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Extract prevalence from fitting a PMCMC algorithm

Source: R/prevalence.R
prevalence-SimInf_pmcmc-method.Rd

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

Usage

# S4 method for class '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.