afex_plot(): Publication-Ready Plots for Factorial Designs

This article is originally published at http://singmann.org

I am happy to announce that a new version of afex (version 0.22-1) has appeared on CRAN. This version comes with two major changes, for more see the NEWS file. To get the new version including all packages used in the examples run:

install.packages("afex", dependencies = TRUE)

First, afex does not load or attach package emmeans automatically anymore. This reduces the package footprint and makes it more lightweight. If you want to use afex without using emmeans, you can do this now. The consequence of this is that you have to attach emmeans explicitly if you want to continue using emmeans() et al. in the same manner. Simply add library("emmeans") to the top of your script just below library("afex") and things remain unchanged. Alternatively, you can use emmeans::emmeans() without attaching the package.

Second and more importantly, I have added a new plotting function to afex. afex_plot() visualizes results from factorial experiments combining estimated marginal means and associated uncertainties (i.e., error bars) in the foreground with a depiction of the raw data in the background. Currently, afex_plots() supports ANOVAs and mixed models fitted with afex as well as mixed models fitted with lme4 (support for more models will come in the next version). As shown in the example below, afex_plots() makes it easy to produce nice looking plots that are ready to be incorporated into publications. Importantly, afex_plots() allows different types of error bars, including within-subjects confidence intervals, which makes it particularly useful for fields where such designs are very common (e.g., psychology). Furthermore, afex_plots() is built on ggplot2 and designed in a modular manner, making it easy to customize the plot to ones personal preferences.

afex_plot() requires the fitted model object as first argument and then has three arguments determining which factor or factors are displayed how:
– x is necessary and specifies the factor(s) plotted on the x-axis
– trace is optional and specifies the factor(s) plotted as separate lines (i.e., with each factor-level present at each x-axis tick)
– panel is optional and specifies the factor(s) which separate the plot into different panels.

The further arguments make it easy to customize the plot in various ways. A comprehensive overview is provided in the new vignette, further details, specifically regarding the question of which type of error bars are supported, is given on its help page (which also has many more examples).

Let us look at an example. We take data from a 3 by 2 within-subject experiment that also features prominently in the vignette. Note that we plot within-subjects confidence intervals (by setting error = "within") and then customize the plot quite a bit by changing the theme, using nicer labels, removing some y-axis ticks, adding colour, and using a customized geom (geom_boxjitter from the ggpol package) for displaying the data in the background.

library("afex") 
library("ggplot2") 
data(md_12.1)
aw <- aov_ez("id", "rt", md_12.1, within = c("angle", "noise"))

afex_plot(aw, x = "angle", trace = "noise", error = "within",
          mapping = c("shape", "fill"), dodge = 0.7,
          data_geom = ggpol::geom_boxjitter, 
          data_arg = list(
            width = 0.5, 
            jitter.width = 0,
            jitter.height = 10,
            outlier.intersect = TRUE),
          point_arg = list(size = 2.5), 
          error_arg = list(size = 1.5, width = 0),
          factor_levels = list(angle = c("0°", "4°", "8°"),
                               noise = c("Absent", "Present")), 
          legend_title = "Noise") +
  labs(y = "RTs (in ms)", x = "Angle (in degrees)") +
  scale_y_continuous(breaks=seq(400, 900, length.out = 3)) +
  theme_bw(base_size = 15) + 
  theme(legend.position="bottom", panel.grid.major.x = element_blank())

ggsave("afex_plot.png", device = "png", dpi = 600,
       width = 8.5, height = 8, units = "cm") 

In the plot, the black dots are the means and the thick black lines the 95% within-subject confidence intervals. The raw data is displayed in the background with a half box plot showing the median and upper and lower quartile as well as the raw data. The raw data is jittered on the y-axis to avoid perfect overlap.

One final thing to note. In the vignette on CRAN as well as the help page there is an error in the code. The name of the argument for changing the labels of the factor-levels is factor_levels and not new_levels. The vignette linked above and here uses the correct argument name. This is already corrected on github and will be corrected on CRAN with the next release.

Thanks for visiting r-craft.org
This article is originally published at http://singmann.org
Please visit source website for post related comments.