prevalence-SimInf_pfilter-method: Extract prevalence from running a particle filter
In stewid/SimInf: A Framework for Data-Driven Stochastic Disease Spread Simulations

prevalence,SimInf_pfilter-method

R Documentation

Extract prevalence from running a particle filter

Description

Extract prevalence from running a particle filter

Usage

## S4 method for signature 'SimInf_pfilter'
prevalence(model, formula, level, index, format = c("data.frame", "matrix"))

Arguments

`model`	the `SimInf_pfilter` 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. Note that if the denominator is zero, the prevalence is `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.
`format`	The default (`format = "data.frame"`) is to generate a `data.frame` with one row per time-step with the prevalence. Using `format = "matrix"` returns the result as a matrix.