secsse_ml_func_def_pars: Maximum likehood estimation for (SecSSE) complex functions as...

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

Maximum likehood estimation for (SecSSE) complex functions as parameter

Description

Maximum likehood estimation under Several examined and concealed States-dependent Speciation and Extinction (SecSSE) where some parameters are functions of other parameters and/or factors.

Usage

secsse_ml_func_def_pars(
  phy,
  traits,
  num_concealed_states,
  idparslist,
  idparsopt,
  initparsopt,
  idfactorsopt,
  initfactors,
  idparsfix,
  parsfix,
  idparsfuncdefpar,
  functions_defining_params = NULL,
  cond = "proper_cond",
  root_state_weight = "proper_weights",
  sampling_fraction,
  tol = c(1e-04, 1e-05, 1e-07),
  maxiter = 1000 * round((1.25)^length(idparsopt)),
  optimmethod = "simplex",
  num_cycles = 1,
  loglik_penalty = 0,
  is_complete_tree = FALSE,
  take_into_account_root_edge = FALSE,
  num_threads = 1,
  atol = 1e-08,
  rtol = 1e-06,
  method = "odeint::runge_kutta_cash_karp54",
  return_root_state = FALSE
)

Arguments

phy	phylogenetic tree of class phylo, rooted and with branch lengths. Alternatively, multiple phylogenetic trees can be provided as the multiPhylo 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 vignette("starting_secsse", package = "secsse"). When providing a multiPhylo set of multiple phylognies, traits should be a list where each entry in the list corresponds to the matching phylogeny on that position.
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.
idparsopt	a numeric vector with the ID of parameters to be estimated.
initparsopt	a numeric vector with the initial guess of the 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.
initfactors	the initial guess for a factor (it should be set to NULL when no factors).
idparsfix	a numeric vector with the ID of the fixed parameters.
parsfix	a numeric vector with the value of the fixed parameters.
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.
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), ⁠"equal_weights"'. or ⁠"stationary_weights"⁠It can also be specified for the root state: the vector⁠c(1, 0, 0)⁠ indicates state 1 was the root state. When using a⁠multiPhylo' object, root_state_weight should be list where each entry in the list corresponds to the root_state_weight for each tree.
sampling_fraction	vector that states the sampling proportion per trait state. It must have as many elements as there are trait states. When using a multiPhylo object, sampling fraction should be list where each entry in the list corresponds to the sampling proportion for each tree.
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)).
optimmethod	A string with method used for optimization. Default is "simplex". Alternative is "subplex". Both are called from DDD::optimizer(), simplex is implemented natively in DDD, while subplex is ultimately called from subplex::subplex().
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.
take_into_account_root_edge	if TRUE, the LL integration is continued along the root edge. This also affects conditioning (as now, conditioning no longer needs to assume a speciation event at the start of the tree)
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	ODE integration method. Choose from: "odeint::runge_kutta_cash_karp54", "odeint::runge_kutta_fehlberg78", "odeint::runge_kutta_dopri5", "odeint::bulirsch_stoer" and "odeint::runge_kutta4". Default method is: "odeint::runge_kutta_cash_karp54".
return_root_state	if TRUE, returns the state of the system at the root, this can be useful to use as the starting point of a simulation. When used in ML, after finishing the ML optimization, the found optimum is evaluated one more time to retrieve the root state (to avoid having to store the root state every ML evaluation).

Value

Parameter estimates and maximum likelihood

Examples

# Example of how to set the arguments for an ML search. The ML search is stopped
# after 10 iterations to keep run time short.
library(secsse)
library(DDD)
set.seed(16)
phylotree <- ape::rbdtree(0.07,0.001,Tmax=50)
startingpoint<-bd_ML(brts = ape::branching.times(phylotree))
intGuessLamba <- startingpoint$lambda0
intGuessMu <- startingpoint$mu0
traits <- sample(c(0,1,2), ape::Ntip(phylotree),replace=TRUE) #get some traits
num_concealed_states<-3
idparslist<-id_paramPos(traits, num_concealed_states)
idparslist[[1]][c(1,4,7)] <- 1
idparslist[[1]][c(2,5,8)] <- 2
idparslist[[1]][c(3,6,9)] <- 3
idparslist[[2]][] <- 4
masterBlock <- matrix(c(5,6,5,6,5,6,5,6,5),ncol = 3,nrow = 3,byrow = TRUE)
diag(masterBlock) <- NA
diff.conceal <- FALSE
idparslist[[3]] <- q_doubletrans(traits,masterBlock,diff.conceal)
idparsfuncdefpar <- c(3,5,6)
idparsopt <- c(1,2)
idparsfix <- c(0,4)
initparsopt <- c(rep(intGuessLamba,2))
parsfix <- c(0,0)
idfactorsopt <- 1
initfactors <- 4
# functions_defining_params is a list of functions. Each function has no
# arguments and to refer
# to parameters ids should be indicated as "par_" i.e. par_3 refers to
# parameter 3. When a function is defined, be sure that all the parameters
# involved are either estimated, fixed or
# defined by previous functions (i.e, a function that defines parameter in
# 'functions_defining_params'). The user is responsible for this. In this
# exampl3, par_3 (i.e., parameter 3) is needed to calculate par_6. This is
# correct because par_3 is defined in
# the first function of 'functions_defining_params'. Notice that factor_1
# indicates a value that will be estimated to satisfy the equation. The same
# factor can be shared to define several parameters.
functions_defining_params <- list()
functions_defining_params[[1]] <- function(){
 par_3 <- par_1 + par_2
}
functions_defining_params[[2]] <- function(){
 par_5 <- par_1 * factor_1
}
functions_defining_params[[3]] <- function(){
 par_6 <- par_3 * factor_1
}

tol = c(1e-03, 1e-03, 1e-03)
optimmethod = "simplex"
cond<-"proper_cond"
root_state_weight <- "proper_weights"
sampling_fraction <- c(1,1,1)
maxiter <- 10
model <- secsse_ml_func_def_pars(phylotree,
traits,
num_concealed_states,
idparslist,
idparsopt,
initparsopt,
idfactorsopt,
initfactors,
idparsfix,
parsfix,
idparsfuncdefpar,
functions_defining_params,
cond,
root_state_weight,
sampling_fraction,
tol,
maxiter,
optimmethod,
num_cycles = 1)
print(model$ML)
# ML -136.45265 if run till completion

secsse documentation built on May 15, 2026, 5:06 p.m.