Incomplete-Trip Interviews

roving_design <- creel_design(example_calendar, date = date, strata = day_type) |>
  add_counts(prep_counts_daily_effort(example_counts,
    date = date, strata = day_type, daily_effort = effort_hours
  )) |>
  add_interviews(
    example_interviews,
    catch = catch_total, effort = hours_fished, n_anglers = n_anglers,
    harvest = catch_kept, trip_status = trip_status,
    trip_duration = trip_duration,
    interview_type = "roving"
  )
#> ℹ Added 22 interviews: 17 complete (77%), 5 incomplete (23%)

estimate_catch_rate(roving_design)$method
#> ℹ Roving design: using all 22 interviews for CPUE via MOR [auto]
#>   (17 complete, 5 incomplete)
#>   Override with `use_trips = 'complete'` for access-point estimation.
#> ℹ MOR truncation: 0 trips excluded (all >= 0.5 hours)
#> Warning: Small sample size for CPUE estimation.
#> ! Sample size is 22. Ratio estimates are more stable with n >= 30.
#> ℹ Variance estimates may be unstable with n < 30.
#> ℹ Repeats on this design are recorded, not printed: see `get_warnings()`.
#> [1] "mean-of-ratios-cpue"
estimate_catch_rate(roving_design, use_trips = "complete")$method
#> ℹ Using complete trips for CPUE estimation
#>   (n=17, 77.3% of 22 interviews)
#> [1] "ratio-of-means-cpue"

simulate_survey <- function(seed, n_complete, n_incomplete,
                            rate_complete, rate_incomplete,
                            hours_complete, hours_incomplete) {
  set.seed(seed)
  calendar <- data.frame(
    date = seq.Date(as.Date("2024-06-01"), as.Date("2024-06-14"), by = "day"),
    day_type = rep(c("weekday", "weekend"), length.out = 14)
  )
  # Angler-hours for each sampled day
  daily <- data.frame(
    date = calendar$date, day_type = calendar$day_type,
    effort_hours = round(runif(14, min = 50, max = 150))
  )
  trips <- function(n, status, hours, rate) {
    x <- data.frame(
      date = sample(calendar$date, n, replace = TRUE),
      hours_fished = runif(n, hours[1], hours[2]),
      trip_status = status
    )
    x$trip_duration <- x$hours_fished # hours so far, for an incomplete trip
    x$catch_total <- rpois(n, lambda = x$hours_fished * rate)
    x
  }
  interviews <- rbind(
    trips(n_complete, "complete", hours_complete, rate_complete),
    trips(n_incomplete, "incomplete", hours_incomplete, rate_incomplete)
  )
  creel_design(calendar, date = date, strata = day_type) |>
    add_counts(prep_counts_daily_effort(daily,
      date = date, strata = day_type, daily_effort = effort_hours
    )) |>
    add_interviews(interviews,
      catch = catch_total, effort = hours_fished, n_anglers = 1,
      trip_status = trip_status, trip_duration = trip_duration
    )
}

design <- simulate_survey(42,
  n_complete = 50, n_incomplete = 120,
  rate_complete = 2.4, rate_incomplete = 2.35,
  hours_complete = c(2, 8), hours_incomplete = c(0.1, 6)
)
#> ℹ 11 interviews have zero catch.
#>   Zero catch is usual; check it is not missing data recorded as 0.
#> ℹ Added 170 interviews: 50 complete (29%), 120 incomplete (71%)

estimate_catch_rate(design, use_trips = "complete")
#> ℹ Using complete trips for CPUE estimation
#>   (n=50, 29.4% of 170 interviews)
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Ratio-of-Means CPUE
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Unit: fish/angler-hour
#> 
#> # A tibble: 1 × 5
#>   estimate    se ci_lower ci_upper     n
#>      <dbl> <dbl>    <dbl>    <dbl> <int>
#> 1     2.34 0.101     2.14     2.54    50
estimate_catch_rate(design, use_trips = "incomplete", estimator = "mor")
#> ℹ Using incomplete trips for CPUE estimation
#>   (n=120, 70.6% of 170 interviews)
#> ! MOR estimator for incomplete trips. Complete trips preferred.
#> ℹ Using MOR with n=120 incomplete of 120 total interviews.
#> ℹ Incomplete trips may have length-of-stay bias (Pollock et al.).
#> ℹ Validate incomplete estimates with `validate_incomplete_trips()`.
#> ℹ MOR truncation: 5 trips excluded (< 0.5 hours)
#> ── DIAGNOSTIC: MOR Estimator (Incomplete Trips) ────────────────────────────────
#> ! Complete trips preferred for CPUE estimation.
#> Uses incomplete trip interviews only (115 trips).
#> Truncation: 5 trips excluded (< 0.5 hours)
#> Validate with `validate_incomplete_trips()` before use.
#> 
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Mean-of-Ratios CPUE
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Unit: fish/angler-hour
#> 
#> # A tibble: 1 × 5
#>   estimate    se ci_lower ci_upper     n
#>      <dbl> <dbl>    <dbl>    <dbl> <int>
#> 1     2.31 0.114     2.09     2.53   115