Multivariate Generalized Linear Mixed Models Using R
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). It includes chapters that cover the analysis of multilevel models using univariate generalized linear mixed models (GLMMs).
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Produktdetails
Weitere Autoren: Crouchley, Robert
- ISBN: 978-1-4398-1326-3
- EAN: 9781439813263
- Produktnummer: 20744134
- Verlag: Taylor and Francis
- Sprache: Englisch
- Erscheinungsjahr: 2011
- Seitenangabe: 304 S.
- Masse: H23.4 cm x B15.6 cm 720 g
- Abbildungen: Farb., s/w. Abb.
- Gewicht: 720
Über den Autor
Damon M. Berridge is a senior lecturer in the Department of Mathematics and Statistics at Lancaster University. Dr. Berridge has nearly 20 years of experience as a statistical consultant. His research focuses on the modeling of binary and ordinal recurrent events through random effects models, with application in medical and social statistics.Robert Crouchley is a professor of applied statistics and director of the Centre for e-Science at Lancaster University. His research interests involve the development of statistical methods and software for causal inference in nonexperimental data. These methods include models for errors in variables, missing data, heterogeneity, state dependence, nonstationarity, event history data, and selection effects.
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