Summarise creel survey estimates as a formatted table

summary.creel_estimates() converts a creel_estimates object into a creel_summary table with human-readable column names, suitable for display or export.

Usage

# S3 method for class 'creel_estimates'
summary(object, digits = 4L, ...)

Arguments

object

A creel_estimates object returned by estimate_effort(), estimate_catch_rate(), estimate_harvest_rate(), estimate_total_catch(), or estimate_total_harvest().

digits

Integer number of significant digits for numeric columns (default: 4).

...

Additional arguments (currently ignored).

Value

A creel_summary S3 object (a list) with components:

table

A data.frame with columns: any grouping variables, Estimate, SE, CI Lower, CI Upper, N.

method

Character string — the estimation method.

variance_method

Character string — the variance method.

conf_level

Numeric confidence level (e.g. 0.95).

See also

estimate_effort(), estimate_catch_rate(), estimate_harvest_rate()

Other "Reporting & Diagnostics": adjust_nonresponse(), as_tibble.creel_estimates(), check_completeness(), compare_variance(), flag_outliers(), season_summary(), 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(), 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)
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%)

est <- estimate_effort(design)
#> Warning: Instantaneous counts were expanded without a period length.
#> ℹ No `period_length_col` was supplied to `add_counts()`, so the estimate is the
#>   count column summed over days.
#> ! If that column holds an instantaneous angler count, the result is in
#>   angler-days, not angler-hours.
#> ℹ Supply the period each count was randomised within: `add_counts(design,
#>   counts, period_length_col = <col>)`.
#> ℹ Repeats on this design are recorded, not printed: see `get_warnings()`.
summary(est)
#> -- Creel Survey Summary (Total | Taylor linearization | 95%) --
#>  Estimate    SE CI Lower CI Upper  N
#>     372.5 13.18    343.8    401.2 14
as.data.frame(summary(est))
#>   Estimate    SE CI Lower CI Upper  N
#> 1    372.5 13.18    343.8    401.2 14