Roving interviews from the 36 sampled days in example_season_calendar,
one row per angling party. A roving clerk meets parties while they fish, so
every trip is incomplete: hours_fished and catch_total are the hours
fished and the fish caught so far. Pair with example_season_counts for
effort.
Format
A data frame with 212 rows and 8 columns:
- date
Interview date (Date class), a sampled day in the calendar
- day_type
Day type stratum: "weekday" or "weekend"
- interview_id
Integer interview identifier, 1 to 212
- trip_status
Always "incomplete"
- hours_fished
Hours the party had fished when interviewed
- n_anglers
Integer number of anglers in the party
- catch_total
Integer fish caught so far, kept and released
- catch_kept
Integer fish kept so far, never more than
catch_total
Details
The parties were simulated so that the true catch rate is known: 0.22 fish per angler-hour. A party's chance of being met is proportional to its trip length, it is met at a random point in its trip, and its catch so far accrues at the true rate over the time fished so far. Parties that had fished less than 15 minutes were not interviewed.
See also
example_season_interviews for access interviews on the same days,
vignette("instantaneous-counts")
Other "Example Datasets":
creel_counts_toy,
creel_interviews_toy,
example_aerial_counts,
example_aerial_glmm_counts,
example_aerial_interviews,
example_ages,
example_calendar,
example_camera_counts,
example_camera_interviews,
example_camera_timestamps,
example_catch,
example_counts,
example_ice_interviews,
example_ice_sampling_frame,
example_interviews,
example_lengths,
example_season_calendar,
example_season_counts,
example_season_interviews,
example_sections_calendar,
example_sections_counts,
example_sections_interviews
Examples
data(example_season_calendar)
data(example_season_counts)
data(example_roving_interviews)
design <- creel_design(example_season_calendar, date = date, strata = day_type)
design <- add_counts(design, example_season_counts,
count_col = total_anglers, period_length_col = daylight_hours
)
design <- add_interviews(design, example_roving_interviews,
catch = catch_total, harvest = catch_kept, effort = hours_fished,
trip_status = trip_status, trip_duration = hours_fished,
n_anglers = n_anglers, interview_type = "roving"
)
#> ℹ 133 interviews have zero catch.
#> Zero catch is usual; check it is not missing data recorded as 0.
#> ℹ Added 212 interviews: 0 complete (0%), 212 incomplete (100%)
estimate_catch_rate(design)
#> ℹ Roving design: using all 212 interviews for CPUE via MOR [auto]
#> (0 complete, 212 incomplete)
#> Override with `use_trips = 'complete'` for access-point estimation.
#> Warning: ! MOR truncation: 29 trips excluded (13.7%)
#> ℹ Trips < 0.5 hours excluded to prevent unstable variance
#> ! High truncation rate may indicate data quality issues
#> ℹ Consider reviewing trip duration data for errors
#> ℹ Repeats on this design are recorded, not printed: see `get_warnings()`.
#> ── MOR Estimator (All Trips) ───────────────────────────────────────────────────
#> Averages per-trip CPUE ratios over 183 interviews (183 incomplete).
#> Truncation: 29 trips excluded (< 0.5 hours)
#>
#>
#> ── 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 0.191 0.0193 0.154 0.229 183