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 = TRUE,
  ci_method = "weighted_samples",
  parametric = TRUE,
  delta = 9.21,
  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,
  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 treat the distributions as constituting a single distribution (as opposed to taking the mean) when calculating model averaged estimates.
ci_method: A string specifying which method to use for estimating the bootstrap values. Possible values are "multi_free" and "multi_fixed" which treat the distributions as constituting a single distribution but differ in whether the model weights are fixed and "weighted_samples" and "weighted_arithmetic" take bootstrap samples from each distribution proportional to its weight versus calculating the weighted arithmetic means of the lower and upper confidence limits.
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.
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)