Multilevel Modeling Using R

Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

by W. Holmes Finch, Jocelyn E. Bolin

Multilevel Modelling using R  provides a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. The book concludes with Bayesian fitting of multilevel models.
Complete data sets for the book can be found on the book's website www.mlminr.com/

Kindle Edition

First published December 26, 2013

Reviews

[Terran M · Rating 2 out of 5]

This brief book is designed in the model of a practitioner's guide with just enough theory to understand how to call and interpret the R functions. Unfortunately, it partially fails in this; the mathematical background it provides is too thin to explain several key concepts and they are glossed over.

For example, when explaining why to use multilevel models, the book compares them to a strawman of not including the groupings at all; a more meaningful comparison would have been to a linear model which included the grouping variable as a factor. A better explanation is provided for free in one paragraph in section 2.2 of the lmer vignette documentation.

As another example, in describing the lme4 syntax, the book explains how to specify that random slopes are correlated or uncorrelated, but does not explain what that actually means, what it translates to in equations, or how it actually impacts the model fit. The term "shrinkage" is never mentioned.

I would recommend the multilevel modeling book by Gelman and Hill instead, or the unfinished online PDF by Doug Bates.