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Multivariate generalized linear mixed models using R

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multivariate generalized linear mixed models using RDamon M. Berridge, Robert Crouchley.
作者:	Berridge, Damon.
其他作者:	Crouchley, Robert.
出版者:	Boca Raton, FL :CRC Press,©2011.
面頁冊數:	1 online resource (xxiii, 280 p.) :ill.
標題:	Social sciencesResearch
電子資源:	http://www.crcnetbase.com/doi/book/10.1201/b10850
ISBN:	9781439813270 (electronic bk.)

Multivariate generalized linear mixed models using R
Berridge, Damon.

Multivariate generalized linear mixed models using R[electronic resource] /Damon M. Berridge, Robert Crouchley. - Boca Raton, FL :CRC Press,©2011. - 1 online resource (xxiii, 280 p.) :ill.

Includes bibliographical references and indexes.

Introduction2.1.

ISBN: 9781439813270 (electronic bk.)Subjects--Topical Terms:

240022
Social sciences
--Research

LC Class. No.: HA31.35 / .B47 2011

Dewey Class. No.: 300.1519535

Multivariate generalized linear mixed models using R
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100 1 $a Berridge, Damon. $3 544751
245 1 0 $a Multivariate generalized linear mixed models using R $h [electronic resource] / $c Damon M. Berridge, Robert Crouchley.
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300 $a 1 online resource (xxiii, 280 p.) : $b ill.
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.
650 0 $a Social sciences $x Research $x Mathematical models. $3 240022
650 0 $a Social sciences $x Research $x Statistical methods. $3 230792
650 0 $a Social sciences $x Research $x Data processing. $3 268958
650 0 $a Multivariate analysis. $3 181905
700 1 $a Crouchley, Robert. $3 544752
856 4 0 $u http://www.crcnetbase.com/doi/book/10.1201/b10850