fitode: Fit ordinary differential equations model

In fitode: Tools for Ordinary Differential Equations Model Fitting

Fit ordinary differential equations model

Description

This function fits ordinary differential equations models to a uni- or multi-variate time series by maximum likelihood. It relies on sensitivity equations to compute gradients of the estimated trajectory with respect to model parameters. This allows one to use gradient-based optimization algorithms, which can provide more robust estimation.

Usage

fitode(
  model,
  data,
  start,
  tcol = "times",
  method = "BFGS",
  optimizer = "optim",
  link,
  fixed = list(),
  prior = list(),
  prior.density = TRUE,
  control = list(maxit = 1e+05),
  solver.opts = list(method = "rk4"),
  solver = ode,
  skip.hessian = FALSE,
  force.hessian = FALSE,
  use.ginv = TRUE,
  quietly = FALSE,
  ...
)

Arguments

model	odemodel object
data	data frame with a time column and observation columns
start	named vector of starting parameter values
tcol	(character) time column
method	optimization method
optimizer	optimizer
link	named vector or list of link functions for model parameters
fixed	named vector or list of model parameters to fix and their values
prior	list of formulas specifying prior distributions
prior.density	(logical) should priors represent probability distributions?
control	see optim
solver.opts	options for ode integration. See ode
solver	ode solver
skip.hessian	skip hessian calculation
force.hessian	(logical) calculate the hessian numerically instead of taking the jacobian of the gradients based on sensitivity equations
use.ginv	(logical) use generalized inverse (ginv) to compute approximate vcov
quietly	suppress progress messages?
...	mle2 arguments

Value

An object of class “fitode” as described in fitode-class.

See Also

mle2

fitode documentation built on Oct. 29, 2022, 9:05 a.m.