search

bcgov / ssdtools

An R package to fit and plot Species Sensitivity Distributions (SSDs)
https://bcgov.github.io/ssdtools/
Apache License 2.0
31 stars 17 forks source link
ecotoxicology env rstats species-sensitivity-distribution

readme

ssdtools ssdtools logo of cumulative species sensitivity distribution with outlines of commonly used species as data

Lifecycle: stable R-CMD-check Codecov test coverage CRAN status CRAN downloads

ssdtools is an R package to fit and plot Species Sensitivity Distributions (SSD).

SSDs are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al. (2001). The ssdtools package uses Maximum Likelihood to fit distributions such as the log-normal, log-logistic, log-Gumbel (also known as the inverse Weibull), gamma, Weibull and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.

Installation

Release

To install the latest release version from CRAN.

install.packages("ssdtools")

Website

The website for the release version is at https://bcgov.github.io/ssdtools/.

Development

To install the latest development version from r-universe.

install.packages("ssdtools", repos = c("https://bcgov.r-universe.dev", "https://cloud.r-project.org"))

or from GitHub

# install.packages("pak", repos = sprintf("https://r-lib.github.io/p/pak/stable/%s/%s/%s", .Platform$pkgType, R.Version()$os, R.Version()$arch))
pak::pak("bcgov/ssdtools")

Website

The website for the development version is at https://bcgov.github.io/ssdtools/dev/.

Introduction

The dependency ssddata provides example data sets for several chemicals including Boron.

library(ssdtools)
ssddata::ccme_boron
#> # A tibble: 28 × 5
#>    Chemical Species                  Conc Group        Units
#>    <chr>    <chr>                   <dbl> <fct>        <chr>
#>  1 Boron    Oncorhynchus mykiss       2.1 Fish         mg/L 
#>  2 Boron    Ictalurus punctatus       2.4 Fish         mg/L 
#>  3 Boron    Micropterus salmoides     4.1 Fish         mg/L 
#>  4 Boron    Brachydanio rerio        10   Fish         mg/L 
#>  5 Boron    Carassius auratus        15.6 Fish         mg/L 
#>  6 Boron    Pimephales promelas      18.3 Fish         mg/L 
#>  7 Boron    Daphnia magna             6   Invertebrate mg/L 
#>  8 Boron    Opercularia bimarginata  10   Invertebrate mg/L 
#>  9 Boron    Ceriodaphnia dubia       13.4 Invertebrate mg/L 
#> 10 Boron    Entosiphon sulcatum      15   Invertebrate mg/L 
#> # ℹ 18 more rows

The six default distributions are fit using ssd_fit_dists()

fits <- ssd_fit_dists(ssddata::ccme_boron)

and can be quickly plotted using autoplot

autoplot(fits)
A plot of the data and fitted distributions

The goodness of fit can be assessed using ssd_gof

ssd_gof(fits)
#> # A tibble: 6 × 9
#>   dist           ad     ks    cvm   aic  aicc   bic delta weight
#>   <chr>       <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
#> 1 gamma       0.440 0.117  0.0554  238.  238.  240. 0.005  0.357
#> 2 lgumbel     0.829 0.158  0.134   244.  245.  247. 6.56   0.013
#> 3 llogis      0.487 0.0994 0.0595  241.  241.  244. 3.39   0.066
#> 4 lnorm       0.507 0.107  0.0703  239.  240.  242. 1.40   0.177
#> 5 lnorm_lnorm 0.320 0.116  0.0414  240.  243.  247. 4.98   0.03 
#> 6 weibull     0.434 0.117  0.0542  238.  238.  240. 0      0.357

and the model-averaged 5% hazard concentration estimated (with bootstrapping to get confidence intervals) using ssd_hc.

set.seed(99)
hc5 <- ssd_hc(fits, ci = TRUE)
print(hc5)
#> # A tibble: 1 × 11
#>   dist    proportion   est    se   lcl   ucl    wt method    nboot pboot samples
#>   <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>     <dbl> <dbl> <I<lis>
#> 1 average       0.05  1.26 0.782 0.407  3.29     1 parametr…  1000     1 <dbl>

Model-averaged predictions complete with confidence intervals can also be estimated by parametric bootstrapping using the stats generic predict.

boron_pred <- predict(fits, ci = TRUE)

The predictions can be plotted together with the original data using ssd_plot.

library(ggplot2)

theme_set(theme_bw())

ssd_plot(ssddata::ccme_boron, boron_pred,
  shape = "Group", color = "Group", label = "Species",
  xlab = "Concentration (mg/L)", ribbon = TRUE
) +
  expand_limits(x = 3000) +
  scale_colour_ssd()
A plot of the data and model-averaged prediction with confidence intervals

Information

Get started with ssdtools at https://bcgov.github.io/ssdtools/articles/ssdtools.html.

A shiny app to allow non-R users to interface with ssdtools is available at https://github.com/bcgov/shinyssdtools.

For the latest changes to the development version see the NEWS file.

For recent developments in SSD modeling including a review of existing software see:

Fox, D.R., et al. 2021. Recent Developments in Species Sensitivity Distribution Modeling. Environ Toxicol Chem 40(2): 293–308. https://doi.org/10.1002/etc.4925.

Getting Help or Reporting an Issue

To report bugs/issues/feature requests, please file an issue.

Contribution

If you would like to contribute to the package, please see our CONTRIBUTING guidelines.

Code of Conduct

Please note that the ssdtools project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

References

Posthuma, L., Suter II, G.W., and Traas, T.P. 2001. Species Sensitivity Distributions in Ecotoxicology. CRC Press.

Licensing

Copyright 2015-2023 Province of British Columbia
Copyright 2021 Environment and Climate Change Canada
Copyright 2023-2024 Australian Government Department of Climate Change, Energy, the Environment and Water

The documentation is released under the CC BY 4.0 License

The code is released under the Apache License 2.0