fit_stem: Fit a stochastic epidemic model using the linear noise...

In fintzij/stemr: Stochastic Epidemic Model Simulation and Estimation

Fit a stochastic epidemic model using the linear noise approximation or ordinary differential equations to approximate the latent epidemic process.

Description

Fit a stochastic epidemic model using the linear noise approximation or ordinary differential equations to approximate the latent epidemic process.

Usage

fit_stem(
  stem_object,
  method,
  mcmc_kern,
  iterations,
  initialization_attempts = 500,
  ess_warmup = 50,
  thinning_interval = 100,
  return_adapt_rec = FALSE,
  return_ess_rec = FALSE,
  print_progress = 0,
  status_filename = NULL
)

Arguments

stem_object	a stochastic epidemic model object containing the dataset, model dynamics, and measurement process.
method	either "lna" or "ode".
mcmc_kern	MCMC transition kernel generated by a call to the mcmc_kernel function.
iterations	number of iterations
initialization_attempts	number of initialization attempts
ess_warmup	number of preliminary ESS iterations for the LNA, initial conditions, and time varying parameters prior to starting MCMC
thinning_interval	thinning interval for posterior samples, defaults to saving every 100th sample
return_adapt_rec	should the MCMC samples be returned during adaptation? defaults to FALSE.
return_ess_rec	should elliptical slice sampling steps and angles be returned? defaults to FALSE
print_progress	interval at which to print progress to a text file. If 0 (default) progress is not printed.
status_filename	string to pre-append to status files, defaults to LNA or ODE depending on the method used.

Value

list with posterior samples for the parameters and the latent process, along with MCMC diagnostics.

fintzij/stemr documentation built on March 25, 2022, 12:25 p.m.