Estimate hazard concentrations for multiple simulations using bootstrapping

Usage

ssd_hc_sims(
  x,
  proportion = 0.05,
  ...,
  ci = FALSE,
  nboot = 1000,
  est_method = "multi",
  ci_method = "weighted_samples",
  parametric = TRUE,
  seed = NULL,
  save_to = NULL,
  .progress = FALSE
)

Arguments

x: A data frame with sim and stream integer columns and a list column of fitdists objects.
proportion: A numeric vector of proportion values to estimate hazard concentrations for.
...: Additional arguments passed to ssdtools::ssd_hc().
ci: A flag specifying whether to estimate confidence intervals (by bootstrapping).
nboot: A count of the number of bootstrap samples to use to estimate the confidence limits. A value of 10,000 is recommended for official guidelines.
est_method: A string specifying whether to estimate directly from the model-averaged cumulative distribution function (est_method = 'multi') or to take the arithmetic mean of the estimates from the individual cumulative distribution functions weighted by the AICc derived weights (est_method = 'arithmetic') or or to use the geometric mean instead (est_method = 'geometric').
ci_method: A string specifying which method to use for estimating the standard error and confidence limits from the bootstrap samples. The default and recommended value is still ci_method = "weighted_samples" which takes bootstrap samples from each distribution proportional to its AICc based weights and calculates the confidence limits (and SE) from this single set. ci_method = "multi_fixed" and ci_method = "multi_free" generate the bootstrap samples using the model-averaged cumulative distribution function but differ in whether the model weights are fixed at the values for the original dataset or re-estimated for each bootstrap sample dataset. The value ci_method = "MACL" (was ci_method = "weighted_arithmetic"), which is only included for historical reasons, takes the weighted arithmetic mean of the confidence limits while ci_method = GMACL which takes the weighted geometric mean of the confidence limits was added for completeness but is also not recommended. Finally ci_method = "arithmetic_samples" and ci_method = "geometric_samples" take the weighted arithmetic or geometric mean of the values for each bootstrap iteration across all the distributions and then calculate the confidence limits (and SE) from the single set of samples.
parametric: A flag specifying whether to perform parametric bootstrapping as opposed to non-parametrically resampling the original data with replacement.
seed: An integer of the starting seed or NULL.
save_to: NULL or a string specifying a directory to save where the bootstrap datasets and parameter estimates (when successfully converged) to.
.progress: Whether to show a purrr::progress bar.

Value

The x tibble with a list column hc of data frames produced by applying ssd_hc() to fits.