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Hazard Proportion

Source: R/hp.R
ssd_hp.Rd

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 class '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 class '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
)

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. Possible values include ci_method = "multi_fixed" and ci_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 value ci_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 value ci_method = "MACL" (was ci_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.

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().

Value

A tibble of corresponding hazard proportions.

Methods (by class)

  • ssd_hp(fitdists): Hazard Proportions for fitdists Object

  • ssd_hp(fitburrlioz): Hazard Proportions for fitburrlioz Object

See also

ssd_hc()

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_hp(fits, conc = 1)
#> Warning: ssd_hp(proportion = FALSE) was deprecated in ssdtools 2.3.1.
#> ℹ Please use ssd_hp(proportion = TRUE) instead.
#> ℹ Please set the `proportion` argument to `ssd_hp()` to be TRUE which will
#>   cause it to return hazard proportions instead of percentages then update your
#>   downstream code accordingly.
#> # A tibble: 1 × 15
#>   dist     conc   est    se   lcl   ucl    wt level est_method ci_method       
#>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>      <chr>           
#> 1 average     1  3.90    NA    NA    NA     1  0.95 multi      weighted_samples
#> # ℹ 5 more variables: boot_method <chr>, nboot <int>, pboot <dbl>,
#> #   dists <list>, samples <list>

fit <- ssd_fit_burrlioz(ssddata::ccme_boron)
ssd_hp(fit)
#> # A tibble: 1 × 15
#>   dist       conc   est    se   lcl   ucl    wt level est_method ci_method 
#>   <chr>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>      <chr>     
#> 1 invpareto     1  8.58    NA    NA    NA     1  0.95 cdf        percentile
#> # ℹ 5 more variables: boot_method <chr>, nboot <int>, pboot <dbl>,
#> #   dists <list>, samples <list>