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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 fitdists
ssd_hp(
  x,
  conc = 1,
  average = TRUE,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  multi_est = TRUE,
  multi_ci = TRUE,
  weighted = TRUE,
  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,
  ...
)

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.

multi_ci

A flag specifying whether to treat the distributions as constituting a single distribution which is now the recommended approach (as opposed to taking the mean) when calculating model averaged confidence intervals.

weighted

A flag which specifies whether to use the original model weights (as opposed to re-estimating for each bootstrap sample) unless multi_ci = FALSE in which case it specifies whether to 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().

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)
#> # A tibble: 1 × 11
#>   dist     conc   est    se   lcl   ucl    wt method     nboot pboot samples  
#>   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>      <int> <dbl> <I<list>>
#> 1 average     1  3.90    NA    NA    NA     1 parametric     0    NA <dbl [0]>

fit <- ssd_fit_burrlioz(ssddata::ccme_boron)
ssd_hp(fit)
#> # A tibble: 1 × 11
#>   dist       conc   est    se   lcl   ucl    wt method       nboot pboot samples
#>   <chr>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>        <int> <dbl> <I<lis>
#> 1 invpareto     1  8.58    NA    NA    NA     1 non-paramet…     0    NA <dbl>