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Predict Hazard Concentrations of fitdists Object

Source: R/predict.R
predict.fitdists.Rd

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

Usage

# S3 method for 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. Soft-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)
#> # A tibble: 99 × 11
#>    dist    proportion   est    se   lcl   ucl    wt method   nboot pboot samples
#>    <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>    <int> <dbl> <I<lis>
#>  1 average       0.01 0.267    NA    NA    NA     1 paramet…     0   NaN <dbl>  
#>  2 average       0.02 0.531    NA    NA    NA     1 paramet…     0   NaN <dbl>  
#>  3 average       0.03 0.783    NA    NA    NA     1 paramet…     0   NaN <dbl>  
#>  4 average       0.04 1.02     NA    NA    NA     1 paramet…     0   NaN <dbl>  
#>  5 average       0.05 1.26     NA    NA    NA     1 paramet…     0   NaN <dbl>  
#>  6 average       0.06 1.48     NA    NA    NA     1 paramet…     0   NaN <dbl>  
#>  7 average       0.07 1.71     NA    NA    NA     1 paramet…     0   NaN <dbl>  
#>  8 average       0.08 1.93     NA    NA    NA     1 paramet…     0   NaN <dbl>  
#>  9 average       0.09 2.16     NA    NA    NA     1 paramet…     0   NaN <dbl>  
#> 10 average       0.1  2.38     NA    NA    NA     1 paramet…     0   NaN <dbl>  
#> # ℹ 89 more rows