ssd_hp: Hazard Proportion

Description

Calculates proportion of species affected at specified concentration(s) with quantile based bootstrap confidence intervals for individual or model-averaged distributions using parametric or non-parametric bootstrapping. For more information see the inverse function ssd_hc().

Usage

ssd_hp(x, ...)
# S3 method for fitdists
ssd_hp(
  x,
  conc = 1,
  ...,
  average = TRUE,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  multi_est = deprecated(),
  est_method = "multi",
  ci_method = "weighted_samples",
  parametric = TRUE,
  delta = 9.21,
  proportion = FALSE,
  samples = FALSE,
  save_to = NULL,
  control = NULL
)
# S3 method for fitburrlioz
ssd_hp(
  x,
  conc = 1,
  ...,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  parametric = FALSE,
  proportion = FALSE,
  samples = FALSE,
  save_to = NULL
)

Value

A tibble of corresponding hazard proportions.

Arguments

x: The object.
...: Unused.
conc: A numeric vector of concentrations to calculate the hazard proportions for.
average: A flag specifying whether to provide model averaged values as opposed to a value for each distribution.
ci: A flag specifying whether to estimate confidence intervals (by bootstrapping).
level: A number between 0 and 1 of the confidence level of the interval.
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.
min_pboot: A number between 0 and 1 of the minimum proportion of bootstrap samples that must successfully fit (return a likelihood) to report the confidence intervals.
multi_est: A flag specifying whether to estimate directly from the model-averaged cumulative distribution function (multi_est = TRUE) or to take the arithmetic mean of the estimates from the individual cumulative distribution functions weighted by the AICc derived weights (multi_est = FALSE).
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.
delta: A non-negative number specifying the maximum absolute AIC difference cutoff. Distributions with an absolute AIC difference greater than delta are excluded from the calculations.
proportion: A flag specifying whether to return hazard proportions (proportion = TRUE) or hazard percentages (proportion = FALSE). To not break existing code the default value is FALSE but will be switching the default to TRUE in a future version. The user is recommended to manually set to TRUE now to avoid unexpected changes in future versions.
samples: A flag specfying whether to include a numeric vector of the bootstrap samples as a list column in the output.
save_to: NULL or a string specifying a directory to save where the bootstrap datasets and parameter estimates (when successfully converged) to.
control: A list of control parameters passed to stats::optim().

Methods (by class)

ssd_hp(fitdists): Hazard Proportions for fitdists Object
ssd_hp(fitburrlioz): Hazard Proportions for fitburrlioz Object

Examples

Run this code

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_hp(fits, conc = 1)

fit <- ssd_fit_burrlioz(ssddata::ccme_boron)
ssd_hp(fit)