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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 class 'fitdists'
predict(
  object,
  percent,
  proportion = 1:99/100,
  ...,
  average = TRUE,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  est_method = "multi",
  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. [Deprecated]

proportion

A numeric vector of proportion values to estimate hazard concentrations for.

...

Unused.

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.

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.

control

A list of control parameters passed to stats::optim().

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 × 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       0.01 0.267    NA    NA    NA     1  0.95 multi      weighted_s…
#>  2 average       0.02 0.531    NA    NA    NA     1  0.95 multi      weighted_s…
#>  3 average       0.03 0.783    NA    NA    NA     1  0.95 multi      weighted_s…
#>  4 average       0.04 1.02     NA    NA    NA     1  0.95 multi      weighted_s…
#>  5 average       0.05 1.26     NA    NA    NA     1  0.95 multi      weighted_s…
#>  6 average       0.06 1.48     NA    NA    NA     1  0.95 multi      weighted_s…
#>  7 average       0.07 1.71     NA    NA    NA     1  0.95 multi      weighted_s…
#>  8 average       0.08 1.93     NA    NA    NA     1  0.95 multi      weighted_s…
#>  9 average       0.09 2.16     NA    NA    NA     1  0.95 multi      weighted_s…
#> 10 average       0.1  2.38     NA    NA    NA     1  0.95 multi      weighted_s…
#> # ℹ 89 more rows
#> # ℹ 5 more variables: boot_method <chr>, nboot <int>, pboot <dbl>,
#> #   dists <list>, samples <list>