est_marg_liks: Estimate the marginal likelihoods for one or more inference...

In mcbette: Model Comparison Using 'babette'

Estimate the marginal likelihoods for one or more inference models

Description

Estimate the marginal likelihoods (aka evidence) for one or more inference models, based on a single alignment. Also, the marginal likelihoods are compared, resulting in a relative weight for each model, where a relative weight of a model close to 1.0 means that that model is way likelier than the others.

Usage

est_marg_liks(
  fasta_filename,
  inference_models = list(beautier::create_inference_model(mcmc =
    beautier::create_ns_mcmc())),
  beast2_optionses = rep(list(beastier::create_mcbette_beast2_options()), times =
    length(inference_models)),
  verbose = FALSE,
  os = rappdirs::app_dir()$os
)

Arguments

fasta_filename	name of the FASTA file
inference_models	a list of one or more inference models, as can be created by create_inference_model
beast2_optionses	list of one or more beast2_options structures, as can be created by create_mcbette_beast2_options. Use of reduplicated plural to achieve difference with beast2_options
verbose	if TRUE show debug output
os	name of the operating system, must be unix (Linux, Mac) or win (Windows)

Details

In the process, multiple (temporary) files are created (where [x] denotes the index in a list)

• beast2_optionses[x]$input_filename path to the the BEAST2 XML input file

• beast2_optionses[x]$output_state_filename path to the BEAST2 XML state file

• inference_models[x]$mcmc$tracelog$filename path to the BEAST2 trace file with parameter estimates

• inference_models[x]$mcmc$treelog$filename path to the BEAST2 trees file with the posterior trees

• inference_models[x]$mcmc$screenlog$filename path to the BEAST2 screen output file

These file can be deleted manually by bbt_delete_temp_files, else these will be deleted automatically by the operating system.

Value

a data.frame showing the estimated marginal likelihoods (and its estimated error) per combination of models. Columns are:

• site_model_name: name of the site model

• clock_model_name: name of the clock model

• tree_prior_name: name of the tree prior

• marg_log_lik: estimated marginal (natural) log likelihood

• marg_log_lik_sd: estimated error of marg_log_lik

• weight: relative model weight, a value from 1.0 (all evidence is in favor of this model combination) to 0.0 (no evidence in favor of this model combination)

• ess: effective sample size of the marginal likelihood estimation

Author(s)

Richèl J.C. Bilderbeek

See Also

• can_run_mcbette: see if 'mcbette' can run

• est_marg_liks: estimate multiple marginal likelihood of a single inference mode

Examples

if (can_run_mcbette()) {

  # Use an example FASTA file
  fasta_filename <- system.file("extdata", "simple.fas", package = "mcbette")

  # Create two inference models
  inference_model_1 <- beautier::create_ns_inference_model(
    site_model = beautier::create_jc69_site_model()
  )
  inference_model_2 <- beautier::create_ns_inference_model(
    site_model = beautier::create_hky_site_model()
  )

  # Shorten the run, by doing a short (dirty, unreliable) MCMC
  inference_model_1$mcmc <- beautier::create_test_ns_mcmc()
  inference_model_2$mcmc <- beautier::create_test_ns_mcmc()

  # Combine the inference models
  inference_models <- list(inference_model_1, inference_model_2)

  # Create the BEAST2 options, that will write the output
  # to different (temporary) filanems
  beast2_options_1 <- beastier::create_mcbette_beast2_options()
  beast2_options_2 <- beastier::create_mcbette_beast2_options()

  # Combine the two BEAST2 options sets,
  # use reduplicated plural
  beast2_optionses <- list(beast2_options_1, beast2_options_2)

  # Compare the models
  marg_liks <- est_marg_liks(
    fasta_filename,
    inference_models = inference_models,
    beast2_optionses = beast2_optionses
  )

  # Interpret the results
  interpret_marg_lik_estimates(marg_liks)

  beastier::remove_beaustier_folders()
  beastier::check_empty_beaustier_folders()
}

mcbette documentation built on Sept. 27, 2023, 9:08 a.m.