Dose-response modelling using R Christian Ritz Faculty of Life - - PowerPoint PPT Presentation

▶

Jan 20, 2024 28 likes •99 views

Christian Ritz Dose-response modelling using R Christian Ritz Faculty of Life Sciences, University of Copenhagen, Denmark Rennes, July 8 2009 Christian Ritz (Uni. Copenhagen) useR! 2009 1 / 7 Package overview: drc Principal idea: use of self

SLIDE 1

Christian Ritz

Dose-response modelling using R

Christian Ritz

Faculty of Life Sciences, University of Copenhagen, Denmark

Rennes, July 8 2009

Christian Ritz (Uni. Copenhagen) useR! 2009 1 / 7

SLIDE 2

Christian Ritz

Package overview: drc

Principal idea: use of self starter functions Model fitting function: drm() Feel and interface much like lm() and glm() Response: continuous, count, or quantal One or more curves separately/simultaneously Parameter constraints possible Methods: anova, plot, predict, summary . . .

Christian Ritz (Uni. Copenhagen) useR! 2009 2 / 7

SLIDE 3

Christian Ritz

Applications

Some examples: Hearing and speech science

◮ Estimation of psychometric functions

Screening of drugs

◮ Analysis of high-throughput dose-response data

Toxicity tests

◮ Estimation of effect concentrations

(e.g. EC/ED/LC/LD50)

Weed science

◮ Modelling seed germination, yield loss Christian Ritz (Uni. Copenhagen) useR! 2009 3 / 7

SLIDE 4

Christian Ritz

Elaborate infrastructure

Off-the-shelf model functions: Symmetric: log-logistic, log-normal (Hill) Asymmetric: Richards, Weibull models (2 types) (also Gompertz) Other: binary mixtures, fractional polynomials, hormesis models (e.g. Brain-Cousens) Full flexibility in model specification: Special cases obtained by fixing parameter values Examples: asymptotic regression, exponential decay, logit, Michaelis-Menten, probit

Christian Ritz (Uni. Copenhagen) useR! 2009 4 / 7

SLIDE 5

Christian Ritz

Special functions

After-fitting: Accessing parameters of interest: ED(), SI() – estimated effect concentrations MAX() – maximum hormesis effects yieldLoss() Other useful functions: compParm() – comparison of parameters maED() – model-averaging rdrm() – simulation of dose-response models

Christian Ritz (Uni. Copenhagen) useR! 2009 5 / 7

SLIDE 6

Christian Ritz

Visualization – traditional graphics

Christian Ritz (Uni. Copenhagen) useR! 2009 6 / 7

SLIDE 7

Christian Ritz

Future developments

This is a dynamic package community Ongoing work: Bootstrap and other types of confidence intervals Extending mixed model capabilities Handling other types of response Robustifying starting value procedures Variance modelling Visualization using lattice

Christian Ritz (Uni. Copenhagen) useR! 2009 7 / 7