Bugfixes:
sim_slopes()
no longer fails getting Johnson-Neyman intervals for merMod
models. (#20)cat_plot()
no longer ignores pred.values
and pred.labels
arguments. Thanks to Paul Djupe for alerting me to this.tidy()
method for sim_slopes
objects no longer returns numbers as strings. This had downstream effects on, e.g., the plot()
method for sim_slopes
. (#22; thanks to Noah Greifer)sim_slopes()
now handles lmerModTest
objects properly. Thanks to Eric Shuman for bringing it to my attention.sim_margins()
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.
interact_plot()
and cat_plot()
now respect the user’s selection of outcome.scale
; in 1.0.0, it always plotted on the response scale. (#12)modx.values
argument 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)modx.values
now accepts "mean-plus-minus"
as a manual specification of the default auto-calculated values for continuous moderators. NULL
still 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.modx.values
or mod2.values
include values outside the observed range of the modx
/mod2
. (#9)pred
, modx
, and mod2
are not all involved in an interaction with each other in the provided model. (#10)cat_plot()
was ignoring mod2.values
arguments but now works properly. (#17)interact_plot()
and cat_plot()
.sim_slopes()
now handles non-syntactic variable names better.interactions
now requires you to have a relatively new version of rlang
. Users with older versions were experiencing cryptic errors. (#15)interact_plot()
and cat_plot()
now have an at
argument for more granular control over the values of covariates.sim_slopes()
now allows for custom specification of robust standard error estimators via providing a function to v.cov
and arguments to v.cov.args
.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 jtools
.
interactions
now have a new theme, which you can use yourself, called theme_nice()
(from the jtools
package). 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.interact_plot()
and cat_plot()
now use tidy evaluation for the pred
, modx
, and mod2
arguments. This means you can pass a variable that contains the name of pred
/modx
/mod2
, which is most useful if you are creating a function, for loop, etc. If using a variable, put a !!
from the rlang
package 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.
You can plot partial residuals instead of the observed data in interact_plot()
and cat_plot()
via the argument partial.residuals = TRUE
.
make_predictions()
and removal of plot_predictions()
In the jtools
1.0.0 release, I introduced make_predictions()
as a lower-level way to emulate the functionality of effect_plot()
, interact_plot()
, and 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 modx
or 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 predictions
/margins
packages. See the jtools
documentation for more info on this revised syntax.