View source: R/default_params_doc.R
default_params_doc  R 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 
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

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 
parsfix 
a numeric vector with the value of the fixed parameters. 
cond 
condition on the existence of a node root: 
root_state_weight 
the method to weigh the states:

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 
maxiter 
max number of iterations. Default is

num_cycles 
Number of cycles of the optimization. When set to 
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 
verbose 
sets verbose output; default is 
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:

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 : 
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 
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 nonextinction 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 
non_extinction 
boolean stating if the tree should be conditioned on
nonextinction of the crown lineages. Defaults to 
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 
seed 
pseudorandom 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, 
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 
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 
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 
a list of functions. Each element will be a
function which defines a parameter e.g. 
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.: 
model 
used model, choice of 
concealed_spec_rates 
vector specifying the rate indicators for each
concealed state, length should be identical to 
shift_matrix 
matrix of shifts, indicating in order:

q_matrix 

lambda_list 
previously generated list of lambda matrices, used to infer the rate number to start with. 
object 
lambda matrices, 
params 
parameters in order, where each value reflects the value
of the parameter at that position, e.g. 
param_posit 
initial parameter structure, consisting of a list with three entries:
In each entry, integers numbers (1n) indicate the parameter to be optimized. 
ml_pars 
resulting parameter estimates as returned by for instance

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

Nothing
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.