Multivariate generalized linear mixed models using R - Info and Reading Options

"Multivariate generalized linear mixed models using R" Table Of Contents:

• 1- Machine generated contents note: -- 2.1.
• 2- Introduction -- -- 2.2.
• 3- Continuous/interval scale data -- -- 2.3.
• 4- Simple and multiple linear regression models -- -- 2.4.
• 5- Checking assumptions in linear regression models -- -- 2.5.
• 6- Likelihood: multiple linear regression -- -- 2.6.
• 7- Comparing model likelihoods -- -- 2.7.
• 8- Application of a multiple linear regression model -- -- 2.8.
• 9- Exercises on linear models -- -- 3.1.
• 10- Binary data -- -- 3.1.1.
• 11- Introduction -- -- 3.1.2.
• 12- Logistic regression -- -- 3.1.3.
• 13- Logit and probit transformations -- -- 3.1.4.
• 14- General logistic regression -- -- 3.1.5.
• 15- Likelihood -- -- 3.1.6.
• 16- Example with binary data -- -- 3.2.
• 17- Ordinal data -- -- 3.2.1.
• 18- Introduction -- -- 3.2.2.
• 19- The ordered logit model -- -- 3.2.3.
• 20- Dichotomization of ordered categories -- -- 3.2.4.
• 21- Likelihood -- -- 3.2.5.
• 22- Example with ordered data -- -- 3.3.
• 23- Count data -- -- 3.3.1.
• 24- Introduction -- -- 3.3.2.
• 25- Poisson regression models -- -- 3.3.3.
• 26- Likelihood -- -- 3.3.4.
• 27- Example with count data -- -- 3.4.
• 28- Exercises -- -- 4.1.
• 29- Introduction -- -- 4.2.
• 30- The linear model
• 31- 4.3.
• 32- The binary response model -- -- 4.4.
• 33- The Poisson model -- -- 4.5.
• 34- Likelihood -- -- 5.1.
• 35- Introduction -- -- 5.2.
• 36- Linear mixed model -- -- 5.3.
• 37- The intraclass correlation coefficient -- -- 5.4.
• 38- Parameter estimation by maximum likelihood -- -- 5.5.
• 39- Regression with level-two effects -- -- 5.6.
• 40- Two-level random intercept models -- -- 5.7.
• 41- General two-level models including random intercepts -- -- 5.8.
• 42- Likelihood -- -- 5.9.
• 43- Residuals -- -- 5.10.
• 44- Checking assumptions in mixed models -- -- 5.11.
• 45- Comparing model likelihoods -- -- 5.12.
• 46- Application of a two-level linear model -- -- 5.13.
• 47- Two-level growth models -- -- 5.13.1.
• 48- A two-level repeated measures model -- -- 5.13.2.
• 49- A linear growth model -- -- 5.13.3.
• 50- A quadratic growth model -- -- 5.14.
• 51- Likelihood -- -- 5.15.
• 52- Example using linear growth models -- -- 5.16.
• 53- Exercises using mixed models for continuous/interval scale data -- -- 6.1.
• 54- Introduction -- -- 6.2.
• 55- The two-level logistic model -- -- 6.3.
• 56- General two-level logistic models -- -- 6.4.
• 57- Intraclass correlation coefficient -- -- 6.5.
• 58- Likelihood -- -- 6.6.
• 59- Example using binary data -- -- 6.7.
• 60- Exercises using mixed models for binary data
• 61- 7.1.
• 62- Introduction -- -- 7.2.
• 63- The two-level ordered logit model -- -- 7.3.
• 64- Likelihood -- -- 7.4.
• 65- Example using mixed models for ordered data -- -- 7.5.
• 66- Exercises using mixed models for ordinal data -- -- 8.1.
• 67- Introduction -- -- 8.2.
• 68- The two-level Poisson model -- -- 8.3.
• 69- Likelihood -- -- 8.4.
• 70- Example using mixed models for count data -- -- 8.5.
• 71- Exercises using mixed models for count data -- -- 9.1.
• 72- Introduction -- -- 9.2.
• 73- The mixed linear model -- -- 9.3.
• 74- The mixed binary response model -- -- 9.4.
• 75- The mixed Poisson model -- -- 9.5.
• 76- Likelihood -- -- 10.1.
• 77- Introduction -- -- 10.2.
• 78- Three-level random intercept models -- -- 10.3.
• 79- Three-level generalized linear models -- -- 10.4.
• 80- Linear models -- -- 10.5.
• 81- Binary response models -- -- 10.6.
• 82- Likelihood -- -- 10.7.
• 83- Example using three-level generalized linear models -- -- 10.8.
• 84- Exercises using three-level generalized linear mixed models -- -- 11.1.
• 85- Introduction -- -- 11.2.
• 86- Multivariate two-level generalized linear model -- -- 11.3.
• 87- Bivariate Poisson model: example -- -- 11.4.
• 88- Bivariate ordered response model: example -- -- 11.5.
• 89- Bivariate linear-probit model: example -- -- 11.6.
• 90- Multivariate two-level generalized linear model likelihood
• 91- 11.7.
• 92- Exercises using multivariate generalized linear mixed models -- -- 12.1.
• 93- Introduction -- -- 12.1.1.
• 94- Left censoring -- -- 12.1.2.
• 95- Right censoring -- -- 12.1.3.
• 96- Time-varying explanatory variables -- -- 12.1.4.
• 97- Competing risks -- -- 12.2.
• 98- Duration data in discrete time -- -- 12.2.1.
• 99- Single-level models for duration data -- -- 12.2.2.
• 100- Two-level models for duration data -- -- 12.2.3.
• 101- Three-level models for duration data -- -- 12.3.
• 102- Renewal data -- -- 12.3.1.
• 103- Introduction -- -- 12.3.2.
• 104- Example: renewal models -- -- 12.4.
• 105- Competing risk data -- -- 12.4.1.
• 106- Introduction -- -- 12.4.2.
• 107- Likelihood -- -- 12.4.3.
• 108- Example: competing risk data -- -- 12.5.
• 109- Exercises using renewal and competing risks models -- -- 13.1.
• 110- Introduction -- -- 13.2.
• 111- Mover-stayer model -- -- 13.3.
• 112- Likelihood incorporating the mover-stayer model -- -- 13.4.
• 113- Example 1: stayers within count data -- -- 13.5.
• 114- Example 2: stayers within binary data -- -- 13.6.
• 115- Exercises: stayers -- -- 14.1.
• 116- Introduction to key issues: heterogeneity, state dependence and non-stationarity -- -- 14.2.
• 117- Example -- -- 14.3.
• 118- Random effects models -- -- 14.4.
• 119- Initial conditions problem -- -- 14.5.
• 120- Initial treatment
• 121- 14.6.
• 122- Example: depression data -- -- 14.7.
• 123- Classical conditional analysis -- -- 14.8.
• 124- Classical conditional model: example -- -- 14.9.
• 125- Conditioning on initial response but allowing random effect uol to be dependent on z3 -- -- 14.10.
• 126- Wooldridge conditional model: example -- -- 14.11.
• 127- Modelling the initial conditions -- -- 14.12.
• 128- Same random effect in the initial response and subsequent response models with a common scale parameter -- -- 14.13.
• 129- Joint analysis with a common random effect: example -- -- 14.14.
• 130- Same random effect in models of the initial response and subsequent responses but with different scale parameters -- -- 14.15.
• 131- Joint analysis with a common random effect (different scale parameters): example -- -- 14.16.
• 132- Different random effects in models of the initial response and subsequent responses -- -- 14.17.
• 133- Different random effects: example -- -- 14.18.
• 134- Embedding the Wooldridge approach in joint models for the initial response and subsequent responses -- -- 14.19.
• 135- Joint model incorporating the Wooldridge approach: example -- -- 14.20.
• 136- Other link functions -- -- 14.21.
• 137- Exercises using models incorporating initial conditions/state dependence in binary data
• 138- 15.1.
• 139- Introduction -- -- 15.2.
• 140- Fixed effects treatment of the two-level linear model -- -- 15.3.
• 141- Dummy variable specification of the fixed effects model -- -- 15.4.
• 142- Empirical comparison of two-level fixed effects and random effects estimators -- -- 15.5.
• 143- Implicit fixed effects estimator -- -- 15.6.
• 144- Random effects models -- -- 15.7.
• 145- Comparing two-level fixed effects and random effects models -- -- 15.8.
• 146- Fixed effects treatment of the three-level linear model -- -- 15.9.
• 147- Exercises comparing fixed effects and random effects -- -- A.1.
• 148- SabreR installation -- -- A.2.
• 149- SabreR commands -- -- A.2.1.
• 150- The arguments of the SabreR object -- -- A.2.2.
• 151- The anatomy of a SabreR command file -- -- A.3.
• 152- Quadrature -- -- A.3.1.
• 153- Standard Gaussian quadrature -- -- A.3.2.
• 154- Performance of Gaussian quadrature -- -- A.3.3.
• 155- Adaptive quadrature -- -- A.4.
• 156- Estimation -- -- A.4.1.
• 157- Maximizing the log likelihood of random effects models -- -- A.5.
• 158- Fixed effects linear models -- -- A.6.
• 159- Endogenous and exogenous variables -- -- B.1.
• 160- Getting started with R -- -- B.1.1.
• 161- Preliminaries -- -- B.1.1.1.
• 162- Working with R in interactive mode -- -- B.1.1.2.
• 163- Basic functions -- -- B.1.1.3.
• 164- Getting help
• 165- B.1.1.4.
• 166- Stopping R -- -- B.1.2.
• 167- Creating and manipulating data -- -- B.1.2.1.
• 168- Vectors and lists -- -- B.1.2.2.
• 169- Vectors -- -- B.1.2.3.
• 170- Vector operations -- -- B.1.2.4.
• 171- Lists -- -- B.1.2.5.
• 172- Data frames -- -- B.1.3.
• 173- Session management -- -- B.1.3.1.
• 174- Managing objects -- -- B.1.3.2.
• 175- Attaching and detaching objects -- -- B.1.3.3.
• 176- Serialization -- -- B.1.3.4.
• 177- R scripts -- -- B.1.3.5.
• 178- Batch processing -- -- B.1.4.
• 179- R packages -- -- B.1.4.1.
• 180- Loading a package into R -- -- B.1.4.2.
• 181- Installing a package for use in R -- -- B.1.4.3.
• 182- R and Statistics -- -- B.2.
• 183- Data preparation for SabreR -- -- B.2.1.
• 184- Creation of dummy variables -- -- B.2.2.
• 185- Missing values -- -- B.2.3.
• 186- Creating lagged response covariate data.

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