000			03631cam a2200505M 4500
001			9781315270098
003			FlBoTFG
005			20220414100212.0
006			m o d
007			cr |n|||||||||
008			190725s2018 xx o 000 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9781315270098 _q(electronic bk.)
020			_a1315270099 _q(electronic bk.)
020			_a9781351981033 _q(electronic bk. : EPUB)
020			_a135198103X _q(electronic bk. : EPUB)
020			_a9781351981040 _q(electronic bk. : PDF)
020			_a1351981048 _q(electronic bk. : PDF)
020			_a9781351981026 _q(electronic bk. : Mobipocket)
020			_a1351981021 _q(electronic bk. : Mobipocket)
020			_z9781138034310
020			_z1138034312
035			_a(OCoLC)1109972695 _z(OCoLC)1111977079
035			_a(OCoLC-P)1109972695
050		4	_aRM301.8
072		7	_aMAT _x029000 _2bisacsh
072		7	_aMED _x096000 _2bisacsh
072		7	_aPBT _2bicssc
082	0	4	_a615.1 _223
100	1		_aRitz, Christian, _eauthor.
245	1	0	_aDose-response Analysis With R _h[electronic resource].
260			_bChapman & Hall _c2018.
300			_a1 online resource
520			_aNowadays 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.
588			_aOCLC-licensed vendor bibliographic record.
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
650		7	_aMEDICAL / Toxicology _2bisacsh
650		0	_aDrugs _xDose-response relationship.
650		0	_aDrugs _xTesting _xComputer simulation.
700	1		_aStreibig, Jens C.
700	1		_aJensen, Signe Marie.
700	1		_aGerhard, Daniel.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781315270098
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c57251 _d57251