states
`currentTree_output`	tree indices output chunk in the XML (as one of the returned value of `xml_treemodel`)
`bssvs`	Whether Bayesian Stochastic Search Variable Selection (BSSVS) is used (default true) in the discrete-geographic model.
`complete_history`	Whether to perform stochastic mapping to simulate full histories of the discrete-geographic trait (default) or perform the "fast" stochastic mapping to compute the expected number of events on each branch
`lheat`	Number of data clones
`ctmc`	Whether to specify the CTMC-rate reference prior (default prior recommended by BEAUti) on the average dispersal rate
`clockrate_mean_stochastic`	Whether the mean of the Exponential prior should be treated as a random variable with a hyperprior (default true)
`symmetry`	Whether the specified geographic model is symmetric (true) or asymmetric (false)
`delta_prior`	Which of the three prior options to put on Δ, including: "Poisson": a(n offset) Poisson distribution (the default option); "Uniform": a uniform distribution between zero and the maximum Δ (when all the dispersal routes exist), and; "Beta-Binomial": a Beta-Binomial distribution.
`under_prior`	Whether to infer the joint prior distribution (default false) or the joint posterior distribution
`mcmc_samplingfreq`	Every number of MCMC generations to log to the output
`ml_numstones`	Number of powers ("stones") to use in the power-posterior analysis.
`ml_chainlengthperstone`	Number of generations to run per stone.
`ml_samplingfreq`	Every number of generations to log to the output.
`ml_alphaofbeta`	Alpha of the Beta distribution that decides where to put the stones.
`filename`	Name of the output file (without file extension)