16 Additional Resources
If you have read through this guide, you can see how flexible LMMs are for addressing a broad array of analytical scenarios and in particular, are advantageous when there is unbalanced data due to missing data, unequal replication or due to the experimental design itself. However, LMMs can be easily misspecified when you are a new LMM user, so it helps to have a deeper understanding of how they work. Below are some resources than can help expand your understanding.
16.1 Further Reading on Mixed Models
Mixed-Effects Models in S and S-PLUS thee book for nlme, by José C. Pinheiro and Douglas M. Bates (2000). We used this book extensively for developing this guide. Sadly, it’s out of print, and we could not find a free copy online. However, there are affordable used copies available; some school libraries also carry this.
Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill (2006). Good general introduction with a major focus on Bayesian models. Free PDF
Mixed Effects Models and Extensions in Ecology with R by Alain F. Zuur, Elena N. Ieno, Neil Walker, Anatoly A. Saveliev, and Graham M. Smith (2009).
ANOVA and Mixed Models by Lukas Meier (2022).