This section continues from “Linear Models in Experiments”. While the earlier chapters each focused on a single treatment factor, the chapters here deal with two treatment factors at once - and with what their interaction means for analysis and interpretation. We start with two factors in a familiar randomized complete block design, and then re-analyze the very same data as a split-plot design, which introduces our first practical mixed model. You should be familiar with the material from the “Linear Models in Experiments” chapters (ANOVA, estimated marginal means, compact letter displays).

Required R packages

The chapters in this section use the R packages below. Run this once to install any that are missing:

pkgs <- c(
  "tidyverse", "here", "emmeans", "multcomp", "multcompView",
  "desplot", "ggtext", "MetBrewer", "lme4", "lmerTest", "pbkrtest"
)
install.packages(setdiff(pkgs, rownames(installed.packages())))

How to navigate ..

.. through chapters

You can navigate chapters by clicking on the table of chapters on the left. Alternatively, you can always go to the next chapter by clicking on its name at the end (i.e. scroll all the way down)

.. through content within chapters

Within a chapter, you can navigate through the content by clicking on the table of contents on the right.