fitode {fitode}

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