Calculates concentration(s) with bootstrap confidence intervals that protect specified proportion(s) of species for individual or model-averaged distributions using parametric or non-parametric bootstrapping.
Usage
ssd_hc(x, ...)
# S3 method for class 'list'
ssd_hc(x, percent, proportion = 0.05, ...)
# S3 method for class 'fitdists'
ssd_hc(
x,
percent = deprecated(),
proportion = 0.05,
...,
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,
samples = FALSE,
save_to = NULL,
control = NULL
)
# S3 method for class 'fitburrlioz'
ssd_hc(
x,
percent,
proportion = 0.05,
...,
ci = FALSE,
level = 0.95,
nboot = 1000,
min_pboot = 0.95,
parametric = FALSE,
samples = FALSE,
save_to = NULL
)Arguments
- x
The object.
- ...
Unused.
- percent
A numeric vector of percent values to estimate hazard concentrations for. Deprecated for
proportion = 0.05.![[Deprecated]](figures/lifecycle-deprecated.svg)
- 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 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. Possible values include
ci_method = "multi_fixed"andci_method = "multi_free"which 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 valueci_method = "weighted_samples"takes bootstrap samples from each distribution proportional to its AICc based weights and calculates the confidence limits (and SE) from this single set. The valueci_method = "MACL"(wasci_method = "weighted_arithmetic"but has been soft-deprecated) which is only included for historical reasons takes the weighted arithmetic mean of the 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().
Details
Model-averaged estimates and/or confidence intervals (including standard error)
can be calculated by treating the distributions as
constituting a single mixture distribution
versus 'taking the mean'.
When calculating the model averaged estimates treating the
distributions as constituting a single mixture distribution
ensures that ssd_hc() is the inverse of ssd_hp().
Distributions with an absolute AIC difference greater than a delta of by default 7 have considerably less support (wt < 0.01) and are excluded prior to calculation of the hazard concentrations to reduce the run time.
Methods (by class)
ssd_hc(list): Hazard Concentrations for Distributional Estimatesssd_hc(fitdists): Hazard Concentrations for fitdists Objectssd_hc(fitburrlioz): Hazard Concentrations for fitburrlioz Object
References
Burnham, K.P., and Anderson, D.R. 2002. Model Selection and Multimodel Inference. Springer New York, New York, NY. doi:10.1007/b97636.
Examples
ssd_hc(ssd_match_moments())
#> # A tibble: 6 × 9
#> dist proportion est se lcl ucl wt nboot pboot
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 gamma 0.05 0.439 NA NA NA 1 0 NA
#> 2 lgumbel 0.05 0.739 NA NA NA 1 0 NA
#> 3 llogis 0.05 0.562 NA NA NA 1 0 NA
#> 4 lnorm 0.05 0.558 NA NA NA 1 0 NA
#> 5 lnorm_lnorm 0.05 0.489 NA NA NA 1 0 NA
#> 6 weibull 0.05 0.501 NA NA NA 1 0 NA
fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_hc(fits)
#> # A tibble: 1 × 15
#> dist proportion est se lcl ucl wt level est_method ci_method
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 average 0.05 1.26 NA NA NA 1 0.95 multi weighted_sa…
#> # ℹ 5 more variables: boot_method <chr>, nboot <int>, pboot <dbl>,
#> # dists <list>, samples <list>
fit <- ssd_fit_burrlioz(ssddata::ccme_boron)
ssd_hc(fit)
#> # A tibble: 1 × 15
#> dist proportion est se lcl ucl wt level est_method ci_method
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 invpareto 0.05 0.387 NA NA NA 1 0.95 cdf percentile
#> # ℹ 5 more variables: boot_method <chr>, nboot <int>, pboot <dbl>,
#> # dists <list>, samples <list>