04204nam 2200625 450 99650857050331620231009165153.03-031-20719-X10.1007/978-3-031-20719-8(MiAaPQ)EBC7187133(Au-PeEL)EBL7187133(CKB)26069049300041(DE-He213)978-3-031-20719-8(PPN)267807643(EXLCZ)992606904930004120230429d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDiscrete choice analysis with R /Antonio Páez, Geneviève Boisjoly1st ed. 2022.Cham, Switzerland :Springer,[2022]©20221 online resource (349 pages)Use R!,2197-5744Print version: Páez, Antonio Discrete Choice Analysis with R Cham : Springer International Publishing AG,c2023 9783031207181 Includes bibliographical references.1. Data, Models, and Software -- 2. Exploratory Data Analysis -- 3. Fundamental Concepts -- 4. 4 Logit -- 5. Practical Issues in the Specification and Estimation of Discrete Choice Models -- 6. Behavioral Insights from Choice Models -- 7. Non-Proportional Substitution Patterns I: Generalized Extreme Value Models -- 8. Non-Proportional Substitution Patterns II: The Probit Model -- 9. Dealing with Heterogeneity I: The Latent Class Logit Model -- 10 Dealing with Heterogeneity II: The Mixed Logit Model -- 11. Models for Ordinal Responses -- Epilogue -- References.This book is designed as a gentle introduction to the fascinating field of choice modeling and its practical implementation using the R language. Discrete choice analysis is a family of methods useful to study individual decision-making. With strong theoretical foundations in consumer behavior, discrete choice models are used in the analysis of health policy, transportation systems, marketing, economics, public policy, political science, urban planning, and criminology, to mention just a few fields of application. The book does not assume prior knowledge of discrete choice analysis or R, but instead strives to introduce both in an intuitive way, starting from simple concepts and progressing to more sophisticated ideas. Loaded with a wealth of examples and code, the book covers the fundamentals of data and analysis in a progressive way. Readers begin with simple data operations and the underlying theory of choice analysis and conclude by working with sophisticated models including latent class logit models, mixed logit models, and ordinal logit models with taste heterogeneity. Data visualization is emphasized to explore both the input data as well as the results of models. This book should be of interest to graduate students, faculty, and researchers conducting empirical work using individual level choice data who are approaching the field of discrete choice analysis for the first time. In addition, it should interest more advanced modelers wishing to learn about the potential of R for discrete choice analysis. By embedding the treatment of choice modeling within the R ecosystem, readers benefit from learning about the larger R family of packages for data exploration, analysis, and visualization.Use R!,2197-5744MathematicsCiències socialsthubEstadísticathubModels matemàticsthubMostreig (Estadística)thubR (Llenguatge de programació)thubLlibres electrònicsthubMathematics.Ciències socialsEstadísticaModels matemàticsMostreig (Estadística)R (Llenguatge de programació)780Páez Antonio1275240Boisjoly GenevièveMiAaPQMiAaPQMiAaPQBOOK996508570503316Discrete Choice Analysis with R3004746UNISA