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Compute power curve for detection across prevalence range

Source: R/12-advanced.R
surv_power_curve.Rd

Generates a detection probability curve that can be directly plotted or included in publications. Answers: "At what prevalence does our surveillance achieve X% detection?"

Usage

surv_power_curve(
  design,
  prevalence_range = seq(0.001, 0.05, by = 0.001),
  delay_fit = NULL,
  thresholds = c(0.5, 0.8, 0.95)
)

# S3 method for class 'surv_power_curve'
plot(x, ...)

Arguments

design

A surv_design object.

prevalence_range

Numeric vector of prevalences to evaluate. Default seq(0.001, 0.05, by = 0.001).

delay_fit

Optional surv_delay_fit.

thresholds

Numeric vector of detection thresholds to mark. Default c(0.5, 0.8, 0.95).

x

A surv_power_curve object.

...

Additional arguments (unused).

Value

A list with:

curve

Tibble with prevalence and detection columns.

thresholds

Tibble with threshold, prevalence_needed columns.

A ggplot2 object.

See also

surv_detection_probability(), surv_required_sequences()

Examples

sim <- surv_simulate(n_regions = 3, n_weeks = 10, seed = 1)
d <- surv_design(sim$sequences, ~ region,
                 sim$population[c("region", "seq_rate")], sim$population)
pc <- surv_power_curve(d)
pc$thresholds
#> # A tibble: 3 × 2
#>   threshold prevalence_needed
#>       <dbl>             <dbl>
#> 1      0.5              0.01 
#> 2      0.8              0.021
#> 3      0.95             0.039