Dose-Response Analysis Using R
By Christian Ritz, Florent Baty, Jens Carl Streibig, Daniel Gerhard. PLoS ONE, 2015
3,246 citations recorded by the source corpus.
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.
Subfields: Cancer Research, Food Science, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/b71b71a256e1d88ddf38833e6834a4f1912063d0
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In Cancer Research
In Management Science and Operations Research
Citations in the corpus
- Remarkably high and consistent tolerance of a Red Sea coral to acute and chronic thermal stress exposures (2021, methodology): “Measurements of Fv/Fm were fitted to log-logistic dose–response curves (DRCs) using the package "drc” (Ritz et al. 2015), with model selection based on Akaike’s Information Criterion (AIC; Table S2) and individual curves fit for each experiment.”
- Hydrothermal-time-to-event models for seed germination (2018, methodology): “All the models and their sub-models, including the appropriate selfstarting routines, were incorporated into the package “drc” (Ritz et al., 2015) within the R statistical environment (R Core Team, 2016).”