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Compute caught-while-sought (CWS) rates by group

Source: R/creel-summaries.R
summarize_cws_rates.Rd

Computes mean caught-while-sought rates (fish per angler-hour) for anglers targeting each species. For each interview, the rate is: caught_count / angler_effort where caught_count is the total number of fish caught of the species the angler was seeking, and angler_effort is angler-hours (effort x n_anglers, standardized at design time by add_interviews).

Usage

summarize_cws_rates(design, by = NULL, conf_level = 0.95)

Arguments

design

A creel_design object with interviews attached via add_interviews (with species_sought) and species catch data attached via add_catch.

by

Optional tidy selector for grouping columns from design$interviews. Common choices: by = species_sought (CWS-03), by = c(month, species_sought) (CWS-02), by = c(month, angler_type, species_sought) (CWS-01). When NULL, returns a single overall rate across all interviews.

conf_level

Numeric confidence level for the t-interval. Default 0.95.

Value

A data.frame with class c("creel_summary_cws_rates", "data.frame") and columns: grouping columns (if any), N (integer, interviews per group), mean_rate (numeric, mean fish/angler-hour), se (numeric, standard error), ci_lower, ci_upper.

Details

Interview-based summary, not pressure-weighted. This function computes a simple arithmetic mean over sampled interviews. It does NOT apply survey weighting by sampling effort or effort stratum. For pressure-weighted extrapolated estimates use estimate_catch_rate.

The catch filter ensures only species the angler was targeting are counted (i.e., rows in design$catch where catch_type == "caught" and species == species_sought).

See also

summarize_hws_rates(), estimate_catch_rate()

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

Examples

data(example_calendar)
data(example_interviews)
data(example_catch)
d <- creel_design(example_calendar, date = date, strata = day_type)
d <- add_interviews(d, example_interviews,
  catch = catch_total, effort = hours_fished, harvest = catch_kept,
  trip_status = trip_status, species_sought = species_sought
)
#> ℹ No `n_anglers` provided — assuming 1 angler per interview.
#> ℹ Pass `n_anglers = <column>` to use actual party sizes for angler-hour
#>   normalization.
#> ℹ Added 22 interviews: 17 complete (77%), 5 incomplete (23%)
d <- add_catch(d, example_catch,
  catch_uid = interview_id, interview_uid = interview_id,
  species = species, count = count, catch_type = catch_type
)
summarize_cws_rates(d, by = species_sought)
#>   species_sought  N mean_rate        se    ci_lower  ci_upper
#> 1           bass  6 0.2083333 0.2083333 -0.32720455 0.7438712
#> 2        panfish  5 0.4666667 0.4666667 -0.82900772 1.7623410
#> 3        walleye 11 0.7835498 0.3359583  0.03498793 1.5321116