Comprehensive, User-Friendly Toolkit for Probing Interactions
A suite of functions for conducting and interpreting analysis
of statistical interaction in regression models that was formerly part of the
'jtools' package. Functionality includes visualization of two- and three-way
interactions among continuous and/or categorical variables as well as
calculation of "simple slopes" and Johnson-Neyman intervals (see e.g.,
Bauer & Curran, 2005 <doi:10.1207/s15327906mbr4003_5>). These
capabilities are implemented for generalized linear models in addition to the
standard linear regression context.
This package consists of a number of tools that pertain to the analysis
and exploration of statistical interactions in the context of
regression. Some of these features, especially those that pertain to
visualization, are not exactly impossible to do oneself but are tedious
and error-prone when done “by hand.” Most things in
once part of the
jtools package and
have been spun off to this package for clarity and simplicity.
Quick rundown of features:
- simple slopes analysis
- calculation of Johnson-Neyman intervals
- visualization of predicted and observed values using
All of these are implemented in a consistent interface designed to be as
simple as possible with tweaks and guts available to advanced users.
GLMs, models from the
survey package, and multilevel models from
lme4 are fully supported as is visualization for Bayesian models from
The package is now available via CRAN.
Unless you have a really keen eye and good familiarity with both the underlying mathematics and the scale of your variables, it can be very difficult to look at the output of regression model that includes an interaction and actually understand what the model is telling you.
This package contains several means of aiding understanding and doing statistical inference with interactions.
Johnson-Neyman intervals and simple slopes analysis
The “classic” way of probing an interaction effect is to calculate the slope of the focal predictor at different values of the moderator. When the moderator is binary, this is especially informative—e.g., what is the slope for men vs. women? But you can also arbitrarily choose points for continuous moderators.
With that said, the more statistically rigorous way to explore these effects is to find the Johnson-Neyman interval, which tells you the range of values of the moderator in which the slope of the predictor is significant vs. nonsignificant at a specified alpha level.
sim_slopes function will by default find the Johnson-Neyman
interval and tell you the predictor’s slope at specified values of the
moderator; by default either both values of binary predictors or the
mean and the mean +/- one standard deviation for continuous moderators.
library(interactions) fiti <- lm(mpg ~ hp * wt, data = mtcars) sim_slopes(fiti, pred = hp, modx = wt, jnplot = TRUE)
#> JOHNSON-NEYMAN INTERVAL #> #> When wt is OUTSIDE the interval [3.69, 5.90], the slope of hp is p < #> .05. #> #> Note: The range of observed values of wt is [1.51, 5.42]
#> SIMPLE SLOPES ANALYSIS #> #> Slope of hp when wt = 2.24 (- 1 SD): #> #> Est. S.E. t val. p #> ------- ------ -------- ------ #> -0.06 0.01 -5.66 0.00 #> #> Slope of hp when wt = 3.22 (Mean): #> #> Est. S.E. t val. p #> ------- ------ -------- ------ #> -0.03 0.01 -4.07 0.00 #> #> Slope of hp when wt = 4.20 (+ 1 SD): #> #> Est. S.E. t val. p #> ------- ------ -------- ------ #> -0.00 0.01 -0.31 0.76
The Johnson-Neyman plot can really help you get a handle on what the
interval is telling you, too. Note that you can look at the
Johnson-Neyman interval directly with the
The above all generalize to three-way interactions, too.
Visualizing interaction effects
This function plots two- and three-way interactions using
a similar interface to the aforementioned
sim_slopes function. Users
can customize the appearance with familiar
ggplot2 commands. It
supports several customizations, like confidence intervals.
interact_plot(fiti, pred = hp, modx = wt, interval = TRUE)
You can also plot the observed data for comparison:
interact_plot(fiti, pred = hp, modx = wt, plot.points = TRUE)
The function also supports categorical moderators—plotting observed data in these cases can reveal striking patterns.
fitiris <- lm(Petal.Length ~ Petal.Width * Species, data = iris) interact_plot(fitiris, pred = Petal.Width, modx = Species, plot.points = TRUE)
You may also combine the plotting and simple slopes functions by using
probe_interaction, which calls both functions simultaneously.
Categorical by categorical interactions can be investigated using the
I’m happy to receive bug reports, suggestions, questions, and (most of all) contributions to fix problems and add features. I prefer you use the Github issues system over trying to reach out to me in other ways. Pull requests for contributions are encouraged.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
The source code of this package is licensed under the MIT License.
Functions in interactions
|as_huxtable.sim_slopes||Create tabular output for simple slopes analysis|
|interact_plot||Plot interaction effects in regression models|
|probe_interaction||Probe interaction effects via simple slopes and plotting|
|sim_margins||Perform a simple margins analysis.|
|as_huxtable.sim_margins||Create tabular output for simple margins analysis|
|reexports||Objects exported from other packages|
|cat_plot||Plot interaction effects between categorical predictors.|
|plot.sim_slopes||Plot coefficients from simple slopes analysis|
|johnson_neyman||Calculate Johnson-Neyman intervals for 2-way interactions|
|tidy.sim_slopes||Tidiers for sim_slopes() objects.|
|plot.sim_margins||Plot coefficients from simple slopes analysis|
|sim_slopes||Perform a simple slopes analysis.|
|tidy.sim_margins||Tidiers for sim_margins() objects.|
Vignettes of interactions
Last month downloads
|License||MIT + file LICENSE|
|Packaged||2019-07-04 22:39:16 UTC; jlong|
|Date/Publication||2019-07-05 07:30:23 UTC|
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