Plan a survey design via the /plan API — sight_plan

Calls the sightability API to produce a planned survey design (sample allocation across strata) from an authoritative design supplied by the caller, rather than one looked up server-side.

Usage

sight_plan_id(
  method = "stratified",
  target = "precision",
  value,
  design = NULL,
  variance = NULL,
  confidence = 0.9,
  density = NULL,
  width_m = NULL,
  sides = NULL,
  mean_group_size = NULL,
  b = NULL,
  k = NULL,
  group_cv = NULL,
  area_km2 = NULL,
  dry_run = FALSE,
  ...
)

Arguments

method: Character. Survey method: "stratified" or "distance".
target: Character. Allocation target: "precision" (target error bound) or "fixed" (target sample size).
value: Numeric. For "precision": desired error bound. For "fixed": target sample size.
design: Data frame or list. Authoritative per-stratum design, e.g. with columns stratum_id, subunits_available, pop_sd, cost, and include. Stratified method only.
variance: List. Variance components with elements samp_var, sight_var, and mod_var. Stratified method only.
confidence: Numeric. Confidence level for the error bound (default 0.90 for 90% CI). Must be between 0 and 1.
density: Numeric. Expected population density. Distance method only.
width_m: Numeric. Transect half-width in meters. Distance method only.
sides: Numeric. Number of sides searched per transect (1 or 2). Distance method only.
mean_group_size: Numeric. Expected mean group size. Distance method only.
b: Numeric. Truncation distance parameter. Distance method only.
k: Numeric. Detection function shape parameter. Distance method only.
group_cv: Numeric. Coefficient of variation for group size. Distance method only.
area_km2: Numeric. Total survey area in square kilometers. Distance method only.
dry_run: If TRUE, print the HTTP request without sending it and return the request object invisibly. Useful for debugging.
...: Additional arguments passed to the underlying spdgt.auth function (e.g., verbosity, timeout).

Value

Tibble with the planned design, mirroring the input design with allocation columns (e.g. sample_proportion, n_samples, dau_proportion) added by the /plan endpoint.

Examples

if (FALSE) { # \dontrun{
sight_plan_id(
  method = "stratified",
  target = "fixed",
  value = 100,
  design = data.frame(
    stratum_id = c(1, 2),
    subunits_available = c(50, 80),
    pop_sd = c(5, 12),
    cost = c(1, 1),
    include = c(TRUE, TRUE)
  ),
  variance = list(samp_var = 15000, sight_var = 2000, mod_var = 800)
)
} # }