IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0146021.html
   My bibliography  Save this article

Dose-Response Analysis Using R

Author

Listed:
  • Christian Ritz
  • Florent Baty
  • Jens C Streibig
  • Daniel Gerhard

Abstract

Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.

Suggested Citation

  • Christian Ritz & Florent Baty & Jens C Streibig & Daniel Gerhard, 2015. "Dose-Response Analysis Using R," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0146021
    DOI: 10.1371/journal.pone.0146021
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0146021
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0146021&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0146021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kahm, Matthias & Hasenbrink, Guido & Lichtenberg-Fraté, Hella & Ludwig, Jost & Kschischo, Maik, 2010. "grofit: Fitting Biological Growth Curves with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i07).
    2. Ritz, Christian & Streibig, Jens C., 2005. "Bioassay Analysis Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i05).
    3. Baty, Florent & Ritz, Christian & Charles, Sandrine & Brutsche, Martin & Flandrois, Jean-Pierre & Delignette-Muller, Marie-Laure, 2015. "A Toolbox for Nonlinear Regression in R: The Package nlstools," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i05).
    4. Bornkamp, Björn & Pinheiro, José & Bretz, Frank, 2009. "MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i07).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baty, Florent & Ritz, Christian & Charles, Sandrine & Brutsche, Martin & Flandrois, Jean-Pierre & Delignette-Muller, Marie-Laure, 2015. "A Toolbox for Nonlinear Regression in R: The Package nlstools," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i05).
    2. Lea A I Vaas & Johannes Sikorski & Victoria Michael & Markus Göker & Hans-Peter Klenk, 2012. "Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-18, April.
    3. Luca Micoli & Giuseppe Di Rauso Simeone & Maria Turco & Giuseppe Toscano & Maria A. Rao, 2023. "Anaerobic Digestion of Olive Mill Wastewater in the Presence of Biochar," Energies, MDPI, vol. 16(7), pages 1-14, April.
    4. Tilman Schmider & Anne Grethe Hestnes & Julia Brzykcy & Hannes Schmidt & Arno Schintlmeister & Benjamin R. K. Roller & Ezequiel Jesús Teran & Andrea Söllinger & Oliver Schmidt & Martin F. Polz & Andre, 2024. "Physiological basis for atmospheric methane oxidation and methanotrophic growth on air," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Philip N. Newsome & Arun J. Sanyal & Guy Neff & Jörn M. Schattenberg & Vlad Ratziu & Judith Ertle & Jasmin Link & Alison Mackie & Corinna Schoelch & Eric Lawitz, 2023. "A randomised Phase IIa trial of amine oxidase copper-containing 3 (AOC3) inhibitor BI 1467335 in adults with non-alcoholic steatohepatitis," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    6. Camilo Guzmán & Manish Bagga & Amanpreet Kaur & Jukka Westermarck & Daniel Abankwa, 2014. "ColonyArea: An ImageJ Plugin to Automatically Quantify Colony Formation in Clonogenic Assays," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-9, March.
    7. Jens Peter Andersen & Björn Hammarfelt, 2011. "Price revisited: on the growth of dissertations in eight research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 371-383, August.
    8. Mario Gallego-Abenza & Nicolas Mathevon & David Wheatcroft & Ulrika Candolin, 2020. "Experience modulates an insect’s response to anthropogenic noise," Behavioral Ecology, International Society for Behavioral Ecology, vol. 31(1), pages 90-96.
    9. S. Mahmuda & T. Sigler & E. Knight & J. Corcoran, 2020. "Sectoral evolution and shifting service delivery models in the sharing economy," Business Research, Springer;German Academic Association for Business Research, vol. 13(2), pages 663-684, July.
    10. LonÄ arić, Željka & K. Hackenberger, Branimir, 2013. "Stage and age structured Aedes vexans and Culex pipiens (Diptera: Culicidae) climate-dependent matrix population model," Theoretical Population Biology, Elsevier, vol. 83(C), pages 82-94.
    11. Aysan Badraghi & Beáta Novotná & Jan Frouz & Koloman Krištof & Martin Trakovický & Martin Juriga & Branislav Chvila & Leonardo Montagnani, 2023. "Temporal Dynamics of CO 2 Fluxes over a Non-Irrigated Vineyard," Land, MDPI, vol. 12(10), pages 1-16, October.
    12. Se Yoon Lee & Bowen Lei & Bani Mallick, 2020. "Estimation of COVID-19 spread curves integrating global data and borrowing information," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
    13. Taufan Alam & Priyono Suryanto & Nanang Susyanto & Budiastuti Kurniasih & Panjisakti Basunanda & Eka Tarwaca Susila Putra & Dody Kastono & Dyah Weny Respatie & Muhammad Habib Widyawan & Nurmansyah & A, 2022. "Performance of 45 Non-Linear Models for Determining Critical Period of Weed Control and Acceptable Yield Loss in Soybean Agroforestry Systems," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    14. Frank Schaarschmidt & Christian Ritz & Ludwig A. Hothorn, 2022. "The Tukey trend test: Multiplicity adjustment using multiple marginal models," Biometrics, The International Biometric Society, vol. 78(2), pages 789-797, June.
    15. Román-Román, P. & Torres-Ruiz, F., 2015. "A stochastic model related to the Richards-type growth curve. Estimation by means of simulated annealing and variable neighborhood search," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 579-598.
    16. Duarte, Belmiro P.M. & Atkinson, Anthony C. & Oliveira, Nuno M.C., 2023. "Optimum design for ill-conditioned models: K–optimality and stable parameterizations," LSE Research Online Documents on Economics 122986, London School of Economics and Political Science, LSE Library.
    17. Signe M. Jensen & Christian Ritz, 2015. "Simultaneous Inference for Model Averaging of Derived Parameters," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 68-76, January.
    18. Christoph Kunz & Dominic J. Sturm & Markus Sökefeld & Roland Gerhards, 2017. "Weed suppression and early sugar beet development under different cover crop mulches," Plant Protection Science, Czech Academy of Agricultural Sciences, vol. 53(3), pages 187-193.
    19. Diana Carolina Rodríguez-Abello & Jorge Augusto Navarro-Alberto & Luis Ramírez-Avilés & Roberto Zamora-Bustillos, 2018. "The effect of sowing time on the growth of chia (Salvia hispanica L.): What do nonlinear mixed models tell us about it?," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-14, November.
    20. Mathilde, Godefroid & Tom, Zeimes & Lorenzo, Bramanti & Pascal, Romans & Marzia, Bo & Margherita, Toma & Bruno, Danis & Philippe, Dubois & Charlène, Guillaumot, 2023. "Low vulnerability of the Mediterranean antipatharian Antipathella subpinnata (Ellis & Solander, 1786) to ocean warming," Ecological Modelling, Elsevier, vol. 475(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0146021. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.