# How to break down colour variable in sjPlot::plot_model into equally-sized bins

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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). For instance, using the `plot_model`

function, I plotted the interaction between two continuous variables.

library(lme4) #> Loading required package: Matrix library(sjPlot) #> Learn more about sjPlot with 'browseVignettes("sjPlot")'. library(ggplot2) theme_set(theme_sjplot()) # Create data partially based on code by Ben Bolker # from https://stackoverflow.com/a/38296264/7050882 set.seed(101) spin = runif(800, 1, 24) trait = rep(1:40, each = 20) ID = rep(1:80, each = 10) testdata <- data.frame(spin, trait, ID) testdata$fatigue <- testdata$spin * testdata$trait / rnorm(800, mean = 6, sd = 2) # Model fit = lmer(fatigue ~ spin * trait + (1|ID), data = testdata, REML = TRUE) #> boundary (singular) fit: see help('isSingular') plot_model(fit, type = 'pred', terms = c('spin', 'trait')) #> Warning: Ignoring unknown parameters: linewidth

^{Created on 2023-06-24 with reprex v2.0.2}

However, I needed an extra feature, as sjPlot by default breaks down the colour (`fill`

) variable into few levels that do not include the minimum or the maximum values in my variable. What I would like to do is to stratify the colour variable into equally-sized levels that include the minimum and the maximum values.

Furthermore, in the legend, I would also like to display the number of levels of a grouping variable (`ID`

) that are contained in each level of the colour variable.

Below is a solution using custom functions called `deciles_interaction_plot`

and `sextiles_interaction_plot`

.

library(lme4) #> Loading required package: Matrix library(sjPlot) library(ggplot2) theme_set(theme_sjplot()) # Create data partially based on code by Ben Bolker # from https://stackoverflow.com/a/38296264/7050882 set.seed(101) spin = runif(800, 1, 24) trait = rep(1:40, each = 20) ID = rep(1:80, each = 10) testdata <- data.frame(spin, trait, ID) testdata$fatigue <- testdata$spin * testdata$trait / rnorm(800, mean = 6, sd = 2) # Model fit = lmer(fatigue ~ spin * trait + (1|ID), data = testdata, REML = TRUE) #> boundary (singular) fit: see help('isSingular') # plot_model(fit, type = 'pred', terms = c('spin', 'trait')) # Binning the colour variable into ten levels (deciles) # Read in function from GitHub source('https://raw.githubusercontent.com/pablobernabeu/language-sensorimotor-simulation-PhD-thesis/main/R_functions/deciles_interaction_plot.R') deciles_interaction_plot( model = fit, x = 'spin', fill = 'trait', fill_nesting_factor = 'ID' ) #> Loading required package: dplyr #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union #> Loading required package: RColorBrewer #> Loading required package: ggtext #> Loading required package: Cairo #> Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9 #> Returning the palette you asked for with that many colors #> Warning: Ignoring unknown parameters: linewidth #> Scale for 'y' is already present. Adding another scale for 'y', which will #> replace the existing scale. #> Scale for 'colour' is already present. Adding another scale for 'colour', #> which will replace the existing scale.

# If you wanted or needed to make six levels (sextiles) instead # of ten, you could use the function sextiles_interaction_plot. # Read in function from GitHub source('https://raw.githubusercontent.com/pablobernabeu/language-sensorimotor-simulation-PhD-thesis/main/R_functions/sextiles_interaction_plot.R') sextiles_interaction_plot( model = fit, x = 'spin', fill = 'trait', fill_nesting_factor = 'ID' ) #> Warning: Ignoring unknown parameters: linewidth #> Scale for 'y' is already present. Adding another scale for 'y', which will #> replace the existing scale. #> Scale for 'colour' is already present. Adding another scale for 'colour', #> which will replace the existing scale.

^{Created on 2023-06-24 with reprex v2.0.2}

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