Data Analysis Using Hierarchical Generalized Linear Models with R

Youngjo Lee, Lars Ronnegard, Maengseok Noh

June 7, 2017 by Chapman and Hall/CRC
Reference - 322 Pages - 71 B/W Illustrations
ISBN 9781138627826 - CAT# K32000


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  • Provides a practical overview of HGLMs
  • Includes brief overviews of the methodology with references for theoretical details
  • Features detailed worked examples using a range of real data
  • R code for implementation is fully-integrated into the text
  • Supplemented by an R package on CRAN including datasets


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.