aphylo_mle {aphylo}

Model estimation using Maximum Likelihood Estimation

Description

The function is a wrapper of stats::optim().

Usage

aphylo_mle(
  model,
  params,
  method = "L-BFGS-B",
  priors = function(p) 1,
  control = list(),
  lower = 1e-05,
  upper = 1 - 1e-05,
  check_informative = getOption("aphylo_informative", FALSE),
  reduced_pseq = getOption("aphylo_reduce_pseq", TRUE)
)

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.

method, control, lower, upper

Arguments passed to stats::optim().

priors

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

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.

Details

The default starting parameters are described in APHYLO_PARAM_DEFAULT.

Value

An object of class aphylo_estimates.

See Also

Other parameter estimation: APHYLO_DEFAULT_MCMC_CONTROL

Examples

# Using simulated data ------------------------------------------------------
set.seed(19)
dat <- raphylo(100)
dat <- rdrop_annotations(dat, .4)

# Computing Estimating the parameters 
ans  <- aphylo_mle(dat ~ psi + mu_d + eta + Pi)
ans

# Plotting the path
plot(ans)

# Computing Estimating the parameters Using Priors for all the parameters
mypriors <- function(params) {
    dbeta(params, c(2, 2, 2, 2, 1, 10, 2), rep(10, 7))
}

ans_dbeta <- aphylo_mle(dat ~ psi + mu_d + eta + Pi, priors = mypriors)
ans_dbeta