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100   $a20180331d2011      uy         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aMultivariate generalized linear mixed models using R /$fDamon M. Berridge, Robert Crouchley
205   $a1st ed.
210   $aBoca Raton, Fla. $cCRC Press$dc2011
210  1$aBoca Raton, Fla. :$cCRC Press,$d2011.
215   $a1 online resource (284 p.)
300   $aA Chapman & Hall book.
311   $a1-322-61288-9 
311   $a1-4398-1326-4 
320   $aIncludes bibliographical references and indexes.
327   $aFront 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
327   $a12. 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
330   $aTo 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
606   $aR (Computer program language)
606   $aSocial sciences$xResearch$xMathematical models
606   $aSocial sciences$xResearch$xStatistical methods
606   $aSocial sciences$xResearch$xData processing
606   $aMultivariate analysis
615  0$aR (Computer program language).
615  0$aSocial sciences$xResearch$xMathematical models.
615  0$aSocial sciences$xResearch$xStatistical methods.
615  0$aSocial sciences$xResearch$xData processing.
615  0$aMultivariate analysis.
676   $a003/.35133
700   $aBerridge$b Damon M.$01651990
701   $aCrouchley$b Robert$01651991
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910812458003321
996   $aMultivariate generalized linear mixed models using R$94002323
997   $aUNINA