Example interview data for spatially stratified creel survey
Source:R/data.R
example_sections_interviews.RdAngler interview data for a 3-section lake (North, Central, South) with
9 interviews per section (27 total). Catch rates differ materially across
sections: South has approximately 2.5x the catch rate of North, making this
dataset suitable for demonstrating spatially stratified estimation. The
catch_kept column enables estimate_total_harvest() in addition to
estimate_catch_rate() and estimate_total_catch().
Format
A data frame with 27 rows and 9 columns:
- date
Interview date (Date class), matching example_sections_calendar
- day_type
Day type stratum:
"weekday"or"weekend"- section
Section identifier:
"North","Central", or"South"- catch_total
Integer total fish caught per interview
- catch_kept
Integer fish harvested (kept); always
<= catch_total- hours_fished
Numeric fishing effort in hours
- trip_status
Character trip completion status;
"complete"for all 27 interviews- trip_duration
Numeric trip duration in hours
- interview_id
Integer interview identifier (1 to 27)
See also
example_sections_calendar, example_sections_counts,
add_interviews(), estimate_catch_rate(), estimate_total_catch()
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_counts,
example_season_interviews,
example_sections_calendar,
example_sections_counts
Examples
data(example_sections_calendar)
data(example_sections_counts)
data(example_sections_interviews)
sections_df <- data.frame(
section = c("North", "Central", "South"),
stringsAsFactors = FALSE
)
design <- creel_design(example_sections_calendar, date = date, strata = day_type)
design <- add_sections(design, sections_df, section_col = section)
# effort_hours is already effort per section-day: mark it as such
counts <- prep_counts_daily_effort(example_sections_counts,
date = date, strata = day_type, daily_effort = effort_hours,
section = section
)
design <- add_counts(design, counts)
design <- suppressWarnings(add_interviews(design, example_sections_interviews,
catch = catch_total, effort = hours_fished,
harvest = catch_kept,
trip_status = trip_status, trip_duration = trip_duration
))
#> ℹ Added 27 interviews: 27 complete (100%), 0 incomplete (0%)
estimate_total_catch(design, aggregate_sections = TRUE)
#> Warning: Rate and effort may be in different units.
#> ✖ `n_anglers` was not supplied, so the rate is per party-hour while
#> count-derived effort is per angler.
#> ℹ The product is correct only if every party is one angler. Pass
#> `add_interviews(n_anglers = <col>)` to normalise.
#> Warning: 2 rate cells in "estimate_total_catch" rests on fewer than 10 trips.
#> • section=North, day_type=weekday: n=6
#> • section=North, day_type=weekend: n=3
#> ℹ The total multiplies each cell's effort by a rate built from that cell's
#> trips alone; a ratio from so few trips is unstable, and the rate functions
#> refuse a group this small.
#> ℹ Combine cells or collect more interviews. Totals warn here rather than refuse
#> (GH #417).
#> ℹ Repeats on this design are recorded, not printed: see `get_warnings()`.
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: product-total-catch-sections
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Grouped by: section
#> Effort target: sampled_days
#>
#> # A tibble: 4 × 9
#> section estimate se ci_lower ci_upper n prop_of_lake_total
#> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 North 285. 20.3 237. 333. 9 0.226
#> 2 Central 717. 30.1 646. 788. 9 0.569
#> 3 South 259. 22.8 205. 313. 9 0.205
#> 4 .lake_total 1261. 64.8 1126. 1396. 3 1
#> # ℹ 2 more variables: se_prop_of_lake_total <dbl>, data_available <lgl>