prevalence,SimInf_pmcmc-method: 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 SimInf_pmcmc
prevalence(model, formula, level, index, start = 1, end = NULL, thin = 1)

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.

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.