Coverage as a function of verification effort

Runs fp_calibrate() over a grid of verification fractions and prints the coverage of the occupancy slope against the fraction of sites verified. If the constant-rate model does not approach nominal coverage as verification grows, the false positives are probably structured, and the remedy is to model the false-positive rate (for instance on the occupancy covariate), not to verify more.

Usage

fp_verification_curve(
  ...,
  frac_verified = c(0.1, 0.3, 0.5, 0.7, 0.9),
  models = list(constant = ~1),
  occ = ~x,
  det = ~1,
  reps = 50,
  base_seed = 1L,
  parallel = FALSE,
  fit_args = list(chains = 4, parallel_chains = 1, iter_warmup = 1000, iter_sampling =
    1000, adapt_delta = 0.95)
)

Arguments

...

Arguments of simulate_fp() that define the scenario (held fixed), e.g. p10 = 0.05, conf_effect = 1.5, conf_cor = 0.7.

frac_verified

Fractions of sites verified to sweep.

models, occ, det, reps, base_seed, parallel, fit_args

As in fp_calibrate().

Value

The fp_calibration object (invisibly), after printing the curve.

Examples

if (FALSE) { # \dontrun{
fp_verification_curve(p10 = 0.05, conf_effect = 1.5, conf_cor = 0.7,
                      models = list(constant = ~ 1, fp_on_x = ~ x),
                      reps = 50)
} # }