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Discrete choice analysis with R

Páez, Antonio.

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Discrete choice analysis with R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Discrete choice analysis with R/ by Antonio Paez, Genevieve Boisjoly.
Author:	Páez, Antonio.
other author:	Boisjoly, Geneviève.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xxiii, 332 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Social sciences - Statistical methods -
Online resource:	https://doi.org/10.1007/978-3-031-20719-8
ISBN:	9783031207198

Discrete choice analysis with R
Páez, Antonio.

Discrete choice analysis with R[electronic resource] /by Antonio Paez, Genevieve Boisjoly. - Cham :Springer International Publishing :2022. - xxiii, 332 p. :ill., digital ;24 cm. - Use R!,2197-5744. - Use R!.

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.

ISBN: 9783031207198

Standard No.: 10.1007/978-3-031-20719-8doiSubjects--Topical Terms:

565862
Social sciences
--Statistical methods

LC Class. No.: H61.25 / .P34 2022

Dewey Class. No.: 300.727

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245 1 0 $a Discrete choice analysis with R $h [electronic resource] / $c by Antonio Paez, Genevieve Boisjoly.
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505 0 $a 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.
520 $a 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.
650 0 $a Social sciences $x Statistical methods $x Mathematical models. $3 565862
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650 0 $a R (Computer program language) $x Mathematical models. $3 1535971
650 1 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. $3 3538811
650 2 4 $a Methodology of Data Collection and Processing. $3 3598081
700 1 $a Boisjoly, Geneviève. $3 3614126
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856 4 0 $u https://doi.org/10.1007/978-3-031-20719-8
950 $a Mathematics and Statistics (SpringerNature-11649)