Example effort counts for spatially stratified creel survey

Instantaneous count observations for a 3-section lake (North, Central, South) covering 12 survey dates. Each section has one count row per date (36 rows total). Effort varies materially by section: Central has the highest angler traffic, South the lowest. Use with add_sections() and add_counts().

Usage

example_sections_counts

Format

A data frame with 36 rows and 4 columns:

date

Survey date (Date class), matching example_sections_calendar

day_type

Day type stratum: "weekday" or "weekend"

section

Section identifier: "North", "Central", or "South"

effort_hours

Numeric instantaneous count of angler-hours observed

Source

Simulated data for package examples

See also

example_sections_calendar, example_sections_interviews, add_counts(), add_sections(), estimate_effort()

Other "Example Datasets": creel_counts_toy, creel_interviews_toy, example_aerial_counts, example_aerial_glmm_counts, example_aerial_interviews, 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_sections_calendar, example_sections_interviews

Examples

data(example_sections_calendar)
data(example_sections_counts)

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)
design <- suppressWarnings(add_counts(design, example_sections_counts))
estimate_effort(design)
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: total-sections
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: sampled_days
#> 
#> # A tibble: 4 × 10
#>   section     estimate    se se_between se_within ci_lower ci_upper     n
#>   <chr>          <dbl> <dbl>      <dbl>     <dbl>    <dbl>    <dbl> <int>
#> 1 North            269 12.3       12.3          0    242.      296.    12
#> 2 Central          472 19.0       19.0          0    430.      514.    12
#> 3 South            105  9.18       9.18         0     84.6     125.    12
#> 4 .lake_total      846 39.4       NA           NA    758.      934.    36
#> # ℹ 2 more variables: prop_of_lake_total <dbl>, data_available <lgl>