Severity Estimate Fit Class

This class contains the output from a severity estimate model fitting.

Usage

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

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

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

Arguments

x

An object of class SeverityEstimateFit or SummaryEstimateFit.

...

For summary.SeverityEstimateFit(), unused. For print.SeverityEstimateFit(), further arguments passed to the print method for a stanfit object. For print.SummaryEstimateFit(), further arguments passed to print.data.frame().

object

An object of class SeverityEstimateFit.

digits

The number of significant digits to print.

Value

A function-dependent value:

• summary.SeverityEstimateFit() returns a SummaryEstimateFit with elements detection_rates and severity_estimates.

• print.SeverityEstimateFit() and print.SummaryEstimateFit() invisibly return their input object.

Slots

model_fit

A stanfit object returned from fitting a severity estimate model.

population

The population data used in model fitting in array form with dimensions corresponding to 'time_period' and 'strata'.

incidence

The line list data used in model fitting in array form counting incidence with dimensions corresponding to 'time_period', 'strata', 'surveillance', and 'outcome'.

time_period

A data.frame with the variables describing the 'time_period' dimensions of population and incidence.

strata

A data.frame with the variables describing the 'strata' dimensions of population and incidence.

surveillance

A data.frame with the variables describing the 'surveillance' dimension of incidence.

outcome

A data.frame with the variables describing the 'outcome' dimension of incidence.

Functions and methods

• summary(object) summarises a fitted severity estimate model by reporting mean detection rate estimates and mean IFR/SIR estimates by strata.

• print.SeverityEstimateFit(x) prints a SeverityEstimateFit object in a structured format. Currently this prints the model_fit slot using the print method for a stanfit object.

• print.SummaryEstimateFit(x, digits) prints a SummaryEstimateFit object in a structured format.

Examples

# \donttest{
set.seed(1)
line_list <- data.frame(
  time = 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 <- default_model(line_list, population)
fitted_model <- suppressWarnings(fit(
  model,
  chains = 1L,
  cores = 1L,
  iter = 10L,
  warmup = 5L,
  seed = 1,
  refresh = 0
))
summary(fitted_model)
#> Detection Rates:
#>                      Estimate
#> passive_asymptomatic   0.2670
#> passive_symptomatic    0.8934
#> active                 0.5253
#> 
#> Severity Estimates:
#>     age IFR Estimate SIR Estimate
#>   Adult       0.7987       0.8570
#>  Senior       0.2675       0.9178
#>   Youth       0.2517       0.2178
# }