APHYLO_DEFAULT_MCMC_CONTROL {aphylo}

Model estimation using Markov Chain Monte Carlo

Description

The function is a wrapper of fmcmc::MCMC().

Usage

APHYLO_DEFAULT_MCMC_CONTROL

aphylo_mcmc(
  model,
  params,
  priors = uprior(),
  control = list(),
  check_informative = getOption("aphylo_informative", FALSE),
  reduced_pseq = getOption("aphylo_reduce_pseq", TRUE)
)

APHYLO_PARAM_DEFAULT

Arguments

model

A model as specified in aphylo-model.

params

A vector of length 7 with initial parameters. In particular psi[1], psi[2], mu[1], mu[2], eta[1], eta[2] and Pi.

priors

A function to be used as prior for the model (see bprior).

control

A list with parameters for the optimization method (see details).

check_informative

Logical scalar. When TRUE the algorithm stops with an error when the annotations are uninformative (either 0s or 1s).

reduced_pseq

Logical. When TRUE it will use a reduced peeling sequence in which it drops unannotated leafs. If the model includes eta this is set to FALSE.

Format

An object of class list of length 6.

An object of class numeric of length 9.

Details

APHYLO_DEFAULT_MCMC_CONTROL lists the default values for the MCMC estimation:

• nsteps: 1e4L

• burnin: 5e3L

• thin : 10L

• nchains : 2L

• multicore : FALSE

• conv_checker : fmcmc::convergence_auto(5e3)

For more information about the MCMC estimation process, see fmcmc::MCMC().

Methods base::print(), base::summary(), stats::coef, stats::window(), stats::vcov(), stats::logLik(), predict(), and the various ways to query features of the trees via Ntip() are available post estimation.

The vector APHYLO_PARAM_DEFAULT lists the starting values for the parameters in the model. The current defaults are:

• psi0: 0.10

• psi1: 0.05

• mu_d0: 0.90

• mu_d1: 0.50

• mu_s0: 0.10

• mu_s1: 0.05

• eta0: 1.00

• eta1: 1.00

• Pi: 0.50

Value

An object of class aphylo_estimates.

See Also

Other parameter estimation: aphylo_mle()

Examples

# Using the MCMC ------------------------------------------------------------



set.seed(1233)
# Simulating a tree
tree <- sim_tree(200)

# Simulating functions
atree <- raphylo(
  tree = tree,
  psi  = c(.01, .03),
  mu_d = c(.05, .02),
  Pi   = .5
)

# Running the MCMC
set.seed(1231)

ans_mcmc <- aphylo_mcmc(
  atree ~ mu_d + psi + eta + Pi,
  control = list(nsteps = 2e5, burnin=1000, thin=200)
)