Plotting two-way interactions from mixed-effects models using alias variables
Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package
sjPlot (Lüdecke, 2022). In Bernabeu (2022), the sjPlot function called
plot_model served as the basis for the creation of some custom functions. One of these functions is
alias_interaction_plot, which allows the plotting of interactions between a continuous variable and a categorical variable. Importantly, the categorical variable is replaced with an alias variable. This feature allows the back-transformation of the categorical variable to facilitate the communication of the results, for instance, when the categorical variable was sum-coded, which has been recommended for mixed-effects models (Brauer & Curtin, 2018).
Below, we’ll use the function with a model fitted using
lmerTest (Kuznetsova et al., 2022), although the function also works with several other models (see sjPlot manual). The plot can be reproduced using the materials at https://osf.io/gt5uf.
Alias interaction plot
Bernabeu, P. (2022). Language and sensorimotor simulation in conceptual processing: Multilevel analysis and statistical power. Lancaster University. https://doi.org/10.17635/lancaster/thesis/1795
Brauer, M., & Curtin, J. J. (2018). Linear mixed-effects models and the analysis of nonindependent data: A unified framework to analyze categorical and continuous independent variables that vary within-subjects and/or within-items. Psychological Methods, 23(3), 389–411. https://doi.org/10.1037/met0000159
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2022). Package ’lmerTest’. CRAN. https://cran.r-project.org/web/packages/lmerTest/lmerTest.pdf
Lüdecke, D. (2022). Package ’sjPlot’. CRAN. https://cran.r-project.org/web/packages/sjPlot/sjPlot.pdf