This package 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. Marginal effects can be calculated for many different models. Currently supported model-objects are: lm, glm, glm.nb, lme, lmer, glmer, glmer.nb, nlmer, glmmTMB, gam, vgam, gamm, gamm4, betareg, truncreg, coxph, gls, gee, plm, lrm, polr, zeroinfl, hurdle, stanreg, svyglm and 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 ggpredict().

Interaction terms, splines and polynomial terms are also supported. The two main functions are ggpredict() and ggaverage(), however, there are some convenient wrapper-functions especially for polynomials or interactions. 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. x and predicted are the values for the x- and y-axis. conf.low and conf.high could be used as ymin and 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 terms.

data(efc)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)

ggpredict(fit, terms = "c12hour")
#> # A tibble: 62 × 6
#>        x predicted conf.low conf.high  group
#>    <dbl>     <dbl>    <dbl>     <dbl> <fctr>
#> 1      4  74.43040 72.33073  76.53006      1
#> 2      5  74.17710 72.09831  76.25588      1
#> 3      6  73.92379 71.86555  75.98204      1
#> 4      7  73.67049 71.63242  75.70857      1
#> 5      8  73.41719 71.39892  75.43546      1
#> 6      9  73.16389 71.16504  75.16275      1
#> 7     10  72.91059 70.93076  74.89042      1
#> 8     11  72.65729 70.69608  74.61850      1
#> 9     12  72.40399 70.46098  74.34700      1
#> 10    14  71.89738 69.98948  73.80529      1
#> # ... with 52 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)

plot() offers a few, but useful arguments, so it's easy to use.

With three variables, predictions can be grouped and faceted.

ggpredict(fit, terms = c("c12hour", "c172code", "c161sex"))
#> # A tibble: 372 × 7
#>        x predicted  conf.low conf.high                           group      facet
#>    <dbl>     <dbl>     <dbl>     <dbl>                          <fctr>     <fctr>
#> 1      4  74.70073  72.38031  77.02114 intermediate level of education [2] Female
#> 2      4  73.98237  70.45711  77.50763          low level of education [2] Female
#> 3      4  75.41908  71.91747  78.92070         high level of education [2] Female
#> 4      4  73.65930  70.08827  77.23033 intermediate level of education   [1] Male
#> 5      4  72.94094  68.38540  77.49649          low level of education   [1] Male
#> 6      4  74.37766  70.05658  78.69874         high level of education   [1] Male
#> 7      5  74.44742  72.14644  76.74841 intermediate level of education [2] Female
#> 8      5  73.72907  70.21926  77.23888          low level of education [2] Female
#> 9      5  75.16578  71.67430  78.65726         high level of education [2] Female
#> 10     5  73.40600  69.84575  76.96625 intermediate level of education   [1] Male
#> # ... with 362 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.

The package is easily extendable, to add support for other model objects. The only requirement is that following methods are available: predict(), model.frame() and family(). If model objects do not support these methods, you may implement workarounds (see below).

Following code needs to be revised to add further model objects:

• file utils_model_function.R, function get_model_function() needs a line to specify whether the new model can be considered as linear or generalized linear model.
• file utils_model_function.R, function get_predict_function() needs a line to specify the class.
• finally, in the file predictions.R, add a line to select_prediction_method() to call the right prediction-method, and add a method get_predictions_<class>(), if one of the existing prediction-methods does not fit the needs of the new model object.

When the model object does not support one of predict(), model.frame() or family(), you may add workarounds:

• if the model does not have a family()-function, a workaround has to be added to get_glm_family() in the file utils_model_family.R.
• if the model does not have a model.frame()-function with standard arguments or return values, a workaround has to be added to get_model_frame() in the file utils_model_frame.R.
• if the model does not have a predict()-function, a workaround has to be added to get_predictions_<class>() in the file predictions.R.

To install the latest development snapshot (see latest changes below), type following commands into the R console:

library(devtools)
devtools::install_github("strengejacke/ggeffects")

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:

sjlabelled → sjmisc → sjstats → ggeffects → sjPlot

To install the latest stable release from CRAN, type following command into the R console:

install.packages("ggeffects")

In case you want / have to cite my package, please use citation('ggeffects') for citation information.