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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