trajectory-SimInf_pmcmc-method: Extract filtered trajectories from fitting a PMCMC algorithm

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

Extract filtered trajectories from fitting a PMCMC algorithm

Description

Extract filtered trajectories from a particle Markov chain Monte Carlo algorithm.

Usage

## S4 method for signature 'SimInf_pmcmc'
trajectory(model, compartments, index, start = 1, end = NULL, thin = 1)

Arguments

model	the SimInf_pmcmc object to extract the filtered trajectories from.
compartments	specify the names of the compartments to extract data from. The compartments can be specified as a character vector e.g. compartments = c('S', 'I', 'R'), or as a formula e.g. compartments = ~S+I+R (see ‘Examples’). Default (compartments=NULL) is to extract the number of individuals in each compartment i.e. the data from all discrete state compartments in the model. In models that also have continuous state variables e.g. the SISe model, they are also included.
index	indices specifying the subset of nodes to include when extracting data. Default (index = NULL) is to extract data from all nodes.
start	The start iteration to remove some burn-in iterations. Default is start = 1.
end	the last iteration to include. Default is NULL which set end to the last iteration in the chain.
thin	keep every thin iteration after the start iteration. Default is thin = 1, i.e., keep every iteration.

Value

A data.frame where the first column is the iteration and the remaining columns are the result from calling trajectory,SimInf_model-method with the arguments compartments and index for each iteration.

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