Multivariate generalized linear mixed models using R
By: Damon M. Berridge, Robert Crouchley
Contributors: Crouchley, Robert
Publisher: CRC Press
Published: 2011
Place of publication: Boca Raton, FL
Language: eng
Number of pages: 280
ISBN-13: 9781439813263
Subjects: Statistical methods; Multivariate analysis; Data processing; Mathematical models; Social sciences; Research
Notes: Includes bibliographical references and indexes.
Table of contents: title: Machine generated contents note: -- 2.1.; title: Introduction -- -- 2.2.; title: Continuous/interval scale data -- -- 2.3.; title: Simple and multiple linear regression models -- -- 2.4.; title: Checking assumptions in linear regression models -- -- 2.5.; title: Likelihood: multiple linear regression -- -- 2.6.; title: Comparing model likelihoods -- -- 2.7.; title: Application of a multiple linear regression model -- -- 2.8.; title: Exercises on linear models -- -- 3.1.; title: Binary data -- -- 3.1.1.; title: Introduction -- -- 3.1.2.; title: Logistic regression -- -- 3.1.3.; title: Logit and probit transformations -- -- 3.1.4.; title: General logistic regression -- -- 3.1.5.; title: Likelihood -- -- 3.1.6.; title: Example with binary data -- -- 3.2.; title: Ordinal data -- -- 3.2.1.; title: Introduction -- -- 3.2.2.; title: The ordered logit model -- -- 3.2.3.; title: Dichotomization of ordered categories -- -- 3.2.4.; title: Likelihood -- -- 3.2.5.; title: Example with ordered data -- -- 3.3.; title: Count data -- -- 3.3.1.; title: Introduction -- -- 3.3.2.; title: Poisson regression models -- -- 3.3.3.; title: Likelihood -- -- 3.3.4.; title: Example with count data -- -- 3.4.; title: Exercises -- -- 4.1.; title: Introduction -- -- 4.2.; title: The linear model; title: 4.3.; title: The binary response model -- -- 4.4.; title: The Poisson model -- -- 4.5.; title: Likelihood -- -- 5.1.; title: Introduction -- -- 5.2.; title: Linear mixed model -- -- 5.3.; title: The intraclass correlation coefficient -- -- 5.4.; title: Parameter estimation by maximum likelihood -- -- 5.5.; title: Regression with level-two effects -- -- 5.6.; title: Two-level random intercept models -- -- 5.7.; title: General two-level models including random intercepts -- -- 5.8.; title: Likelihood -- -- 5.9.; title: Residuals -- -- 5.10.; title: Checking assumptions in mixed models -- -- 5.11.; title: Comparing model likelihoods -- -- 5.12.; title: Application of a two-level linear model -- -- 5.13.; title: Two-level growth models -- -- 5.13.1.; title: A two-level repeated measures model -- -- 5.13.2.; title: A linear growth model -- -- 5.13.3.; title: A quadratic growth model -- -- 5.14.; title: Likelihood -- -- 5.15.; title: Example using linear growth models -- -- 5.16.; title: Exercises using mixed models for continuous/interval scale data -- -- 6.1.; title: Introduction -- -- 6.2.; title: The two-level logistic model -- -- 6.3.; title: General two-level logistic models -- -- 6.4.; title: Intraclass correlation coefficient -- -- 6.5.; title: Likelihood -- -- 6.6.; title: Example using binary data -- -- 6.7.; title: Exercises using mixed models for binary data; title: 7.1.; title: Introduction -- -- 7.2.; title: The two-level ordered logit model -- -- 7.3.; title: Likelihood -- -- 7.4.; title: Example using mixed models for ordered data -- -- 7.5.; title: Exercises using mixed models for ordinal data -- -- 8.1.; title: Introduction -- -- 8.2.; title: The two-level Poisson model -- -- 8.3.; title: Likelihood -- -- 8.4.; title: Example using mixed models for count data -- -- 8.5.; title: Exercises using mixed models for count data -- -- 9.1.; title: Introduction -- -- 9.2.; title: The mixed linear model -- -- 9.3.; title: The mixed binary response model -- -- 9.4.; title: The mixed Poisson model -- -- 9.5.; title: Likelihood -- -- 10.1.; title: Introduction -- -- 10.2.; title: Three-level random intercept models -- -- 10.3.; title: Three-level generalized linear models -- -- 10.4.; title: Linear models -- -- 10.5.; title: Binary response models -- -- 10.6.; title: Likelihood -- -- 10.7.; title: Example using three-level generalized linear models -- -- 10.8.; title: Exercises using three-level generalized linear mixed models -- -- 11.1.; title: Introduction -- -- 11.2.; title: Multivariate two-level generalized linear model -- -- 11.3.; title: Bivariate Poisson model: example -- -- 11.4.; title: Bivariate ordered response model: example -- -- 11.5.; title: Bivariate linear-probit model: example -- -- 11.6.; title: Multivariate two-level generalized linear model likelihood; title: 11.7.; title: Exercises using multivariate generalized linear mixed models -- -- 12.1.; title: Introduction -- -- 12.1.1.; title: Left censoring -- -- 12.1.2.; title: Right censoring -- -- 12.1.3.; title: Time-varying explanatory variables -- -- 12.1.4.; title: Competing risks -- -- 12.2.; title: Duration data in discrete time -- -- 12.2.1.; title: Single-level models for duration data -- -- 12.2.2.; title: Two-level models for duration data -- -- 12.2.3.; title: Three-level models for duration data -- -- 12.3.; title: Renewal data -- -- 12.3.1.; title: Introduction -- -- 12.3.2.; title: Example: renewal models -- -- 12.4.; title: Competing risk data -- -- 12.4.1.; title: Introduction -- -- 12.4.2.; title: Likelihood -- -- 12.4.3.; title: Example: competing risk data -- -- 12.5.; title: Exercises using renewal and competing risks models -- -- 13.1.; title: Introduction -- -- 13.2.; title: Mover-stayer model -- -- 13.3.; title: Likelihood incorporating the mover-stayer model -- -- 13.4.; title: Example 1: stayers within count data -- -- 13.5.; title: Example 2: stayers within binary data -- -- 13.6.; title: Exercises: stayers -- -- 14.1.; title: Introduction to key issues: heterogeneity, state dependence and non-stationarity -- -- 14.2.; title: Example -- -- 14.3.; title: Random effects models -- -- 14.4.; title: Initial conditions problem -- -- 14.5.; title: Initial treatment; title: 14.6.; title: Example: depression data -- -- 14.7.; title: Classical conditional analysis -- -- 14.8.; title: Classical conditional model: example -- -- 14.9.; title: Conditioning on initial response but allowing random effect uol to be dependent on z3 -- -- 14.10.; title: Wooldridge conditional model: example -- -- 14.11.; title: Modelling the initial conditions -- -- 14.12.; title: Same random effect in the initial response and subsequent response models with a common scale parameter -- -- 14.13.; title: Joint analysis with a common random effect: example -- -- 14.14.; title: Same random effect in models of the initial response and subsequent responses but with different scale parameters -- -- 14.15.; title: Joint analysis with a common random effect (different scale parameters): example -- -- 14.16.; title: Different random effects in models of the initial response and subsequent responses -- -- 14.17.; title: Different random effects: example -- -- 14.18.; title: Embedding the Wooldridge approach in joint models for the initial response and subsequent responses -- -- 14.19.; title: Joint model incorporating the Wooldridge approach: example -- -- 14.20.; title: Other link functions -- -- 14.21.; title: Exercises using models incorporating initial conditions/state dependence in binary data; title: 15.1.; title: Introduction -- -- 15.2.; title: Fixed effects treatment of the two-level linear model -- -- 15.3.; title: Dummy variable specification of the fixed effects model -- -- 15.4.; title: Empirical comparison of two-level fixed effects and random effects estimators -- -- 15.5.; title: Implicit fixed effects estimator -- -- 15.6.; title: Random effects models -- -- 15.7.; title: Comparing two-level fixed effects and random effects models -- -- 15.8.; title: Fixed effects treatment of the three-level linear model -- -- 15.9.; title: Exercises comparing fixed effects and random effects -- -- A.1.; title: SabreR installation -- -- A.2.; title: SabreR commands -- -- A.2.1.; title: The arguments of the SabreR object -- -- A.2.2.; title: The anatomy of a SabreR command file -- -- A.3.; title: Quadrature -- -- A.3.1.; title: Standard Gaussian quadrature -- -- A.3.2.; title: Performance of Gaussian quadrature -- -- A.3.3.; title: Adaptive quadrature -- -- A.4.; title: Estimation -- -- A.4.1.; title: Maximizing the log likelihood of random effects models -- -- A.5.; title: Fixed effects linear models -- -- A.6.; title: Endogenous and exogenous variables -- -- B.1.; title: Getting started with R -- -- B.1.1.; title: Preliminaries -- -- B.1.1.1.; title: Working with R in interactive mode -- -- B.1.1.2.; title: Basic functions -- -- B.1.1.3.; title: Getting help; title: B.1.1.4.; title: Stopping R -- -- B.1.2.; title: Creating and manipulating data -- -- B.1.2.1.; title: Vectors and lists -- -- B.1.2.2.; title: Vectors -- -- B.1.2.3.; title: Vector operations -- -- B.1.2.4.; title: Lists -- -- B.1.2.5.; title: Data frames -- -- B.1.3.; title: Session management -- -- B.1.3.1.; title: Managing objects -- -- B.1.3.2.; title: Attaching and detaching objects -- -- B.1.3.3.; title: Serialization -- -- B.1.3.4.; title: R scripts -- -- B.1.3.5.; title: Batch processing -- -- B.1.4.; title: R packages -- -- B.1.4.1.; title: Loading a package into R -- -- B.1.4.2.; title: Installing a package for use in R -- -- B.1.4.3.; title: R and Statistics -- -- B.2.; title: Data preparation for SabreR -- -- B.2.1.; title: Creation of dummy variables -- -- B.2.2.; title: Missing values -- -- B.2.3.; title: Creating lagged response covariate data.