Course Syllabus

Lessons

See the main course page for more details on the schedule, and the worksheets and datasets page for information on accessing or downloading the worksheets and example datasets. All times are in Eastern Standard Time.

Time	Lesson (full text version)	Slides
Day 1, 9:15-10:15 AM	Lesson 1: R Boot Camp: the very basics	Slides
Day 2, 10:45-11:45 AM	Lesson 2: R Boot Camp: working with data frames	Slides
Day 3, 12:00-1:00 PM	Lesson 3: From linear model to linear mixed model	Slides
Day 2, 9:15-10:15 AM	Lesson 4: Going further with mixed models	Slides
Day 2, 10:45-11:45 AM	Lesson 5: Generalized linear mixed models	Slides
Day 2, 12:00-1:00 PM	Lesson 6: Estimating and comparing treatment means	Slides

Conceptual learning objectives

At the end of the workshop, students will be able to . . .

• Conceptually understand all components of a linear mixed model (LMM), including:
• Fixed effects
• Random intercepts and random slopes
• Estimated marginal means
• Understand the difference between a linear mixed model (LMM) and generalized linear mixed model (GLMM)
• Interpret the output of a LMM and GLMM

Practical learning objectives

At the end of the workshop, students will be able to . . .

• Read data into R
• Do simple manipulations of R data frames
• Use the R modeling package lme4 to fit LMM and GLMM models
• Generate predictions from a fitted model, including estimated marginal means
• Test specific hypotheses with contrasts

R packages we will learn about

• “tidyverse” packages especially readr, dplyr, and tidyr (read and manipulate data)
• lme4 (fit models)
• lmerTest (do ANOVA tests on mixed models)
• easystats (make diagnostic plots to test model assumptions)
• emmeans (estimate treatment effects and test hypotheses)
• ggplot2 (make publication-quality graphics)