Class SimInf_pmcmc

Class SimInf_pmcmc

Slots

model

The SimInf_model object to estimate parameters in.

priors

A data.frame defining the prior distributions for the parameters. It contains four columns:

• parameter: The name of the parameter in the model.

• distribution: The prior distribution type. Valid values are "gamma", "lognormal", "normal", or "uniform".

• p1: The first hyperparameter:

  • "gamma": shape

  • "lognormal": meanlog (mean on the log scale)

  • "normal": mean

  • "uniform": lower bound

• p2: The second hyperparameter:

  • "gamma": rate

  • "lognormal": sdlog (standard deviation on the log scale)

  • "normal": sd (standard deviation)

  • "uniform": upper bound

target

Character vector ("gdata" or "ldata") that determines if the ABC-SMC method estimates parameters in model@gdata (global data) or in model@ldata (local data).

pars

An integer vector with the indices of the parameters in target that are being estimated.

n_particles

An integer with the number of particles (> 1) to use in the bootstrap particle filter.

data

A data.frame holding the time series data for the observation process.

chain

A matrix where each row contains logPost, logLik, logPrior, accept, and the parameters for each iteration.

covmat

A named numeric (npars x npars) matrix with covariances to use as initial proposal matrix.

adaptmix

A numeric scalar specifying the mixing proportion for the adaptive proposal distribution.

adaptive

An integer specifying when to start the adaptive update of the proposal distribution (iteration number).

See also

pmcmc for the main PMCMC function, continue_pmcmc for continuing an existing PMCMC run, and abc for ABC-SMC parameter estimation.