This is the first release, but a look at the NEWS for
jtools prior to its version 2.0.0 will give you an idea of the history of the functions in this package.
What follows is an accounting of changes to functions in this package since they were last in
interactionsnow have a new theme, which you can use yourself, called
jtoolspackage). The previous default,
theme_apa, is still available but I don’t like it as a default since I don’t think the APA has defined the nicest-looking design guidelines for general use.
interact_plotnow has appropriate coloring for observed data when the moderator is numeric (#1). In previous versions I had to use a workaround that involved tweaking the alpha of the observed data points.
cat_plotnow use tidy evaluation for the
mod2arguments. This means you can pass a variable that contains the name of
mod2, which is most useful if you are creating a function, for loop, etc. If using a variable, put a
rlangpackage before it (e.g.,
pred = !! variable). For most users, these changes will not affect their usage.
sim_slopesno longer prints coefficient tables as data frames because this caused RStudio notebook users issues with the output not being printed to the console and having the notebook format them in less-than-ideal ways. The tables now have a markdown format that might remind you of Stata’s coefficient tables. Thanks to Kim Henry for contacting me about this.
One negative when visualizing predictions alongside original data with
interact_plot or similar tools is that the observed data may be too spread out to pick up on any patterns. However, sometimes your model is controlling for the causes of this scattering, especially with multilevel models that have random intercepts. Partial residuals include the effects of all the controlled-for variables and let you see how well your model performs with all of those things accounted for.
You can plot partial residuals instead of the observed data in
cat_plot via the argument
partial.residuals = TRUE.
jtools 1.0.0 release, I introduced
make_predictions as a lower-level way to emulate the functionality of
cat_plot. This would return a list object with predicted data, the original data, and a bunch of attributes containing information about how to plot it. One could then take this object, with class
predictions, and use it as the main argument to
plot_predictions, which was another new function that creates the plots you would see in
effect_plot et al.
I have simplified
make_predictions to be less specific to those plotting functions and eliminated
plot_predictions, which was ultimately too complex to maintain and caused problems for separating the interaction tools into a separate package.
make_predictions by default simply creates a new data frame of predicted values along a
pred variable. It no longer accepts
mod2 arguments. Instead, it accepts an argument called
at where a user can specify any number of variables and values to generate predictions at. This syntax is designed to be similar to the
margins packages. See the
jtools documentation for more info on this revised syntax.