secsse_ml_func_def_pars: Maximum likehood estimation for (SecSSE) with parameter as...

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

Maximum likehood estimation for (SecSSE) with parameter as complex functions.

Description

Maximum likehood estimation under Several examined and concealed States-dependent Speciation and Extinction (SecSSE) where some paramaters 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 = "subplex",
  num_cycles = 1,
  loglik_penalty = 0,
  is_complete_tree = FALSE,
  num_threads = 1,
  atol = 1e-08,
  rtol = 1e-06,
  method = "odeint::bulirsch_stoer"
)

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.
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) 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)).
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().
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.
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".

Value

Parameter estimated and maximum likelihood

Examples

# Example of how to set the arguments for a ML search.
rm(list=ls(all=TRUE))
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-02, 1e-03, 1e-04)
maxiter = 1000 * round((1.25)^length(idparsopt))
optimmethod = "subplex"
cond<-"proper_cond"
root_state_weight <- "proper_weights"
sampling_fraction <- c(1,1,1)
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)
# ML -136.5796

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