Estimate angler trips from extrapolated effort

Computes estimated trips by dividing extrapolated effort by the mean trip length per stratum, with Delta Method variance propagation (Powell 2007). This is a composable estimator: the effort object must be pre-computed via estimate_effort before calling this function.

The divisor is hours per trip, so the result comes back in whichever actor the effort was measured in: angler-hours give angler trips, party-hours give party trips. The returned unit field records which, and is NA when the effort's own unit was unknown. The method string is "angler-trips" in every case and so is not a guide to the actor.

Usage

estimate_angler_trips(effort, design, conf_level = 0.95, ...)

Arguments

effort

A creel_estimates object returned by estimate_effort. Must have a numeric estimate column and a se column in effort$estimates.

design

A creel_design object with interview data containing a trip duration column (set via add_interviews(trip_duration = ...)). Used to compute per-stratum mean trip length.

conf_level

Confidence level for confidence intervals. Default 0.95.

...

Reserved for future arguments.

Value

A creel_estimates object with method = "angler-trips" and variance_method = "delta". The estimates tibble contains:

by_vars columns

Any grouping columns from the effort object (if grouped).

estimate

Estimated trips per stratum (effort / mean trip length), in the actor the effort was measured in.

se

Standard error via Delta Method variance propagation.

ci_lower

Lower confidence interval bound.

ci_upper

Upper confidence interval bound.

n

Number of interviews contributing to mean trip length per stratum.

For grouped effort, an .overall row is appended with estimate = sum(stratum trips) and se propagated by addition in quadrature.

References

Powell, L. A. (2007). Approximating variance of demographic parameters using the delta method. Journal of Wildlife Management, 71(3), 1018-1024.

See also

estimate_effort, estimate_exploitation_rate

Examples

data(example_calendar)
data(example_counts)
data(example_interviews)
design <- creel_design(example_calendar, date = date, strata = day_type)
design <- add_counts(design, example_counts)
#> Warning: No weights or probabilities supplied, assuming equal probability
design <- add_interviews(design, example_interviews,
  catch = catch_total, effort = hours_fished, harvest = catch_kept,
  trip_status = trip_status, trip_duration = trip_duration
)
#> Warning: ! No `n_anglers` provided — assuming 1 angler per interview.
#> ℹ Pass `n_anglers = <column>` to use actual party sizes for angler-hour
#>   normalization.
#> ℹ If the interviews really are one angler each, pass `n_anglers = 1` to state
#>   that and silence this warning.
#> ℹ Added 22 interviews: 17 complete (77%), 5 incomplete (23%)
effort <- estimate_effort(design)
estimate_angler_trips(effort, design)
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: angler-trips
#> Variance: delta
#> Confidence level: 95%
#> 
#> # A tibble: 1 × 5
#>   estimate    se ci_lower ci_upper     n
#>      <dbl> <dbl>    <dbl>    <dbl> <int>
#> 1     161.  16.9     126.     197.    22