Assemble pre-computed creel estimates into a report-ready wide tibble

Accepts a named list of pre-computed creel_estimates objects (from estimate_effort(), estimate_catch_rate(), etc.) and joins them into a single wide tibble — one row per stratum with all estimate types as prefixed columns.

Usage

season_summary(estimates, ...)

Arguments

estimates

A named list of creel_estimates objects. Names become column prefixes in the wide tibble (e.g., list(effort = ..., cpue = ...)).

...

Reserved for future arguments.

Value

A creel_season_summary object (S3 list) with:

• $table: A wide tibble — columns prefixed by list element name.

• $names: Character vector of input list element names.

• $n_estimates: Integer count of estimates assembled.

Details

Note: season_summary() performs no re-estimation. All statistical computations must be done before calling this function.

See also

Other "Reporting & Diagnostics": adjust_nonresponse(), check_completeness(), compare_variance(), flag_outliers(), standardize_species(), summarize_boat_composition(), summarize_by_angler_type(), summarize_by_county(), summarize_by_day_type(), summarize_by_method(), summarize_by_species_sought(), summarize_by_trip_length(), summarize_by_zip(), summarize_cws_rates(), summarize_hws_rates(), summarize_length_freq(), summarize_refusals(), summarize_successful_parties(), summarize_trips(), summary.creel_estimates(), tidy.creel_estimates(), validate_creel_data(), validate_design(), validate_incomplete_trips(), validation_report(), write_estimates()

Examples

data(example_calendar)
data(example_counts)
data(example_interviews)

design <- creel_design(example_calendar, date = date, strata = day_type)
design <- add_counts(design, example_counts)
#> Warning: No weights or probabilities supplied, assuming equal probability
design <- add_interviews(design, example_interviews,
  catch = catch_total, effort = hours_fished, harvest = catch_kept,
  trip_status = trip_status
)
#> Warning: ! No `n_anglers` provided — assuming 1 angler per interview.
#> ℹ Pass `n_anglers = <column>` to use actual party sizes for angler-hour
#>   normalization.
#> ℹ If the interviews really are one angler each, pass `n_anglers = 1` to state
#>   that and silence this warning.
#> ℹ Added 22 interviews: 17 complete (77%), 5 incomplete (23%)

result <- season_summary(list(
  effort     = estimate_effort(design),
  catch_rate = estimate_catch_rate(design)
))
#> ℹ Using complete trips for CPUE estimation
#>   (n=17, 77.3% of 22 interviews) [default]
#> Warning: Small sample size for CPUE estimation.
#> ! Sample size is 17. Ratio estimates are more stable with n >= 30.
#> ℹ Variance estimates may be unstable with n < 30.
result$table
#> # A tibble: 1 × 12
#>   effort_estimate effort_se effort_se_between effort_se_within effort_ci_lower
#>             <dbl>     <dbl>             <dbl>            <dbl>           <dbl>
#> 1            372.      13.2              13.2                0            344.
#> # ℹ 7 more variables: effort_ci_upper <dbl>, effort_n <int>,
#> #   catch_rate_estimate <dbl>, catch_rate_se <dbl>, catch_rate_ci_lower <dbl>,
#> #   catch_rate_ci_upper <dbl>, catch_rate_n <int>
result$n_estimates
#> [1] 2