Details

Dose-Response Analysis Using R


Dose-Response Analysis Using R


Chapman & Hall/CRC The R Series

von: Christian Ritz

CHF 121.66

Verlag: Chapman and Hall/CRC
Format: EPUB, PDF
Veröffentl.: 19.07.2019
ISBN/EAN: 9781351981033
Sprache: englisch
Anzahl Seiten: 214

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.

Beschreibungen

Nowadays the term dose-response is used in many different contexts and many different scientific disciplines including agriculture, biochemistry, chemistry, environmental sciences, genetics, pharmacology, plant sciences, toxicology, and zoology.In the 1940 and 1950s, dose-response analysis was intimately linked to evaluation of toxicity in terms of binary responses, such as immobility and mortality, with a limited number of doses of a toxic compound being compared to a control group (dose 0). Later, dose-response analysis has been extended to other types of data and to more complex experimental designs. Moreover, estimation of model parameters has undergone a dramatic change, from struggling with cumbersome manual operations and transformations with pen and paper to rapid calculations on any laptop. Advances in statistical software have fueled this development.Key Features:Provides a practical and comprehensive overview of dose-response analysis. Includes numerous real data examples to illustrate the methodology. R code is integrated into the text to give guidance on applying the methods. Written with minimal mathematics to be suitable for practitioners. Includes code and datasets on the book's GitHub: https://github.com/DoseResponse.This book focuses on estimation and interpretation of entirely parametric nonlinear dose-response models using the powerful statistical environment R. Specifically, this book introduces dose-response analysis of continuous, binomial, count, multinomial, and event-time dose-response data. The statistical models used are partly special cases, partly extensions of nonlinear regression models, generalized linear and nonlinear regression models, and nonlinear mixed-effects models (for hierarchical dose-response data). Both simple and complex dose-response experiments will be analyzed.

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