SimInf_abc-class {SimInf}

Class "SimInf_abc"

Description

Class "SimInf_abc"

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, 'normal') mean, and 'uniform') lower bound. The column p2 is a numeric vector with the second hyperparameter for each prior: 'gamma') rate, 'normal') standard deviation, and 'uniform') upper bound.

target

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

pars

Index to the parameters in target.

nprop

An integer vector with the number of simulated proposals in each generation.

fn

A function for calculating the summary statistics for the simulated trajectory and determine the distance for each particle, see abc for more details.

tolerance

A numeric matrix (number of summary statistics \times number of generations) where each column contains the tolerances for a generation and each row contains a sequence of gradually decreasing tolerances.

x

A numeric array (number of particles \times number of parameters \times number of generations) with the parameter values for the accepted particles in each generation. Each row is one particle.

weight

A numeric matrix (number of particles \times number of generations) with the weights for the particles x in the corresponding generation.

distance

A numeric array (number of particles \times number of summary statistics \times number of generations) with the distance for the particles x in each generation. Each row contains the distance for a particle and each column contains the distance for a summary statistic.

ess

A numeric vector with the effective sample size (ESS) in each generation. The effective sample size is computed as

\left(\sum_{i=1}^N\!(w_{g}^{(i)})^2\right)^{-1},

where w_{g}^{(i)} is the normalized weight of particle i in generation g.

See Also

abc and continue.