Default parameter documentation
This function's purpose is to list all parameter documentation to be inherited by the relevant functions.
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 )
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:
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:
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().
Nothing
Useful links