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`

(package *mgcv*), `vgam`

, `gamm`

, `gamm4`

, `multinom`

, `betareg`

, `truncreg`

, `coxph`

, `gls`

, `gee`

, `plm`

, `lrm`

, `polr`

, `clm`

, `zeroinfl`

, `hurdle`

, `stanreg`

, `brmsfit`

, `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.