sim_slopes()no longer fails getting Johnson-Neyman intervals for
cat_plot()no longer ignores
pred.labelsarguments. Thanks to Paul Djupe for alerting me to this.
sim_slopesobjects no longer returns numbers as strings. This had downstream effects on, e.g., the
sim_slopes. (#22; thanks to Noah Greifer)
lmerModTestobjects properly. Thanks to Eric Shuman for bringing it to my attention.
This is, as the name suggests, related to
sim_slopes(). However, instead of slopes, what is being estimated are marginal effects. In the case of OLS linear regression, this is basically the same thing. The slope in OLS is the expected change in the outcome for each 1-unit increase in the predictor. For other models, however, the actual change in the outcome when there’s a 1-unit increase in a variable depends on the level of other covariates and the initial value of the predictor. In a logit model, for instance, the change in probability will be different if the initial probability was 50% (could go quite a bit up or down) than if it was 99.9% (can’t go up).
sim_margins() uses the
margins package under the hood to estimate marginal effects. Unlike
sim_slopes(), in which by default all covariates not involved in the interaction are mean-centered, in
sim_margins() these covariates are always left at their observed values because they influence the level of the marginal effect. Instead, the marginal effect is calculated with the covariates and focal predictor (
pred) at their observed values and the moderator(s) held at the specified values (e.g., the mean and 1 standard deviation above/below the mean). I advise using
sim_margins() rather than
sim_slopes() when analyzing models other than OLS regression.
cat_plot()now respect the user’s selection of
outcome.scale; in 1.0.0, it always plotted on the response scale. (#12)
modx.valuesargument is now better documented to explain that you may use it to specify the exact values you want. Thanks to Jakub Lysek for asking the question that prompted this. (#8)
"mean-plus-minus"as a manual specification of the default auto-calculated values for continuous moderators.
NULLstill defaults to this, but you can now make this explicit in your code if desired for clarity or to guard against future changes in the default behavior.
mod2.valuesinclude values outside the observed range of the
mod2are not all involved in an interaction with each other in the provided model. (#10)
mod2.valuesarguments but now works properly. (#17)
sim_slopes()now handles non-syntactic variable names better.
interactionsnow requires you to have a relatively new version of
rlang. Users with older versions were experiencing cryptic errors. (#15)
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_plot()now 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_plot()now 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_slopes()no 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.
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.