One instantaneous angler count on each of the 36 sampled days in
example_season_calendar. Each count stands for the whole 14-hour fishing
day, so daylight_hours is the length of the period it represents. Pass it
to add_counts() as period_length_col: effort is then the count times the
period length, in angler-hours.
Format
A data frame with 36 rows and 4 columns:
- date
Survey date (Date class), a sampled day in the calendar
- day_type
Day type stratum: "weekday" or "weekend"
- total_anglers
Integer number of anglers counted
- daylight_hours
Hours in the fishing day the count represents (14)
Source
Simulated with simulate_creel_data() and a fixed seed; see
data-raw/example_season.R in the package source.
See also
example_season_calendar, example_season_interviews
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_roving_interviews,
example_season_calendar,
example_season_interviews,
example_sections_calendar,
example_sections_counts,
example_sections_interviews
Examples
data(example_season_calendar)
data(example_season_counts)
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
)
estimate_effort(design, target = "period_total")
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: period_total
#> Unit: angler-hours
#>
#> # A tibble: 1 × 7
#> estimate se se_between se_within ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 9994. 1286. 1286. 0 7380. 12609. 36