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A special thanks to Github user @olivroy for contributing a number of mostly under-the-hood updates prior to this release.
Enhancements:
sim_slopes() now supports non-continuous variables in
the pred argument.sim_slopes() now has an at argument,
allowing you to specify an exact, perhaps non-centered, level for
variables not involved in the interaction.interact_plot() now has provisional support for factor
predictors (pred). Users will receive a message because of
the possibility for unexpected behavior. cat_plot()
likewise has support for continuous moderators. (#54)johnson_neyman() plots via the arguments
y.label and modx.label. (#56)panelr package are better
supported.Bug fixes:
johnson_neyman() now handles non-syntactic variable
names for modx correctly. (#56)sim_slopes() no longer displays results with factor
moderators in the reverse order of the factor’s levels. (#55)probe_interactions() no longer errors when certain
combinations of arguments are provided. (#50)sim_slopes() no longer errors when ordered factors are
moderators. Thanks to Jonathan Zadra for suggesting the fix. (#42)sim_slopes() no longer errors when given
merModTest objects.Bugfix:
sim_slopes() now correctly handles the
robust argument when it is not set to TRUE or
FALSE. Many thanks to Andy Field for reporting the issue.
(#36)Minor fix:
Bugfixes:
modx.values
and mod2.values arguments. (#29)interact_plot() no longer ignores the
point.alpha argument. (#25)modx.values or mod2.values by passing a named
vector to those arguments. (#30)sim_slopes() now prints labels when requested with the
modx.labels or mod2.labels arguments.
(#32)Feature update:
brmsfit objects,
in particular those with multiple dependent variables and distributional
dependent variables. Use the resp and dpar
arguments to specify which you want to use.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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.