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Audit per-stratum effort precision from a completed creel design or pilot statistics

Source: R/strata-audit.R
audit_strata.Rd

audit_strata() is an S3 generic. Two methods are provided:

  • audit_strata.creel_design() extracts stratum summaries from a completed design object and computes per-stratum RSE, DEFF, and meets-target flag.

  • audit_strata.default() accepts pilot summary statistics (N_h, n_h, ybar_h, s2_h) directly.

Usage

audit_strata(x, ...)

# S3 method for class 'creel_design'
audit_strata(x, rse_target = 0.2, ...)

# Default S3 method
audit_strata(x, n_h, ybar_h, s2_h, rse_target = 0.2, ...)

Arguments

x

A creel_design object (for the creel_design method) or a named numeric vector N_h of total available days per stratum (for the default method).

...

Additional arguments passed to methods.

rse_target

Numeric scalar. Target relative standard error threshold. Default 0.20 (20 percent). Must be in (0, 1].

n_h

Named numeric vector of the same length as x. Observed sample counts per stratum. Values must be >= 1.

ybar_h

Numeric vector of the same length as x. Observed mean effort per day per stratum. Values must be >= 0.

s2_h

Numeric vector of the same length as x. Observed variance of effort per day per stratum. Values must be >= 0.

Value

A creel_strata_audit S3 object. See audit_strata.default() for the complete field description.

A creel_strata_audit S3 object — a named list with fields:

$strata

Tibble with columns: stratum, N_h, n_h, ybar_h, s2_h, RSE, DEFF, meets_target.

$rse_target

Scalar. The RSE threshold supplied by the caller.

$n_total

Integer. Total sampled days across all strata.

$deff

Scalar. Aggregate design effect (Var_strat / Var_SRS).

Details

The per-stratum RSE (relative standard error, equivalent to CV) is computed with the finite-population correction (FPC):

RSE_h = sqrt((1 - n_h / N_h) * s2_h / n_h) / ybar_h

When n_h = 1 for any stratum, var() cannot be estimated; RSE, DEFF, and meets_target are set to NA for those strata and a warning is issued. The function continues processing valid strata.

The per-stratum design effect (DEFF_h) compares the actual stratum variance to the pooled-SRS variance baseline:

DEFF_h = ((1 - n_h/N_h) * s2_h / n_h) / ((1 - n/N) * s2_overall / n)

where n = sum(n_h), N = sum(N_h), and s2_overall = sum(N_h * s2_h) / sum(N_h) (N_h-weighted pooled within-stratum variance). The aggregate DEFF stored in $deff is Var_strat / Var_SRS (Cochran 1977).

References

Cochran, W.G. 1977. Sampling Techniques, 3rd ed. Wiley, New York.

McCormick, J.L. and Quist, M.C. 2017. Sample size estimation for on-site creel surveys. North American Journal of Fisheries Management 37:970-983. doi:10.1080/02755947.2017.1342723

See also

Other "Planning & Sample Size": compare_designs(), creel_n_camera(), creel_n_cpue(), creel_n_effort(), creel_power(), cv_from_n(), power_creel(), reallocate_strata(), simulate_strata_collapse()

Other "Planning & Sample Size": compare_designs(), creel_n_camera(), creel_n_cpue(), creel_n_effort(), creel_power(), cv_from_n(), power_creel(), reallocate_strata(), simulate_strata_collapse()

Examples

# Two-stratum weekday/weekend pilot example
audit <- audit_strata(
  c(weekday = 65, weekend = 28),
  n_h    = c(weekday = 22, weekend = 14),
  ybar_h = c(50, 60),
  s2_h   = c(400, 500),
  rse_target = 0.20
)
audit$strata
#> # A tibble: 2 × 8
#>   stratum   N_h   n_h ybar_h  s2_h    RSE  DEFF meets_target
#>   <chr>   <int> <int>  <dbl> <dbl>  <dbl> <dbl> <lgl>       
#> 1 weekday    65    22     50   400 0.0694  1.64 TRUE        
#> 2 weekend    28    14     60   500 0.0704  2.44 TRUE        
audit$deff
#> [1] 1.023445