default_params_doc: Default parameter documentation

In secsse: Several Examined and Concealed States-Dependent Speciation and Extinction

Default parameter documentation

Description

This function's purpose is to list all parameter documentation to be inherited by the relevant functions.

Usage

default_params_doc(
  phy,
  traits,
  num_concealed_states,
  idparslist,
  initparsopt,
  idparsfix,
  idparsopt,
  idfactorsopt,
  parsfix,
  cond,
  root_state_weight,
  sampling_fraction,
  tol,
  maxiter,
  optimethod,
  num_cycles,
  loglik_penalty,
  is_complete_tree,
  verbose,
  num_threads,
  atol,
  rtol,
  method,
  parameter,
  setting_calculation,
  num_steps,
  see_ancestral_states,
  lambdas,
  mus,
  qs,
  crown_age,
  pool_init_states,
  maxSpec,
  conditioning,
  non_extinction,
  max_tries,
  drop_extinct,
  seed,
  prob_func,
  parameters,
  masterBlock,
  diff.conceal,
  trait_info,
  lambd_and_modeSpe,
  initloglik,
  initfactors,
  idparsfuncdefpar,
  functions_defining_params,
  state_names,
  transition_matrix,
  model,
  concealed_spec_rates,
  shift_matrix,
  q_matrix,
  lambda_list,
  object,
  params,
  param_posit,
  ml_pars,
  mu_vector,
  max_spec,
  min_spec,
  max_species_extant,
  tree_size_hist,
  start_at_crown,
  optimmethod
)

Arguments

phy	phylogenetic tree of class phylo, rooted and with branch lengths.
traits	vector with trait states for each tip in the phylogeny. The order of the states must be the same as the tree tips. For help, see vignette("starting_secsse", package = "secsse").
num_concealed_states	number of concealed states, generally equivalent to the number of examined states in the dataset.
idparslist	overview of parameters and their values.
initparsopt	a numeric vector with the initial guess of the parameters to be estimated.
idparsfix	a numeric vector with the ID of the fixed parameters.
idparsopt	a numeric vector with the ID of parameters to be estimated.
idfactorsopt	id of the factors that will be optimized. There are not fixed factors, so use a constant within functions_defining_params.
parsfix	a numeric vector with the value of the fixed parameters.
cond	condition on the existence of a node root: "maddison_cond", "proper_cond" (default). For details, see vignette.
root_state_weight	the method to weigh the states: "maddison_weights", "proper_weights" (default) or "equal_weights". It can also be specified for the root state: the vector c(1, 0, 0) indicates state 1 was the root state.
sampling_fraction	vector that states the sampling proportion per trait state. It must have as many elements as there are trait states.
tol	A numeric vector with the maximum tolerance of the optimization algorithm. Default is c(1e-04, 1e-05, 1e-05).
maxiter	max number of iterations. Default is 1000 * round((1.25) ^ length(idparsopt)).
num_cycles	Number of cycles of the optimization. When set to Inf, the optimization will be repeated until the result is, within the tolerance, equal to the starting values, with a maximum of 10 cycles.
loglik_penalty	the size of the penalty for all parameters; default is 0 (no penalty).
is_complete_tree	logical specifying whether or not a tree with all its extinct species is provided. If set to TRUE, it also assumes that all all extinct lineages are present on the tree. Defaults to FALSE.
verbose	sets verbose output; default is TRUE when optimmethod is "simplex". If optimmethod is set to "simplex", then even if set to FALSE, optimizer output will be shown.
num_threads	number of threads to be used. Default is one thread.
atol	A numeric specifying the absolute tolerance of integration.
rtol	A numeric specifying the relative tolerance of integration.
method	integration method used, available are: "odeint::runge_kutta_cash_karp54", "odeint::runge_kutta_fehlberg78", "odeint::runge_kutta_dopri5", "odeint::bulirsch_stoer" and "odeint::runge_kutta4". Default method is: "odeint::bulirsch_stoer".
parameter	list where first vector represents lambdas, the second mus and the third transition rates.
setting_calculation	argument used internally to speed up calculation. It should be left blank (default : setting_calculation = NULL).
num_steps	number of substeps to show intermediate likelihoods along a branch.
see_ancestral_states	Boolean for whether the ancestral states should be shown? Defaults to FALSE.
lambdas	speciation rates, in the form of a list of matrices.
mus	extinction rates, in the form of a vector.
qs	The Q matrix, for example the result of function q_doubletrans, but generally in the form of a matrix.
crown_age	crown age of the tree, tree will be simulated conditional on non-extinction and this crown age.
pool_init_states	pool of initial states at the crown, in case this is different from all available states, otherwise leave at NULL
conditioning	can be "obs_states", "true_states" or "none", the tree is simulated until one is generated that contains all observed states ("obs_states"), all true states (e.g. all combinations of obs and hidden states), or is always returned ("none"). Alternatively, a vector with the names of required observed states can be provided, e.g. c("S", "N").
non_extinction	boolean stating if the tree should be conditioned on non-extinction of the crown lineages. Defaults to TRUE.
max_tries	maximum number of simulations to try to obtain a tree.
drop_extinct	boolean stating if extinct species should be dropped from the tree. Defaults to TRUE.
seed	pseudo-random number generator seed.
prob_func	a function to calculate the probability of interest, see description.
parameters	list where first vector represents lambdas, the second mus and the third transition rates.
masterBlock	matrix of transitions among only examined states, NA in the main diagonal, used to build the full transition rates matrix.
diff.conceal	Boolean stating if the concealed states should be different. E.g. that the transition rates for the concealed states are different from the transition rates for the examined states. Normally it should be FALSE in order to avoid having a huge number of parameters.
trait_info	data frame where first column has species ids and the second one is the trait associated information.
lambd_and_modeSpe	a matrix with the 4 models of speciation possible.
initloglik	A numeric with the value of loglikehood obtained prior to optimisation. Only used internally.
initfactors	the initial guess for a factor (it should be set to NULL when no factors).
idparsfuncdefpar	id of the parameters which will be a function of optimized and/or fixed parameters. The order of id should match functions_defining_params.
functions_defining_params	a list of functions. Each element will be a function which defines a parameter e.g. id_3 <- (id_1 + id_2) / 2. See example.
state_names	vector of names of all observed states.
transition_matrix	a matrix containing a description of all speciation events, where the first column indicates the source state, the second and third column indicate the two daughter states, and the fourth column gives the rate indicator used. E.g.: ⁠["SA", "S", "A", 1]⁠ for a trait state "SA" which upon speciation generates two daughter species with traits "S" and "A", where the number 1 is used as indicator for optimization of the likelihood.
model	used model, choice of "ETD" (Examined Traits Diversification), "CTD" (Concealed Traits Diversification) or "CR" (Constant Rate).
concealed_spec_rates	vector specifying the rate indicators for each concealed state, length should be identical to num_concealed_states. If left empty when using the CTD model, it is assumed that all available speciation rates are distributed uniformly over the concealed states.
shift_matrix	matrix of shifts, indicating in order: starting state (typically the column in the transition matrix) ending state (typically the row in the transition matrix) associated rate indicator.
q_matrix	q_matrix with only transitions between observed states.
lambda_list	previously generated list of lambda matrices, used to infer the rate number to start with.
object	lambda matrices, q_matrix or mu vector.
params	parameters in order, where each value reflects the value of the parameter at that position, e.g. c(0.3, 0.2, 0.1) will fill out the value 0.3 for the parameter with rate identifier 1, 0.2 for the parameter with rate identifier 2 and 0.1 for the parameter with rate identifier 3.
param_posit	initial parameter structure, consisting of a list with three entries: lambda matrices mus Q matrix In each entry, integers numbers (1-n) indicate the parameter to be optimized.
ml_pars	resulting parameter estimates as returned by for instance cla_secsse_ml(), having the same structure as param_post.
mu_vector	previously defined mus - used to choose indicator number.
max_spec	Maximum number of species in the tree (please note that the tree is not conditioned on this number, but that this is a safeguard against generating extremely large trees).
min_spec	Minimum number of species in the tree.
max_species_extant	Should the maximum number of species be counted in the reconstructed tree (if TRUE) or in the complete tree (if FALSE).
tree_size_hist	if TRUE, returns a vector of all found tree sizes.
start_at_crown	if FALSE, the simulation starts with one species instead of the two assumed by default by secsse (also in ML), and the resulting crown age will be lower than the set crown age. This allows for direct comparison with BiSSE and facilitates implementing speciation effects at the crown.
optimmethod	A string with method used for optimization. Default is "subplex". Alternative is "simplex" and it shouldn't be used in normal conditions (only for debugging). Both are called from DDD::optimizer(), simplex is implemented natively in DDD, while subplex is ultimately called from subplex::subplex().

Value

Nothing

secsse documentation built on June 22, 2024, 11:35 a.m.