Data analysis using hierarchical generalized linear models with R

Contents
Notation
Preface
Introduction
	Motivating examples
	Regarding criticisms of the h-likelihood
	R code
	Exercises
GLMs via iterative weighted least squares
	Examples
	R code
	Fisher\'s classical likelihood
	Iterative weighted least squares
	Model checking using residual plots
	Hat values
	Exercises
Inference for models with unobservables
	Examples
	R code
	Likelihood inference for random eects
	Extended likelihood principle
	Laplace approximations for the integrals
	Street magician
	H-likelihood and empirical Bayes
	Exercises
HGLMs - from method to algorithm
	Examples
	R code
	IWLS algorithm for interconnected GLMs
	IWLS algorithm for augmented GLM
	A includes parameters. For instance, splines and the Intrinsic Autore-
	IWLS algorithm for HGLMs
	Estimation algorithm for a Poisson GLMM
	H and D. The updating of the parameters  = (; v)
	Exercises
HGLMs modeling in R
	Examples
	R code
	Exercises
Double HGLMs - using the dhglm package
	Model description for DHGLMs
	Examples
	An extension of linear mixed models via DHGLM
	Implementation in the dhglm package
	Exercises
Fitting multivariate HGLMs
	Examples
	Implementation in the mdhglm package
	Exercises
Survival analysis
	Examples
	Competing risk models
	Comparison with alternative R procedures
	H-likelihood theory for the frailty model
	Running the frailtyHL package
	Exercises
Joint models
	Examples
	H-likelihood approach to joint models
	(S1) Estimation of xed and random eects  = (
	(S2) Estimation of dispersion parameters   = (; ; 
)
	Exercises
Further topics
	Examples
	Variable selections
	Examples
	Hypothesis testing
Refs
Data Index
Index