Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing.
This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.
"Data Analysis Using Hierarchical Generalized Linear Models with R by Lee et al is an advanced book on regression and mixed effects statistical models. The book presents a class of generalized linear models (GLMs) with random effects. In hierarchical generalized linear models (HGLMs), the random effects might enter in the location parameter, in the dispersion parameter, or in both. These extensions cover a vast number of statistical problems containing unobservable random variables, including missing data, latent variables, and predictions. The book presents an endless volume of case studies, using a bundle of R packages for implementation: hglm, dhglm, mdhglm, frailtyHL and jointdhglm. …The authors concentrate on the practical aspects of HGLMs, and show how improvements in numerical methods (e.g., Laplace approximations to integrals) allow HGLMs to be used in practice. The diversity of statistical models covered in this book is fascinating. …In general, the authors have presented a good balance between theory and practical applications in R."
-Pablo Emilio Verde, ISCB Jun2 2018
GLMs via iterative weighted least squares.
Inference for models with unobservables.
HGLMs: from Method to Algorithm.
HGLM modelling in R.
Double HGLMS - using the dhglm package.
Fitting multivariate HGLMs.