Discrete Choice Analysis with R (Use R!) - Tapa blanda

Acerca del autor

Antonio Páez is a Professor in the School of Earth, Environment and Society at McMaster University. He trained as a civil engineer and upon joining McMaster was adopted into geography as his home discipline. Now he specializes in spatial data analysis, discrete choice modeling, transportation systems, accessibility, and urban and health geography. He is listed as author or co-author in more than 130 peer reviewed articles in international academic journals, and also recently released his book An Introduction to Spatial Data Analysis and Statistics: A Course in R. He has long standing interests in languages, science, science fiction, fantasy, and art. He lives in Hamilton, Ontario, where the summers are short but hot.

Geneviève Boisjoly is an Assistant Professor in transport engineering at Polytechnique Montréal. Building on her multidisciplinary background (mechanical engineering, sustainability science and urban planning), she is concerned with the interactions between transport networks, land use and travel behavior, from a sustainability standpoint. She has developed an extensive expertise related to indicators of access to urban opportunities and their integration in urban planning. More specifically, she works on the needs and perceptions of users and on how to include them in integrated land use and transport planning tools. Her work focuses on active and public transport modes, travel behavior and social equity. 

De la contraportada

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.