create_ns_mcmc {beautier} | R Documentation |
Create an MCMC object to estimate the marginal likelihood using Nested Sampling.
Description
This will result in a BEAST run that estimates the marginal
likelihood until convergence is achieved.
In this context, chain_length
is only an upper bound
to the length of that run.
Usage
create_ns_mcmc(
chain_length = 1e+07,
store_every = -1,
pre_burnin = 0,
n_init_attempts = 3,
particle_count = 1,
sub_chain_length = 5000,
epsilon = "1e-12",
tracelog = create_tracelog(),
screenlog = create_screenlog(),
treelog = create_treelog()
)
Arguments
chain_length |
upper bound to the length of the MCMC chain |
store_every |
number of states the MCMC will process
before the posterior's state will be saved to file.
Use -1 or |
pre_burnin |
number of burn in samples taken before entering the main loop |
n_init_attempts |
number of initialization attempts before failing |
particle_count |
number of particles |
sub_chain_length |
sub-chain length |
epsilon |
epsilon |
tracelog |
a |
screenlog |
a |
treelog |
a |
Value
an MCMC object
Author(s)
Richèl J.C. Bilderbeek
References
* [1] Patricio Maturana Russel, Brendon J Brewer, Steffen Klaere, Remco R Bouckaert; Model Selection and Parameter Inference in Phylogenetics Using Nested Sampling, Systematic Biology, 2018, syy050, https://doi.org/10.1093/sysbio/syy050
See Also
Use create_mcmc
to create a regular MCMC.
Use create_test_ns_mcmc
to create an NS MCMC for testing,
with, among others, a short MCMC chain length.
Use check_ns_mcmc
to check that an NS MCMC object is valid.
Examples
if (is_on_ci()) {
mcmc <- create_ns_mcmc(
chain_length = 1e7,
store_every = 1000,
particle_count = 1,
sub_chain_length = 1000,
epsilon = 1e-12
)
beast2_input_file <- get_beautier_tempfilename()
create_beast2_input_file(
get_fasta_filename(),
beast2_input_file,
mcmc = mcmc
)
file.remove(beast2_input_file)
remove_beautier_folder()
}