CCP | Linear mixed-effects modeling in R
Linear mixed-effects modeling in R

Aki-Juhani Kyröläinen (McMaster University) & Vincent Porretta (University of Windsor)

Introduction to linear mixed-effects modeling in R

In recent years, linear mixed modeling has become one of the primary forms of analysis for experimental data. This course is designed to provide participants with the conceptual and practical knowledge needed to carry out mixed-effects regression (both linear and logistic) using lme4 in R. In doing so, participants will gain experience and confidence in 1) data verification and visualization; 2) implementing linear mixed-effects regression; and 3) interpreting and reporting the results. A working understanding/command of R is a prerequisite for this course. It would be helpful for participants to bring their own laptop.

Please note: If you would like to obtain a certificate for this course, there will be a final assessment (specified in the course).

Proposed readings:

1) Zuur, A.F., Ieno, E.N., & Elphick, C.S. (2010) A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1(1), 3–14, doi:10.1111/j.2041-210X.2009.00001.x

2) Baayen, R. H. and Milin, P. (2010) Analyzing reaction times. International Journal of Psychological Research, 3.2, 12–28. doi:http:10.21500/20112084.807

3) Jaeger, T.F. (2008) Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models. Journal of Memory and Language 59, 434–446. doi:10.1016/j.jml.2007.11.007