Creates the list object for CS_version argument in DAISIE_ML_CS

Usage

create_CS_version(
  model = 1,
  function_to_optimize = "DAISIE",
  relaxed_par = NULL,
  par_sd = 0,
  par_upper_bound = Inf,
  integration_method = "standard",
  seed = 42,
  sample_size = 100,
  parallel = FALSE,
  n_cores = 1
)

Arguments

model: the CS model to run, options are 1 for single rate DAISIE model, 2 for multi-rate DAISIE, or 0 for IW test model
function_to_optimize: likelihood function that must be optimized in ML, either 'DAISIE', 'DAISIE_approx', or 'DAISIE_DE'
relaxed_par: the parameter to relax (integrate over). Options are "cladogenesis", "extinction", "carrying_capacity", "immigration", "anagenesis"
par_sd: standard deviation of the parameter to relax
par_upper_bound: upper bound of the parameter to relax
integration_method: method of integration, either 'standard','stratified' or 'MC'
seed: seed of the random number generator in case of 'MC'
sample_size: size of sample in case of 'MC' or 'stratified'
parallel: use parallel computing or not in case of 'MC' or 'stratified'
n_cores: number of cores to use when run in parallel

Value

A list of four elements

model: the CS model to run, options are 1 for single rate DAISIE model, 2 for multi-rate DAISIE, or 0 for IW test model
fumction_to_optimize likelihood function that must be optimized in ML, either 'DAISIE', 'DAISIE_approx', or 'DAISIE_DE'
relaxed_par: the parameter to relax (integrate over), for model = 2.
par_sd: the standard deviation of the parameter to relax
par_upperbound: upper bound of the parameter to relax.
integration_method: method of integration, either 'standard', 'stratified' or 'MC'
seed: random seed in case of integration_method = 'MC'
sample_size: size of sample in case of integration_method = 'MC' or 'stratified'
parallel: use parallel computing or not in case of integration_method = 'MC' or 'stratified'
n_cores: number of cores to use when run in parallel