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Calculate Key Parameter Estimates

Source: R/calculate_parameter_estimates.R
calculate_parameter_estimates.Rd

Calculate a summary data.frame describing the key parameters fitted including:

  • The mildly/asymptomatic passive detection rate,

  • The severe symptoms passive detection rate, and

  • The active detection rate.

Usage

calculate_parameter_estimates(x, ...)

# S3 method for class 'SeverityEstimateFit'
calculate_parameter_estimates(
  x,
  mean_estimate = TRUE,
  median_estimate = TRUE,
  alpha = 0.05,
  include_description = TRUE,
  ...
)

# S3 method for class 'list'
calculate_parameter_estimates(
  x,
  mean_estimate = TRUE,
  median_estimate = TRUE,
  alpha = 0.05,
  include_description = TRUE,
  ...
)

# Default S3 method
calculate_parameter_estimates(x, ...)

Arguments

x

A object to calculate fatality ratio statistics from, typically a SeverityEstimateFit S4 object.

...

Further arguments passed to other methods.

mean_estimate

A single logical indicating if the mean estimate for the parameters should be included in the 'mean_estimate' column of the returned data.frame.

median_estimate

A single logical indicating if the median estimate for the parameters should be included in the 'median_estimate column of the returned data.frame.

alpha

A numeric of significance levels to return the parameters confidence intervals for. The columns will be in '{lower/upper}_{alpha}' format (i.e. 'lower_05' and 'upper_05' for alpha=0.05).

include_description

A single logical indicating if descriptions of the key parameters should be included in the return value. Setting this to TRUE is handy for ad-hoc work, but setting to FALSE can be better for production code. The description will be given in the 'parameter_description' column of the returned data.frame.

Value

calculate_parameter_estimates.SeverityEstimateFit returns a data.frame with the column 'parameter'. Other columns are determined by other parameters, see above for details.

calculate_parameter_estimates.default signals an error.

Examples

draws <- list(
  active_detection = c(0.85, 0.90, 0.92),
  passive_asymptomatic_detection = c(0.15, 0.20, 0.18),
  passive_symptomatic_detection = c(0.55, 0.60, 0.58)
)
calculate_parameter_estimates(draws, alpha = numeric())
#>                        parameter                      parameter_description
#> 1               active_detection                      active detection rate
#> 2 passive_asymptomatic_detection mildly/asymptomatic passive detection rate
#> 3  passive_symptomatic_detection     severe symptoms passive detection rate
#>   mean_estimate median_estimate
#> 1     0.8900000            0.90
#> 2     0.1766667            0.18
#> 3     0.5766667            0.58