1.

Record Nr.

UNINA9910781583203321

Autore

Berridge Damon M.

Titolo

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

Pubbl/distr/stampa

Boca Raton, Fla. : , : CRC Press, , 2011

ISBN

0-429-19160-X

1-4987-4070-7

1-4398-1327-2

Descrizione fisica

1 online resource (284 p.)

Altri autori (Persone)

CrouchleyRobert

Disciplina

003/.35133

Soggetti

R (Computer program language)

Social sciences - Research - Mathematical models

Social sciences - Research - Statistical methods

Social sciences - Research - Data processing

Multivariate analysis

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

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