LEADER 04204nam 2200625 450 001 996508570503316 005 20231009165153.0 010 $a3-031-20719-X 024 7 $a10.1007/978-3-031-20719-8 035 $a(MiAaPQ)EBC7187133 035 $a(Au-PeEL)EBL7187133 035 $a(CKB)26069049300041 035 $a(DE-He213)978-3-031-20719-8 035 $a(PPN)267807643 035 $a(EXLCZ)9926069049300041 100 $a20230429d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDiscrete choice analysis with R /$fAntonio Pa?ez, Genevie?ve Boisjoly 205 $a1st ed. 2022. 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (349 pages) 225 1 $aUse R!,$x2197-5744 311 08$aPrint version: Páez, Antonio Discrete Choice Analysis with R Cham : Springer International Publishing AG,c2023 9783031207181 320 $aIncludes bibliographical references. 327 $a1. 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. 330 $aThis book is designed as a gentle introduction to the fascinating field of choice modeling and its practical implementation using the R language. Discrete choice analysis is a family of methods useful to study individual decision-making. With strong theoretical foundations in consumer behavior, discrete choice models are used in the analysis of health policy, transportation systems, marketing, economics, public policy, political science, urban planning, and criminology, to mention just a few fields of application. The book does not assume prior knowledge of discrete choice analysis or R, but instead strives to introduce both in an intuitive way, starting from simple concepts and progressing to more sophisticated ideas. Loaded with a wealth of examples and code, the book covers the fundamentals of data and analysis in a progressive way. Readers begin with simple data operations and the underlying theory of choice analysis and conclude by working with sophisticated models including latent class logit models, mixed logit models, and ordinal logit models with taste heterogeneity. Data visualization is emphasized to explore both the input data as well as the results of models. This book should be of interest to graduate students, faculty, and researchers conducting empirical work using individual level choice data who are approaching the field of discrete choice analysis for the first time. In addition, it should interest more advanced modelers wishing to learn about the potential of R for discrete choice analysis. By embedding the treatment of choice modeling within the R ecosystem, readers benefit from learning about the larger R family of packages for data exploration, analysis, and visualization. 410 0$aUse R!,$x2197-5744 606 $aMathematics 606 $aCiències socials$2thub 606 $aEstadística$2thub 606 $aModels matemàtics$2thub 606 $aMostreig (Estadística)$2thub 606 $aR (Llenguatge de programació)$2thub 608 $aLlibres electrònics$2thub 615 0$aMathematics. 615 7$aCiències socials 615 7$aEstadística 615 7$aModels matemàtics 615 7$aMostreig (Estadística) 615 7$aR (Llenguatge de programació) 676 $a780 700 $aPa?ez$b Antonio$01275240 702 $aBoisjoly$b Genevie?ve 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996508570503316 996 $aDiscrete Choice Analysis with R$93004746 997 $aUNISA