SimInf_pmcmc-class: Class '"SimInf_pmcmc"'

In stewid/SimInf: A Framework for Data-Driven Stochastic Disease Spread Simulations

Class "SimInf_pmcmc"

Description

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.

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

Mixing proportion for adaptive proposal.

adaptive

Controls when to start adaptive update.

See Also

pmcmc and continue_pmcmc.

stewid/SimInf documentation built on April 13, 2025, 4:05 a.m.