Severity Estimate Model — SeverityEstimateModel-class • SeverityEstimate

A representation of a severity estimate model and its metadata.

Usage

SeverityEstimateModel(line_list, population)

# S3 method for class 'SeverityEstimateModel'
summary(object, ...)

# S3 method for class 'SeverityEstimateModel'
print(x, ...)

# S4 method for class 'SeverityEstimateModel'
show(object)

# S3 method for class 'SummaryEstimateModel'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

line_list: A line list of cases to model the severity of.
population: A dataset containing information on the population broken down by stratification. Can also be a single integer in the case that the model is not stratified.
object: An object of class SeverityEstimateModel.
...: For summary.SeverityEstimateModel() and print.SummaryEstimateModel(), unused. For print.SeverityEstimateModel(), further arguments passed to print.SummaryEstimateModel().
x: An object of class SeverityEstimateModel or SummaryEstimateModel.
digits: The number of significant digits to print for prior parameters.

Value

A function-dependent value:

SeverityEstimateModel() returns a SeverityEstimateModel object.
summary.SeverityEstimateModel() returns a SummaryEstimateModel.
print.SeverityEstimateModel(), show(), and print.SummaryEstimateModel() invisibly return their input object.

Slots

line_list: A line list of cases to model the severity of.
population: A dataset containing information on the population broken down by stratification.
strata: A list of model stratification specifications.
timesteps: A list specifying the timestep column of the linelist.
detection: A list specifying the detection type mapping.
outcome: A list specifying the outcome severity mapping.
active_prior: Parameters for the beta distribution prior for the active detection rate.
passive_asymptomatic_prior: Parameters for the beta distribution prior for the passive asymptomatic detection rate.
passive_symptomatic_prior: Parameters for the beta distribution prior for the passive symptomatic detection rate.

Functions and methods

SeverityEstimateModel(line_list, population) creates a SeverityEstimateModel object.
summary(object) summarises a user-defined severity estimate model by reporting input data dimensions, detection probability priors, timestep bounds, mapped detection and outcome counts, and strata specifications.
print.SeverityEstimateModel(x) prints a compact summary of a SeverityEstimateModel object.
show(object) shows a compact summary of a SeverityEstimateModel object.
print.SummaryEstimateModel(x, digits) prints a SummaryEstimateModel object in a structured format.

Examples

line_list <- data.frame(
  patient = 1L:3L,
  week = c(1L, 1L, 2L),
  age = c("Youth", "Adult", "Senior"),
  detection = c("Active", "Passive", "Active"),
  outcome = c("Asymptomatic", "Death", "Symptomatic")
)
population <- data.frame(
  age = c("Youth", "Adult", "Senior"),
  amount = rep(987L, 3L)
)
model <- SeverityEstimateModel(line_list, population)
summary(model)
#> Severity Estimate Model:
#> 
#> Data:
#>     dataset rows columns
#>   line_list    3       5
#>  population    3       2
#> 
#> Detection Probability Priors:
#>   active prior: beta(1.0, 1.0) (default)
#>   passive_asymptomatic prior: beta(1.0, 1.0) (default)
#>   passive_symptomatic prior: beta(1.0, 1.0) (default)
#> 
#> Timesteps:
#>   not set
#> 
#> Detection:
#>   not set
#> 
#> Outcome:
#>   not set
#> 
#> Strata:
#>   none