prevalence-SimInf_pmcmc-method: Extract prevalence from fitting a PMCMC algorithm

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

Extract prevalence from fitting a PMCMC algorithm

Description

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

Usage

## S4 method for signature 'SimInf_pmcmc'
prevalence(model, formula, level, index, start = 1, end = NULL, thin = 1)

Arguments

model	the SimInf_pmcmc object to extract the prevalence from.
formula	A formula that specifies the compartments that define the cases with a disease or that have a specific characteristic (numerator), and the compartments that define the entire population of interest (denominator). The left-hand-side of the formula defines the cases, and the right-hand-side defines the population, for example, I~S+I+R in a ‘SIR’ model (see ‘Examples’). The . (dot) is expanded to all compartments, for example, I~. is expanded to I~S+I+R in a ‘SIR’ model (see ‘Examples’). The formula can also contain a condition (indicated by |) for each node and time step to further control the population to include in the calculation, for example, I ~ . | R == 0 to calculate the prevalence when the recovered is zero in a ‘SIR’ model. The condition must evaluate to TRUE or FALSE in each node and time step. Please note, if the denominator is zero, the prevalence is NaN. Additionally, when level=3 (within-node prevalence) and the formula contains a condition that evaluates to FALSE, the prevalence is also NaN.
level	The level at which the prevalence is calculated at each time point in tspan. 1 (population prevalence): calculates the proportion of the individuals (cases) in the population. 2 (node prevalence): calculates the proportion of nodes with at least one case. 3 (within-node prevalence): calculates the proportion of cases within each node. Default is 1.
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 prevalence,SimInf_model-method with the arguments formula, level and index for each iteration.

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