Compute marginal effects from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.
Results of regression models are typically presented as tables that are easy to understand. For more complex models that include interaction or quadratic / spline terms, tables with numbers are less helpful and difficult to interpret. In such cases, marginal effects are far easier to understand. In particular, the visualization of marginal effects allows to intuitively get the idea of how predictors and outcome are associated, even for complex models.
ggeffects computes marginal effects at the mean or average marginal effects from statistical models and returns the result as tidy data frames. These data frames are ready to use with the ggplot2-package.
Please visit https://strengejacke.github.io/ggeffects/ for documentation and vignettes. In case you want to file an issue or contribute in another way to the package, please follow this guide. For questions about the functionality, you may either contact me via email or also file an issue.
Marginal effects can be calculated for many different models. Currently supported model-objects are:
gam (package mgcv),
svyglm.nb. Other models not listed here are passed to a generic predict-function and might work as well, or maybe with
ggeffect(), which effectively does the same as
Interaction terms, splines and polynomial terms are also supported. The two main functions are
ggeffect(). There is a generic
plot()-method to plot the results using ggplot2.
The returned data frames always have the same, consistent structure and column names, so it's easy to create ggplot-plots without the need to re-write the function call.
predicted are the values for the x- and y-axis.
conf.high could be used as
ymax aesthetics for ribbons to add confidence bands to the plot.
group can be used as grouping-aesthetics, or for faceting.
ggpredict() requires at least one, but not more than three terms specified in the
terms-argument. Predicted values of the response, along the values of the first term are calucalted, optionally grouped by the other terms specified in
data(efc) fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc) ggpredict(fit, terms = "c12hour") #> # Predicted values for Total score BARTHEL INDEX #> # x = average number of hours of care per week #> #> x predicted conf.low conf.high group #> 0 75.444 73.257 77.630 1 #> 5 74.177 72.098 76.256 1 #> 10 72.911 70.931 74.890 1 #> 15 71.644 69.753 73.535 1 #> 20 70.378 68.564 72.191 1 #> 25 69.111 67.361 70.861 1 #> 30 67.845 66.144 69.545 1 #> 35 66.578 64.911 68.245 1 #> 40 65.312 63.661 66.962 1 #> 45 64.045 62.393 65.697 1 #> ... and 25 more rows.
A possible call to ggplot could look like this:
library(ggplot2) mydf <- ggpredict(fit, terms = "c12hour") ggplot(mydf, aes(x, predicted)) + geom_line() + geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .1)
However, there is also a
plot()-method. This method uses convenient defaults, to easily create the most suitable plot for the marginal effects.
mydf <- ggpredict(fit, terms = "c12hour") plot(mydf)
ggeffects has a
plot()-method with some convenient defaults, which allows quickly creating ggplot-objects.
With three variables, predictions can be grouped and faceted.
ggpredict(fit, terms = c("c12hour", "c172code", "c161sex")) #> # Predicted values for Total score BARTHEL INDEX #> # x = average number of hours of care per week #> #> x predicted conf.low conf.high group facet #> 0 74.996 71.406 78.585 low level of education  Female #> 0 73.954 69.354 78.554 low level of education  Male #> 0 75.714 73.313 78.115 intermediate level of education  Female #> 0 74.673 71.055 78.290 intermediate level of education  Male #> 0 76.432 72.887 79.977 high level of education  Female #> 0 75.391 71.040 79.741 high level of education  Male #> 5 73.729 70.219 77.239 low level of education  Female #> 5 72.688 68.143 77.233 low level of education  Male #> 5 74.447 72.146 76.748 intermediate level of education  Female #> 5 73.406 69.846 76.966 intermediate level of education  Male #> ... and 200 more rows. mydf <- ggpredict(fit, terms = c("c12hour", "c172code", "c161sex")) ggplot(mydf, aes(x = x, y = predicted, colour = group)) + stat_smooth(method = "lm", se = FALSE) + facet_wrap(~facet)
plot() works for this case, as well:
There are some more features, which are explained in more detail in the package-vignette.
Please follow this guide if you like to contribute to this package.
To install the latest development snapshot (see latest changes below), type following commands into the R console:
Please note the package dependencies when installing from GitHub. The GitHub version of this package may depend on latest GitHub versions of my other packages, so you may need to install those first, if you encounter any problems. Here's the order for installing packages from GitHub:
To install the latest stable release from CRAN, type following command into the R console:
In case you want / have to cite my package, please use
citation('ggeffects') for citation information.
print()-method, with a nicer print of the returned data frame. This method replaces the
summary()-method, which was removed.
clm2-models from the ordinal-package.
ggpredict()has improved support for
coxph-models from the survival-package (survival probabilities, cumulative hazards).
ggpredict()now has additional options,
type = "fe.zi"and
type = "re.zi", to explicitely condition zero-inflated (mixed) models on their zero-inflation component.
ggpredict()now has additional options,
type = "surv"and
type = "cumhaz", to plot probabilities of survival or cumulative hazards from
vcov.argsto calculate robust standard errors for confidence intervals of predicted values. These are based on the various
sandwich::vcov*()-functions, hence robust standard errors can be calculated for all models that are supported by
plot()-method gets two arguments
dot.size, to determine the size of the geoms.
ci-argument for the
plot()-method now also accepts the character values
"dot"to plot dashed or dotted lines as confidence bands.
ggeffect()may also be a formula, which is more convenient for typing, but less flexible than specifying the terms as character vector with specific options.
ggpredict()now automatically back-transforms predictions to the response scale for model with log-transformed response.
ggpredict()now automatically set numeric vectors with 10 or more unique values to representative values (see
rprs_values()), if these are used as second or third value in the
terms-argument (to represent a grouping structure).
rprs_values()is now exported.
pretty-argument is deprecated, because prettifying values almost always makes sense - so this is done automatically.
brmsfit-objects with categorical-family.
ggalleffect()has been removed.
ggeffect()now plots effects for all model terms if
terms = NULL.
ggpoly()have been removed, as
ggeffect()are more efficient and generic for plotting interaction or polynomial terms.
cbind()in model formula.
type = "re".
CITATIONto the publication in the Journal of Open Source Software.
pretty_range(), to create a pretty sequence of integers of a vector.
condition-argument to specify values at which covariates should be held constant, instead of their
ggpredict()now calculates more values, leading to smoother plots.
ggpredict()can now also select a range of feasible values for numeric values, e.g.
terms = "age [pretty]". In contrast to the
pretty-argument, which prettyfies all terms, you can selectively prettify specific terms with this option.
ggpredict()now also supports all shortcuts that are possible for the
gginteraction(), so for instance
term = "age [meansd]"would return three values: mean(age) - sd(age), mean(age) and mean(age) + sd(age).
plot()gets some new arguments to control which plot-title to show or hide:
log.yargument to transform the y-axis to logarithmic scale, which might be useful for binomial models with predicted probabilities, or other models with log-alike link-functions.
plot()-method for plotting all effects with
term = NULL) now allows to arrange the plot in facets (using
facets = TRUE).
plot()are now passed down to
ggplot::scale_y*(), to control the appearance of the y-axis (like
cbind(...)as response variable.
glmmTMB-objects now compute proper confidence intervals, due to fix in package glmmTMB 0.2.1
ggpredict()is missing or
NULL, marginal effects for each model term are calculated.
ggpredict()then returns a list of data frames, which can also be plotted with
plot()now accepts a numeric value between 0 and 1, to control the width of the random variation in data points.
ggeffect()can now predict transformed values, which is useful, for instance, to exponentiate predictions for
log(term)on the original scale of the variable. See package vignette, section Marginal effects at specific values or levels for examples.
ggpredict()now supports linear multivariate response models, i.e.
lm()with multiple outcomes.
pretty-argument to reduce and "prettify" the value range from variables in
termsfor predictions. This applies to all variables in
termswith more than 25 unique values.
x.as.factor-argument to preserve factor-class for the
x-column in the returned data frame.
terms-argument now also allows the specification of a range of numeric values in square brackets, e.g.
terms = "age [30:50]".
clm-models don't support
emm()did not work properly for some random effects models.
convert_case()from sjlabelled, in preparation for the latest snakecase-package update.