example_output

A dataframe in the format output from running run_sims. This exists
solely for the purpose of making documentation of visualization functions
easier.<ul>
<li>parameter. Parameters (lambda, psi, and theta for a two species assemblage).</li>
<li>Mean. The posterior mean.</li>
<li>SD. The standard deviation of the draws of reach parameter.</li>
<li>Naive SE. The standard error, not accounting for the correlation in draws</li>
<li>Time-series SE. The standard error, accounting for correlation in draws.</li>
<li>quantiles. 2.5%, 25%, 50%, 75% and 97.5% quantiles.</li>
<li>Rhat. The Gelman-Rubin statistic for each parameter.</li>
<li>ess_bulk. The effective sample size in the bulk of the distribution.</li>
<li>ess_tail. The effective sample size in the tails of the distribution.</li>
<li>truth. The known true data generating value.</li>
<li>capture. Did the 95% posterior interval contain the true value?</li>
<li>converge. Was the Gelman-Rubin statistic near 1?</li>
<li>theta_scenario. The ID for the classifier scenario.</li>
<li>scenario. The index of the validation scenario.</li>
<li>dataset. The dataset index.</li>
</ul>

Many bioacoustic data workflows rely on manual review (i.e.,
validation) of a subset of call files to provide information to statistical
models that account for misclassification by automated algorithms.
Because manual review can be prohibitively expensive, simulation can be a
valuable tool to aid the design of studies that use validation. This package
provides user-friendly functions to reduce the programming burden of
simulation studies that compare validation sampling designs. Simulations
assume the count-detection model, which is a realistic model for bioacoustic
data, especially for bats. For more information, see Oram et al. (2025)
<doi:10.1214/25-AOAS2096>.

example_output: example_output

Description

Arguments

Format

Examples