Multivariate generalized linear mixed models using R /

Multivariate generalized linear mixed models using R /

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Bibliographic Details
Author / Creator:Berridge, Damon, author.
Imprint:Boca Raton, FL : CRC Press, ©2011.
Description:1 online resource (xxiii, 280 pages) : illustrations
Language:English
Subject:
Social sciences -- Research -- Mathematical models.
Social sciences -- Research -- Statistical methods.
Social sciences -- Research -- Data processing.
Multivariate analysis.
Research -- statistics & numerical data.
Multivariate analysis.
Social sciences -- Research -- Data processing.
Social sciences -- Research -- Mathematical models.
Social sciences -- Research -- Statistical methods.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12012455
Hidden Bibliographic Details
Other authors / contributors:Crouchley, Robert, author.
ISBN:9781439813270
1439813272
9781439813263
1439813264
1498740707
9781498740708
Digital file characteristics:data file
Notes:Includes bibliographical references and indexes.
English.
Print version record.
Summary:Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R.A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples. Improve Your Longitudinal Study In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website.
Other form:Print version: Berridge, Damon. Multivariate generalized linear mixed models using R. Boca Raton, FL : CRC Press, ©2011 9781439813263

MARC

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245 1 0 |a Multivariate generalized linear mixed models using R /  |c Damon M. Berridge, Robert Crouchley. 
260 |a Boca Raton, FL :  |b CRC Press,  |c ©2011. 
300 |a 1 online resource (xxiii, 280 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
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504 |a Includes bibliographical references and indexes. 
505 0 0 |g 2.1.  |t Introduction  |g 2.2.  |t Continuous/interval scale data  |g 2.3.  |t Simple and multiple linear regression models --  |g 2.4.  |t Checking assumptions in linear regression models --  |g 2.5.  |t Likelihood: multiple linear regression --  |g 2.6.  |t Comparing model likelihoods --  |g 2.7.  |t Application of a multiple linear regression model --  |g 2.8.  |t Exercises on linear models --  |g 3.1.  |t Binary data --  |g 3.1.1.  |t Introduction --  |g 3.1.2.  |t Logistic regression --  |g 3.1.3.  |t Logit and probit transformations --  |g 3.1.4.  |t General logistic regression --  |g 3.1.5.  |t Likelihood --  |g 3.1.6.  |t Example with binary data --  |g 3.2.  |t Ordinal data --  |g 3.2.1.  |t Introduction --  |g 3.2.2.  |t The ordered logit model --  |g 3.2.3.  |t Dichotomization of ordered categories --  |g 3.2.4.  |t Likelihood --  |g 3.2.5.  |t Example with ordered data --  |g 3.3.  |t Count data --  |g 3.3.1.  |t Introduction --  |g 3.3.2.  |t Poisson regression models --  |g 3.3.3.  |t Likelihood --  |g 3.3.4.  |t Example with count data --  |g 3.4.  |t Exercises --  |g 4.1.  |t Introduction --  |g 4.2.  |t The linear model. 
505 0 0 |g 4.3.  |t The binary response model --  |g 4.4.  |t The Poisson model --  |g 4.5.  |t Likelihood --  |g 5.1.  |t Introduction --  |g 5.2.  |t Linear mixed model --  |g 5.3.  |t The intraclass correlation coefficient --  |g 5.4.  |t Parameter estimation by maximum likelihood --  |g 5.5.  |t Regression with level-two effects --  |g 5.6.  |t Two-level random intercept models --  |g 5.7.  |t General two-level models including random intercepts --  |g 5.8.  |t Likelihood --  |g 5.9.  |t Residuals --  |g 5.10.  |t Checking assumptions in mixed models --  |g 5.11.  |t Comparing model likelihoods --  |g 5.12.  |t Application of a two-level linear model --  |g 5.13.  |t Two-level growth models --  |g 5.13.1.  |t A two-level repeated measures model --  |g 5.13.2.  |t A linear growth model --  |g 5.13.3.  |t A quadratic growth model --  |g 5.14.  |t Likelihood --  |g 5.15.  |t Example using linear growth models --  |g 5.16.  |t Exercises using mixed models for continuous/interval scale data --  |g 6.1.  |t Introduction --  |g 6.2.  |t The two-level logistic model --  |g 6.3.  |t General two-level logistic models --  |g 6.4.  |t Intraclass correlation coefficient --  |g 6.5.  |t Likelihood --  |g 6.6.  |t Example using binary data --  |g 6.7.  |t Exercises using mixed models for binary data. 
505 0 0 |g 7.1.  |t Introduction --  |g 7.2.  |t The two-level ordered logit model --  |g 7.3.  |t Likelihood --  |g 7.4.  |t Example using mixed models for ordered data --  |g 7.5.  |t Exercises using mixed models for ordinal data --  |g 8.1.  |t Introduction --  |g 8.2.  |t The two-level Poisson model --  |g 8.3.  |t Likelihood --  |g 8.4.  |t Example using mixed models for count data --  |g 8.5.  |t Exercises using mixed models for count data --  |g 9.1.  |t Introduction --  |g 9.2.  |t The mixed linear model --  |g 9.3.  |t The mixed binary response model --  |g 9.4.  |t The mixed Poisson model --  |g 9.5.  |t Likelihood --  |g 10.1.  |t Introduction --  |g 10.2.  |t Three-level random intercept models --  |g 10.3.  |t Three-level generalized linear models --  |g 10.4.  |t Linear models --  |g 10.5.  |t Binary response models --  |g 10.6.  |t Likelihood --  |g 10.7.  |t Example using three-level generalized linear models --  |g 10.8.  |t Exercises using three-level generalized linear mixed models --  |g 11.1.  |t Introduction --  |g 11.2.  |t Multivariate two-level generalized linear model --  |g 11.3.  |t Bivariate Poisson model: example --  |g 11.4.  |t Bivariate ordered response model: example --  |g 11.5.  |t Bivariate linear-probit model: example --  |g 11.6.  |t Multivariate two-level generalized linear model likelihood. 
505 0 0 |g 11.7.  |t Exercises using multivariate generalized linear mixed models --  |g 12.1.  |t Introduction --  |g 12.1.1.  |t Left censoring --  |g 12.1.2.  |t Right censoring --  |g 12.1.3.  |t Time-varying explanatory variables --  |g 12.1.4.  |t Competing risks --  |g 12.2.  |t Duration data in discrete time --  |g 12.2.1.  |t Single-level models for duration data --  |g 12.2.2.  |t Two-level models for duration data --  |g 12.2.3.  |t Three-level models for duration data --  |g 12.3.  |t Renewal data --  |g 12.3.1.  |t Introduction --  |g 12.3.2.  |t Example: renewal models --  |g 12.4.  |t Competing risk data --  |g 12.4.1.  |t Introduction --  |g 12.4.2.  |t Likelihood --  |g 12.4.3.  |t Example: competing risk data --  |g 12.5.  |t Exercises using renewal and competing risks models --  |g 13.1.  |t Introduction --  |g 13.2.  |t Mover-stayer model --  |g 13.3.  |t Likelihood incorporating the mover-stayer model --  |g 13.4.  |t Example 1: stayers within count data --  |g 13.5.  |t Example 2: stayers within binary data --  |g 13.6.  |t Exercises: stayers --  |g 14.1.  |t Introduction to key issues: heterogeneity, state dependence and non-stationarity --  |g 14.2.  |t Example --  |g 14.3.  |t Random effects models --  |g 14.4.  |t Initial conditions problem --  |g 14.5.  |t Initial treatment. 
505 0 0 |g 14.6.  |t Example: depression data --  |g 14.7.  |t Classical conditional analysis --  |g 14.8.  |t Classical conditional model: example --  |g 14.9.  |t Conditioning on initial response but allowing random effect uol to be dependent on z3 --  |g 14.10.  |t Wooldridge conditional model: example --  |g 14.11.  |t Modelling the initial conditions --  |g 14.12.  |t Same random effect in the initial response and subsequent response models with a common scale parameter --  |g 14.13.  |t Joint analysis with a common random effect: example --  |g 14.14.  |t Same random effect in models of the initial response and subsequent responses but with different scale parameters --  |g 14.15.  |t Joint analysis with a common random effect (different scale parameters): example --  |g 14.16.  |t Different random effects in models of the initial response and subsequent responses --  |g 14.17.  |t Different random effects: example --  |g 14.18.  |t Embedding the Wooldridge approach in joint models for the initial response and subsequent responses --  |g 14.19.  |t Joint model incorporating the Wooldridge approach: example --  |g 14.20.  |t Other link functions --  |g 14.21.  |t Exercises using models incorporating initial conditions/state dependence in binary data. 
505 0 0 |g 15.1.  |t Introduction --  |g 15.2.  |t Fixed effects treatment of the two-level linear model --  |g 15.3.  |t Dummy variable specification of the fixed effects model --  |g 15.4.  |t Empirical comparison of two-level fixed effects and random effects estimators --  |g 15.5.  |t Implicit fixed effects estimator --  |g 15.6.  |t Random effects models --  |g 15.7.  |t Comparing two-level fixed effects and random effects models --  |g 15.8.  |t Fixed effects treatment of the three-level linear model --  |g 15.9.  |t Exercises comparing fixed effects and random effects --  |g A.1.  |t SabreR installation --  |g A.2.  |t SabreR commands --  |g A.2.1.  |t The arguments of the SabreR object --  |g A.2.2.  |t The anatomy of a SabreR command file --  |g A.3.  |t Quadrature --  |g A.3.1.  |t Standard Gaussian quadrature --  |g A.3.2.  |t Performance of Gaussian quadrature --  |g A.3.3.  |t Adaptive quadrature --  |g A.4.  |t Estimation --  |g A.4.1.  |t Maximizing the log likelihood of random effects models --  |g A.5.  |t Fixed effects linear models --  |g A.6.  |t Endogenous and exogenous variables --  |g B.1.  |t Getting started with R --  |g B.1.1.  |t Preliminaries --  |g B.1.1.1.  |t Working with R in interactive mode --  |g B.1.1.2.  |t Basic functions --  |g B.1.1.3.  |t Getting help. 
505 0 0 |g B.1.1.4.  |t Stopping R --  |g B.1.2.  |t Creating and manipulating data --  |g B.1.2.1.  |t Vectors and lists --  |g B.1.2.2.  |t Vectors --  |g B.1.2.3.  |t Vector operations --  |g B.1.2.4.  |t Lists --  |g B.1.2.5.  |t Data frames --  |g B.1.3.  |t Session management --  |g B.1.3.1.  |t Managing objects --  |g B.1.3.2.  |t Attaching and detaching objects --  |g B.1.3.3.  |t Serialization --  |g B.1.3.4.  |t R scripts --  |g B.1.3.5.  |t Batch processing --  |g B.1.4.  |t R packages --  |g B.1.4.1.  |t Loading a package into R --  |g B.1.4.2.  |t Installing a package for use in R --  |g B.1.4.3.  |t R and Statistics --  |g B.2.  |t Data preparation for SabreR --  |g B.2.1.  |t Creation of dummy variables --  |g B.2.2.  |t Missing values --  |g B.2.3.  |t Creating lagged response covariate data. 
588 0 |a Print version record. 
520 |a Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R.A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples. Improve Your Longitudinal Study In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website. 
546 |a English. 
650 0 |a Social sciences  |x Research  |x Mathematical models. 
650 0 |a Social sciences  |x Research  |x Statistical methods. 
650 0 |a Social sciences  |x Research  |x Data processing. 
650 0 |a Multivariate analysis.  |0 http://id.loc.gov/authorities/subjects/sh85088390 
650 2 |a Research  |x statistics & numerical data. 
650 7 |a SOCIAL SCIENCE  |x Methodology.  |2 bisacsh 
650 7 |a Multivariate analysis.  |2 fast  |0 (OCoLC)fst01029105 
650 7 |a Social sciences  |x Research  |x Data processing.  |2 fast  |0 (OCoLC)fst01122948 
650 7 |a Social sciences  |x Research  |x Mathematical models.  |2 fast  |0 (OCoLC)fst01122960 
650 7 |a Social sciences  |x Research  |x Statistical methods.  |2 fast  |0 (OCoLC)fst01122971 
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655 0 |a Electronic books. 
655 4 |a Electronic books. 
700 1 |a Crouchley, Robert,  |e author. 
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