interact_plot plots regression lines at user-specified levels of a
moderator variable to explore interactions. The plotting is done with
ggplot2 rather than base graphics, which some similar functions use.
interact_plot(
model,
pred,
modx,
modx.values = NULL,
mod2 = NULL,
mod2.values = NULL,
centered = "all",
data = NULL,
at = NULL,
plot.points = FALSE,
interval = FALSE,
int.type = c("confidence", "prediction"),
int.width = 0.95,
outcome.scale = "response",
linearity.check = FALSE,
facet.modx = FALSE,
robust = FALSE,
cluster = NULL,
vcov = NULL,
set.offset = 1,
x.label = NULL,
y.label = NULL,
pred.labels = NULL,
modx.labels = NULL,
mod2.labels = NULL,
main.title = NULL,
legend.main = NULL,
colors = NULL,
line.thickness = 1,
vary.lty = TRUE,
point.size = 1.5,
point.shape = FALSE,
jitter = 0,
rug = FALSE,
rug.sides = "b",
partial.residuals = FALSE,
point.alpha = 0.6,
color.class = NULL,
...
)The functions returns a ggplot object, which can be treated
like a user-created plot and expanded upon as such.
A regression model. The function is tested with lm,
glm, svyglm, merMod,
rq, brmsfit,
stanreg models.
Models from other classes may work as well but are not officially
supported. The model should include the interaction of interest.
The name of the predictor variable involved
in the interaction. This can be a bare name or string. Note that it
is evaluated using rlang, so programmers can use the !! syntax
to pass variables instead of the verbatim names.
The name of the moderator variable involved
in the interaction. This can be a bare name or string. The same
rlang proviso applies as with pred.
For which values of the moderator should lines be plotted? There are two basic options:
A vector of values (e.g., c(1, 2, 3))
A single argument asking to calculate a set of values. See details below.
Default is NULL. If NULL (or mean-plus-minus),
then the customary +/- 1 standard
deviation from the mean as well as the mean itself are used for continuous
moderators. If "plus-minus", plots lines when the moderator is at
+/- 1 standard deviation without the mean. You may also choose "terciles"
to split the data into equally-sized groups and choose the point at the
mean of each of those groups.
If the moderator is a factor variable and modx.values is
NULL, each level of the factor is included. You may specify
any subset of the factor levels (e.g., c("Level 1", "Level 3")) as long
as there is more than 1. The levels will be plotted in the order you
provide them, so this can be used to reorder levels as well.
Optional. The name of the second moderator
variable involved in the interaction. This can be a bare name or string.
The same rlang proviso applies as with pred.
For which values of the second moderator should the plot
be
facetted by? That is, there will be a separate plot for each level of this
moderator. Defaults are the same as modx.values.
A vector of quoted variable names that are to be
mean-centered. If "all", all non-focal predictors are centered. You
may instead pass a character vector of variables to center. User can
also use "none" to base all predictions on variables set at 0.
The response variable, pred, modx, and mod2 variables are never
centered.
Optional, default is NULL. You may provide the data used to
fit the model. This can be a better way to get mean values for centering
and can be crucial for models with variable transformations in the formula
(e.g., log(x)) or polynomial terms (e.g., poly(x, 2)). You will
see a warning if the function detects problems that would likely be
solved by providing the data with this argument and the function will
attempt to retrieve the original data from the global environment.
If you want to manually set the values of other variables in the model, do so by providing a named list where the names are the variables and the list values are vectors of the values. This can be useful especially when you are exploring interactions or other conditional predictions.
Logical. If TRUE, plots the actual data points as
a scatterplot on top of the interaction lines. The color of the dots will
be based on their moderator value.
Logical. If TRUE, plots confidence/prediction
intervals around the line using geom_ribbon.
Type of interval to plot. Options are "confidence" or "prediction". Default is confidence interval.
How large should the interval be, relative to the standard error? The default, .95, corresponds to roughly 1.96 standard errors and a .05 alpha level for values outside the range. In other words, for a confidence interval, .95 is analogous to a 95% confidence interval.
For nonlinear models (i.e., GLMs), should the outcome
variable be plotted on the link scale (e.g., log odds for logit models) or
the original scale (e.g., predicted probabilities for logit models)? The
default is "response", which is the original scale. For the link
scale, which will show straight lines rather than curves, use
"link".
For two-way interactions only. If TRUE, plots a
pane for each level of the moderator and superimposes a loess smoothed
line (in gray) over the plot. This enables you to see if the effect is
linear through the span of the moderator. See Hainmueller et al. (2016) in
the references for more details on the intuition behind this. It is
recommended that you also set plot.points = TRUE and use
modx.values = "terciles" with this option.
Create separate panels for each level of the moderator?
Default is FALSE, except when linearity.check is TRUE.
Should robust standard errors be used to find confidence
intervals for supported models? Default is FALSE, but you should specify
the type of sandwich standard errors if you'd like to use them (i.e.,
"HC0", "HC1", and so on). If TRUE, defaults to "HC3" standard
errors.
For clustered standard errors, provide the column name of the cluster variable in the input data frame (as a string). Alternately, provide a vector of clusters.
Optional. You may supply the variance-covariance matrix of the coefficients yourself. This is useful if you are using some method for robust standard error calculation not supported by the sandwich package.
For models with an offset (e.g., Poisson models), sets an offset for the predicted values. All predicted values will have the same offset. By default, this is set to 1, which makes the predicted values a proportion. See details for more about offset support.
A character object specifying the desired x-axis label. If
NULL, the variable name is used.
A character object specifying the desired x-axis label. If
NULL, the variable name is used.
A character vector of 2 labels for the predictor if it is
a 2-level factor or a continuous variable with only 2 values. If
NULL, the default, the factor labels are used.
A character vector of labels for each level of the
moderator values, provided in the same order as the modx.values
argument. If NULL, the values themselves are used as labels unless
modx,values is also NULL. In that case, "+1 SD" and "-1 SD"
are used.
A character vector of labels for each level of the 2nd
moderator values, provided in the same order as the mod2.values
argument. If NULL, the values themselves are used as labels unless
mod2.values is also NULL. In that case, "+1 SD" and "-1 SD"
are used.
A character object that will be used as an overall title
for the plot. If NULL, no main title is used.
A character object that will be used as the title that
appears above the legend. If NULL, the name of the moderating
variable is used.
See jtools_colors for details on the types of arguments
accepted. Default is "CUD Bright" for factor
moderators, "Blues" for +/- SD and user-specified modx.values
values.
How thick should the plotted lines be? Default is 1.
Should the resulting plot have different shapes for each
line in addition to colors? Defaults to TRUE.
What size should be used for observed data when
plot.points is TRUE? Default is 1.5.
For plotted points---either of observed data or predicted values with the "point" or "line" geoms---should the shape of the points vary by the values of the factor? This is especially useful if you aim to be black and white printing- or colorblind-friendly.
How much should plot.points observed values be "jittered"
via ggplot2::position_jitter()? When there are many points near each
other, jittering moves them a small amount to keep them from
totally overlapping. In some cases, though, it can add confusion since
it may make points appear to be outside the boundaries of observed
values or cause other visual issues. Default is 0, but try various
small values (e.g., 0.1) and increase as needed if your points are
overlapping too much. If the argument is a vector with two values,
then the first is assumed to be the jitter for width and the second
for the height.
Show a rug plot in the margins? This uses ggplot2::geom_rug()
to show the distribution of the predictor (top/bottom) and/or
response variable (left/right) in the original data. Default is
FALSE.
On which sides should rug plots appear? Default is "b", meaning bottom. "t" and/or "b" show the distribution of the predictor while "l" and/or "r" show the distribution of the response. "bl" is a good option to show both the predictor and response.
Instead of plotting the observed data, you may plot
the partial residuals (controlling for the effects of variables besides
pred).
What should the alpha aesthetic for plotted points of
observed data be? Default is 0.6, and it can range from 0 (transparent) to
1 (opaque).
Deprecated. Now known as colors.
extra arguments passed to make_predictions
Jacob Long <long.1377@osu.edu>
This function provides a means for plotting conditional effects for the purpose of exploring interactions in regression models.
The function is designed for two and three-way interactions. For additional terms, the effects package may be better suited to the task.
This function supports nonlinear and generalized linear models and by
default will plot them on their original scale
(outcome.scale = "response"). To plot them on the linear scale,
use "link" for outcome.scale.
While mixed effects models from lme4 are supported, only the fixed
effects are plotted. lme4 does not provide confidence intervals,
so they are not supported with this function either.
Note: to use transformed predictors, e.g., log(variable),
put its name in quotes or backticks in the argument.
Details on how observed data are split in multi-pane plots:
If you set plot.points = TRUE and request a multi-pane (facetted) plot
either with a second moderator, linearity.check = TRUE, or
facet.modx = TRUE, the observed
data are split into as many groups as there are panes and plotted
separately. If the moderator is a factor, then the way this happens will
be very intuitive since it's obvious which values go in which pane. The
rest of this section will address the case of continuous moderators.
My recommendation is that you use modx.values = "terciles" or
mod2.values = "terciles" when you want to plot observed data on
multi-pane
plots. When you do, the data are split into three approximately
equal-sized groups with the lowest third, middle third, and highest third
of the data split accordingly. You can replicate this procedure using
Hmisc::cut2() with g = 3 from the Hmisc package. Sometimes, the
groups will not be equal in size because the number of observations is
not divisible by 3 and/or there are multiple observations with the same
value at one of the cut points.
Otherwise, a more ad hoc procedure is used to split the data. Quantiles
are found for each mod2.values or modx.values value. These are not the
quantiles used to split the data, however, since we want the plotted lines
to represent the slope at a typical value in the group. The next step,
then, is to take the mean of each pair of neighboring quantiles and use
these as the cut points.
For example, if the mod2.values are at the 25th, 50th, and 75th
percentiles
of the distribution of the moderator, the data will be split at the
37.5th and and 62.5th percentiles. When the variable is
normally distributed, this will correspond fairly closely to using
terciles.
Info about offsets:
Offsets are partially supported by this function with important
limitations. First of all, only a single offset per model is supported.
Second, it is best in general to specify offsets with the offset argument
of the model fitting function rather than in the formula. You are much
more likely to have success if you provide the data used to fit the model
with the data argument.
Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373-400. tools:::Rd_expr_doi("10.1207/s15327906mbr4003_5")
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analyses for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Hainmueller, J., Mummolo, J., & Xu, Y. (2016). How much should we trust estimates from multiplicative interaction models? Simple tools to improve empirical practice. SSRN Electronic Journal. tools:::Rd_expr_doi("10.2139/ssrn.2739221")
plotSlopes from rockchalk performs a
similar function, but
with R's base graphics---this function is meant, in part, to emulate
its features.
sim_slopes performs a simple slopes analysis with a similar
argument syntax to this function.
# Using a fitted lm model
states <- as.data.frame(state.x77)
states$HSGrad <- states$`HS Grad`
fit <- lm(Income ~ HSGrad + Murder * Illiteracy, data = states)
interact_plot(model = fit, pred = Murder, modx = Illiteracy)
# Using interval feature
fit <- lm(accel ~ mag * dist, data = attenu)
interact_plot(fit, pred = mag, modx = dist, interval = TRUE,
int.type = "confidence", int.width = .8)
# Using second moderator
fit <- lm(Income ~ HSGrad * Murder * Illiteracy, data = states)
interact_plot(model = fit, pred = Murder, modx = Illiteracy, mod2 = HSGrad)
# With svyglm
if (requireNamespace("survey")) {
library(survey)
data(api)
dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw,
data = apistrat, fpc = ~fpc)
regmodel <- svyglm(api00 ~ ell * meals, design = dstrat)
interact_plot(regmodel, pred = ell, modx = meals)
}
# With lme4
if (FALSE) {
library(lme4)
data(VerbAgg)
mv <- glmer(r2 ~ Anger * mode + (1 | item), data = VerbAgg,
family = binomial,
control = glmerControl("bobyqa"))
interact_plot(mv, pred = Anger, modx = mode)
}
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