Vite degli artisti, vite degli architetti / Hans Ulrich Obrist ; traduzione di Marina Astrologo, Violetta Bellocchio e Vincenzo Latronico

• Record Nr.: UNINA9910271156103321
• Autore: Obrist, Hans Ulrich
• Pubbl/distr/stampa: [Torino] : UTET, 2017
• ISBN: 978-88-511-3643-7
• Descrizione fisica: 621 p. ; 23 cm
• Disciplina: 700.92
• Locazione: FARBC
• Collocazione: ARCH B 2969
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

L' Atlantide

• Record Nr.: UNIBAS000026478
• Pubbl/distr/stampa: Roma : <<Edoardo>> Tinto, 1926 (Tip. Castaldi)
• Descrizione fisica: 31 p. ; 17 cm.
• Collana: Biblioteca dei curiosi ; 10
• Disciplina: 398.42; 001.94
• Soggetti: Atlantide
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: In cop. firma autogr.: Sergio de Pilato

Dose-response Analysis With R

• Record Nr.: UNINA9910959099003321
• Autore: Ritz Christian
• Pubbl/distr/stampa: Chapman & Hall, 2018
• ISBN: 1-351-98104-8; 1-351-98103-X; 1-315-27009-9
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (227 pages)
• Altri autori (Persone): StreibigJens C; JensenSigne Marie; GerhardDaniel
• Disciplina: 615.1
• Soggetti: Drugs - Dose-response relationship; Drugs - Testing - Computer simulation; R (Computer program language)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1. Continuous data -- 1.1 Analysis of single dose-response curves -- 1.1.1 Inhibitory effect of secalonic acid -- 1.1.1.1 Fitting the model -- 1.1.1.2 Estimation of arbitrary ED values -- 1.1.2 Data from a fish test in ecotoxicology -- 1.1.3 Ferulic acid as an herbicide -- 1.1.4 Glyphosate in barley -- 1.1.5 Lower limits for dose-response data -- 1.1.6 A hormesis effect on lettuce growth -- 1.1.7 Nonlinear calibration -- 1.2 Analysis of multiple dose-response curves -- 1.2.1 Effect of an herbicide mixture on Galium aparine -- 1.2.2 Glyphosate and bentazone treatment of Sinapis alba -- 1.2.2.1 A joint dose-response model -- 1.2.2.2 Fitting separate dose-response models -- 2. Binary and binomial dose-response data -- 2.1 Analysis of single dose-response curves -- 2.1.1 Acute inhalation toxicity test -- 2.1.1.1 Link to ordinary logistic regression -- 2.1.2 Tumor incidence -- 2.1.3 Earthworm toxicity test: Abbott's formula -- 2.1.4 Another earthworms toxicity test: Estimating the upper limit -- 2.2 Analysis of multiple dose-response curves -- 2.2.1 Toxicity of fluoranthene under different ultraviolet radiation -- 2.2.2 Toxicity of different types of ; selenium -- 3. Count dose-response data -- 3.1 Analysis of single dose-response curves -- 3.1.1 Counting number of fronds -- 3.1.2 Counting offspring: Modeling hormesis -- 3.1.3 More counting offspring: Varying observation periods -- 3.2 Analysis of multiple dose-response curves -- 3.2.1 Counting bacteria colonies: Wadley's problem -- 4. Multinomial dose-response data -- 4.1 Trichotomous data -- 4.1.1 Insecticide residues -- 4.1.2 Effect of two arboviruses on chicken embryos -- 5. Time-to-event-response data -- 5.1 Analysis of a single germination curve -- 5.1.1 Germination of Stellaria media seeds.; 5.2 Analysis of data from multiple germination curves -- 5.2.1 Time to death of daphnias -- 5.2.1.1 Step 1 -- 5.2.1.2 Step 2 -- 5.2.2 A hierarchical three-way factorial design -- 5.2.2.1 Step 1 -- 5.2.2.2 Step 2 -- 6. Benchmark dose estimation -- 6.1 Binomial dose-response data -- 6.1.1 Pathogens in food -- 6.1.2 Chromosomal damage -- 6.1.3 Tumor incidence continued: Integration of historical data -- 6.2 Continuous dose-response data -- 6.2.1 Toxicity of copper in an ecosystem with giant kelp -- 6.2.2 Toxicity of an antituberculosis drug -- 6.3 Model averaging -- 6.3.1 Pathogens in food revisited -- 6.3.2 Toxicity of an antituberculosis drug revisited -- 7. Hierarchical nonlinear models -- 7.1 Normally distributed dose-response data -- 7.2 The R package medrc -- 7.2.1 In vitro effects of the fungicide vinclozolin -- 7.2.2 Inhibition of photosynthesis in spinach -- 7.2.3 Herbicides with auxin effects -- 7.2.4 Drought stress resistance in Brassica oleracea -- Appendix A: Estimation -- A.1 Nonlinear least squares -- A.2 Maximum likelihood estimation -- A.2.1 Binomial dose-response data -- A.2.2 Count dose-response data -- A.2.2.1 The Poisson distribution -- A.2.2.2 The negative-binomial distribution -- A.2.3 Time-to-event-response data -- A.3 The transform-both-sides approach -- A.4 Robust estimation -- A.5 Sandwich variance estimators -- A.6 Constrained estimation -- A.7 Two-stage estimation for hierarchical models -- A.7.1 Technical replicates -- A.7.2 Two-stage approaches -- A.7.3 Lindstrom-Bates algorithm -- A.8 Starting values and self-starter functions -- A.9 Confidence intervals -- A.10 Prediction and inverse regression -- A.10.1 Effective dose -- A.10.2 Relative potency -- Appendix B: Dose-response model functions -- B.1 Log-logistic models -- B.1.1 Four-parameter log-logistic models -- B.1.1.1 Three-parameter version.; B.1.1.2 Two-parameter version -- B.1.1.3 E-max and Michaelis-Menten models -- B.1.2 Extensions -- B.1.2.1 Generalized log-logistic models -- B.1.2.2 A model with two slope parameters -- B.1.2.3 Hormesis models -- B.1.2.4 Two- and three-phase models -- B.1.2.5 Fractional polynomial models -- B.2 Log-normal models -- B.3 Weibull models -- B.3.1 Weibull type 1 models -- B.3.1.1 Exponential decay model -- B.3.1.2 Other special cases -- B.3.2 Weibull type 2 models -- B.3.2.1 Asymptotic regression -- B.3.2.2 Other special cases -- B.3.2.3 Generalized Weibull-2 model -- B.4 Other types of models -- B.4.1 Gamma models -- B.4.2 Multistage models -- B.4.3 NEC -- B.4.4 Biphasic models with a peak -- B.5 Fixing parameters -- Appendix C: R code for plots -- C.1 Continuous dose-response data -- C.1.1 Ferulic acid as an herbicide -- C.2 Estimation of BMD -- C.2.1 Pathogens in food -- C.2.2 Toxicity of an antituberculosis drug -- C.3 Hierarchical nonlinear models -- C.3.1 Inhibition of photosynthesis in spinach -- C.3.2 Herbicides with auxin effects -- C.3.3 Drought stress resistance in Brassica oleracea -- Bibliography -- Index.
• Sommario/riassunto: 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.