predict.fitdists: Predict Hazard Concentrations of fitdists Object

In ssdtools: Species Sensitivity Distributions

Predict Hazard Concentrations of fitdists Object

Description

A wrapper on ssd_hc() that by default calculates all hazard concentrations from 1 to 99%.

Usage

## S3 method for class 'fitdists'
predict(
  object,
  percent,
  proportion = 1:99/100,
  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,
  control = NULL,
  ...
)

Arguments

object	The object.
percent	A numeric vector of percent values to estimate hazard concentrations for. Deprecated for proportion = 0.05.
proportion	A numeric vector of proportion values to estimate hazard concentrations 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.
control	A list of control parameters passed to stats::optim().
...	Unused.

Details

It is useful for plotting purposes.

See Also

ssd_hc() and ssd_plot()

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
predict(fits)

ssdtools documentation built on April 4, 2025, 12:35 a.m.