Class "SimInf_pmcmc"

Class "SimInf_pmcmc"

Slots

model

The SimInf_model object to estimate parameters in.

priors

A data.frame containing the four columns parameter, distribution, p1 and p2. The column parameter gives the name of the parameter referred to in the model. The column distribution contains the name of the prior distribution. Valid distributions are 'gamma', 'normal' or 'uniform'. The column p1 is a numeric vector with the first hyperparameter for each prior: 'gamma') shape, 'lognormal') logmean, 'normal') mean, and 'uniform') lower bound. The column p2 is a numeric vector with the second hyperparameter for each prior: 'gamma') rate, 'lognormal') standard deviation on the log scale, 'normal') standard deviation, and 'uniform') upper bound.

target

Character vector (gdata or ldata) that determines if the pmcmc method estimates parameters in model@gdata or in model@ldata.

pars

Index to the parameters in target.

n_particles

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

obs_process

A formula or function determining the observation process.

init_model

FIXME.

post_particle

An optional function that, if non-NULL, is applied after each completed particle. The function must accept three arguments: 1) an object of SimInf_pmcmc with the current state of the fitting process, 2) an object SimInf_pfilter with the last particle and one filtered trajectory attached, and 3) an integer with the iteration in the fitting process. This function can be useful to, for example, monitor, save and inspect intermediate results.

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.

pf

List with the filtered trajectory from each iteration.

covmat

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

adaptmix

Mixing proportion for adaptive proposal.

adaptive

Controls when to start adaptive update.

See also

pmcmc and continue_pmcmc.