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Estimate a weighted length distribution from creel interview data

Source: R/creel-estimates-length.R
est_length_distribution.Rd

est_length_distribution() estimates a pressure-weighted length-frequency distribution from fish length data attached via add_lengths(). Unlike summarize_length_freq(), which reports raw sample frequencies, est_length_distribution() aggregates interview-level bin counts through the internal interview survey design so the result reflects the survey design rather than only the observed sample.

The estimator returns one row per occupied length bin, with weighted totals, standard errors, confidence intervals, and within-group percentages.

Usage

est_length_distribution(
  design,
  type = "catch",
  by = NULL,
  bin_width = 1,
  length_col = NULL,
  variance = "taylor",
  conf_level = 0.95
)

Arguments

design

A creel_design object with interviews and lengths attached.

type

Character string indicating which fish to include. One of "catch" (default), "harvest", or "release".

by

Optional tidy selector evaluated against design$lengths. Common choices include by = species.

bin_width

Positive numeric bin width in the same units as the attached length data. Default 1.

length_col

Optional character column name in design$lengths to use for the length values. Defaults to the column registered by add_lengths().

variance

Character string specifying variance estimation method. One of "taylor" (default), "bootstrap", or "jackknife".

conf_level

Numeric confidence level for confidence intervals. Default 0.95.

Value

A data.frame with class c("creel_length_distribution", "data.frame") and columns: grouping columns (if any), length_bin (ordered factor), bin_lower, bin_upper, estimate, se, ci_lower, ci_upper, percent, cumulative_percent, and n.

See also

Other "Estimation": estimate_catch_rate(), estimate_effort(), estimate_effort_aerial_glmm(), estimate_harvest_rate(), estimate_release_rate(), estimate_total_catch(), estimate_total_harvest(), estimate_total_release()

Examples

data(example_calendar)
data(example_interviews)
data(example_lengths)

design <- creel_design(example_calendar, date = date, strata = day_type)
design <- add_interviews(design, example_interviews,
  catch = catch_total, effort = hours_fished, harvest = catch_kept,
  trip_status = trip_status
)
#> 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%)
design <- add_lengths(design, example_lengths,
  length_uid = interview_id,
  interview_uid = interview_id,
  species = species,
  length = length,
  length_type = length_type,
  count = count,
  release_format = "binned"
)

est_length_distribution(design, by = species, bin_width = 25)
#>    species length_bin bin_lower bin_upper estimate       se   ci_lower
#> 1     bass  [225,250)       225       250        1 1.000000 -0.9599640
#> 2     bass  [250,275)       250       275        1 1.000000 -0.9599640
#> 3     bass  [275,300)       275       300        6 4.213075 -2.2574750
#> 4     bass  [325,350)       325       350        5 5.000000 -4.7998199
#> 5  panfish  [150,175)       150       175        2 2.000000 -1.9199280
#> 6  panfish  [175,200)       175       200        6 6.000000 -5.7597839
#> 7  panfish  [225,250)       225       250        3 3.000000 -2.8798920
#> 8  walleye  [375,400)       375       400        4 2.380476 -0.6656475
#> 9  walleye  [400,425)       400       425        3 2.236068 -1.3826127
#> 10 walleye  [425,450)       425       450        3 3.000000 -2.8798920
#> 11 walleye  [475,500)       475       500        1 1.000000 -0.9599640
#> 12 walleye  [500,525)       500       525        2 2.000000 -1.9199280
#>     ci_upper percent cumulative_percent  n
#> 1   2.959964     7.7                7.7 22
#> 2   2.959964     7.7               15.4 22
#> 3  14.257475    46.2               61.6 22
#> 4  14.799820    38.5              100.1 22
#> 5   5.919928    18.2               18.2 22
#> 6  17.759784    54.5               72.7 22
#> 7   8.879892    27.3              100.0 22
#> 8   8.665648    30.8               30.8 22
#> 9   7.382613    23.1               53.9 22
#> 10  8.879892    23.1               77.0 22
#> 11  2.959964     7.7               84.7 22
#> 12  5.919928    15.4              100.1 22