Class

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.