de_mcmc

Bayesian inference for a deterministic DE model (with models solved via an DE solver) with an observation model.

de_mcmc(
  N,
  data,
  de.model,
  obs.model,
  all.params,
  ref.params = NULL,
  ref.inits = NULL,
  Tmax,
  data.times,
  cnt = 10,
  plot = TRUE,
  sizestep = 0.01,
  solver = "ode",
  verbose.mcmc = TRUE,
  verbose = FALSE,
  ...
)

Arguments

N	integer, number of MCMC iterations
data	data.frame of time course observations to fit the model to. The observations must be ordered ascending by time.
de.model	a function defining a DE model, compliant with the solvers in deSolve or PBSddesolve
obs.model	a function defining an observation model. Must be a function with arguments 'data', 'sim.data', 'samp'.
all.params	debinfer_parlist containing all model, MCMC, and observation
ref.params	an optional named vector containing a set of reference parameters, e.g. the true parameters underlying a simulated data set
ref.inits	an optional named vector containing a set of reference initial values, e.g. the true initial values underlying a simulated data set
Tmax	maximum timestep for solver
data.times	time points for which observations are available
cnt	integer interval at which to print and possibly plot information on the current state of the MCMC chain
plot	logical, plot traces for all parameters at the interval defined by cnt
sizestep	timestep for solver to return values at, only used if data.times is missing
solver	the solver to use. 1 or "ode" = deSolve::ode; 2 or "dde" = PBSddesolve::dde; 3 or "dede" = deSolve::dde
verbose.mcmc	logical display MCMC progress messages
verbose	logical display verbose solver output
...	further arguments to the solver

Value

a debinfer_result object containing input parameters, data and MCMC samples