Multivariate generalized linear mixed models using R / / Damon M. Berridge, Robert Crouchley

• Autore: Berridge Damon M.
• Pubblicazione: Boca Raton, Fla., : CRC Press, c2011; Boca Raton, Fla. : , : CRC Press, , 2011
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (284 p.)
• Disciplina: 003/.35133
• Soggetto topico: R (Computer program language); Social sciences - Research - Mathematical models; Social sciences - Research - Statistical methods; Social sciences - Research - Data processing; Multivariate analysis
• Altri autori: CrouchleyRobert
• Note generali: A Chapman & Hall book.
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: Front Cover; Contents; List of Figures; List of Tables; List of Applications; List of Datasets; Preface; Acknowledgments; 1. Introduction; 2.Generalized linear models for continuous/interval scale data; 3. Generalized linear models for other types of data; 4. Family of generalized linear models; 5. Mixed models for continuous/interval scale data; 6. Mixed models for binary data; 7. Mixed models for ordinal data; 8. Mixed models for count data; 9. Family of two-level generalized linear models; 10. Three-level generalized linear models; 11. Models for multivariate data ; 12. Models for duration and event history data13. Stayers, non-susceptibles and endpoints; 14. Handling initial conditions/state dependence in binary data; 15. Incidental parameters: an empirical comparison of fixed effects and random effects models; A. SabreR installation, SabreR commands, quadrature, estimation, endogenous effects; B. Introduction to R for Sabre; References
• Sommario/riassunto: To provide researchers with the ability to analyze large and complex data sets using robust models, this book presents a unified framework for a broad class of models that can be applied using a dedicated R package (Sabre). The first five chapters cover the analysis of multilevel models using univariate generalized linear mixed models (GLMMs). The next few chapters extend to multivariate GLMMs and the last chapters address more specialized topics, such as parallel computing for large-scale analyses. Each chapter includes many real-world examples implemented using Sabre as well as exercises and
• Titolo autorizzato: Multivariate generalized linear mixed models using R
• ISBN: 0-429-19160-X; 1-4987-4070-7; 1-4398-1327-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910812458003321
• Lo trovi qui: Univ. Federico II