Propagates the uncertainty of a Bayesian calibration model to the
detection histories: for each of n_draws draws of the posterior, a
detection is reliable when its probability in that draw is >= p_min,
and the nightly history is rebuilt. The histories can then be turned
into community matrices with uncertainty by
community_matrices_draws().
Usage
history_draws(
fit,
scored,
p_min = 0.95,
n_draws = 200,
min_detections_night = 1,
min_confidence = 0.1,
grid_step = 0.01,
seed = 2026
)Arguments
- fit
Model returned by
fit_calibration()withengine = "brms".- scored
Table returned by
score_detections().- p_min
Threshold on the calibrated probability (not on the confidence score).
- n_draws
Number of posterior draws.
- min_detections_night
Repetition rule: minimum number of reliable detections for a night with detection.
- min_confidence
Detections below this confidence are not scored.
- grid_step
Models fitted with
engine = "brms": step of the grid of scores on which the posterior is computed.With a model fitted with
engine = "brms", the posterior of P(TP) is computed once on a grid of scores (stepgrid_step) for each site x species and assigned to the detections by their score rounded to the grid, so that the cost does not depend on the number of detections; the rounding error is small next to the uncertainty of the calibration. Each detection then gets the posterior mean (p_tp), a lower quantile (p_tp_lower) and the posterior probability that P(TP) >=p_min(pr_reliable).detection_history()can use either rule.- seed
Seed of the choice of draws. The global random number generator is left as it was.
Value
A tibble with draw, site, species, night,
n_detections, n_reliable, detection_k1 to detection_k3 and
detection.
Details
As in score_detections(), the probabilities are computed on a grid of
scores (step grid_step) for each site x species and assigned to the
detections by their rounded score, which keeps the cost independent of
the number of detections.