surv_power_curve: Compute power curve for detection across prevalence range

In survinger: Design-Adjusted Inference for Pathogen Lineage Surveillance

Compute power curve for detection across prevalence range

Description

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

survinger documentation built on April 27, 2026, 9:10 a.m.