View source: R/simulation_augmentPD.R
| augmentPD | R Documentation | 
This function performs augmentation of phylogenetic diversity (PD) on a given phylogenetic tree using the Monte Carlo Expectation-Maximization (MCEM) algorithm. It aims to estimate the maximum likelihood parameters for the given phylo object based on the provided parameters and constraints.
augmentPD(
  phylo,
  pars,
  maxN,
  max_missing,
  lower_bound,
  upper_bound,
  num_threads = 1,
  sample_size = 1,
  soc = 2,
  xtol_rel = 1e-05,
  verbose = FALSE
)
phylo | 
 A phylogenetic tree of class 'phylo'.  | 
pars | 
 A numeric vector of initial parameters for the MCEM algorithm.  | 
maxN | 
 The maximum number of iterations for the MCEM algorithm.  | 
max_missing | 
 The maximum amount of missing data allowed.  | 
lower_bound | 
 The lower bound constraint for parameter optimization.  | 
upper_bound | 
 The upper bound constraint for parameter optimization.  | 
num_threads | 
 The number of threads to be used for parallel computation. Defaults to 1.  | 
sample_size | 
 The size of the sample to be used in the MCEM algorithm. Defaults to 1.  | 
soc | 
 The second order correction for the MCEM algorithm. Defaults to 2.  | 
xtol_rel | 
 The relative tolerance for convergence in the optimization algorithm. Defaults to 0.00001.  | 
verbose | 
 A logical value indicating if progress messages should be printed. Defaults to FALSE.  | 
The function uses the branching.times function from the 'ape' package to calculate the branching times of the phylogenetic tree. It then calls a C++ function through the .Call interface for the MCEM algorithm. The parameters 'max_lambda', 'brts', and 'result' are used internally within the function.
The result of the MCEM algorithm, which could be parameters estimates, log-likelihood values, or other relevant metrics depending on the implementation of the C++ function '_emphasis_rcpp_mce'.
## Not run: 
  data(bird.orders) # assuming bird.orders is a phylo object
  initial_pars <- c(0.1, 0.1)
  result <- augmentPD(phylo = bird.orders, pars = initial_pars, 
                      maxN = 1000, max_missing = 0.1, 
                      lower_bound = 0.001, upper_bound = 10)
## End(Not run)
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