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: `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`

, `clm2`

, `zeroinfl`

, `hurdle`

, `stanreg`

, `brmsfit`

, `lmRob`

, `glmRob`

, `brglm`

, `rlm`

, `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 `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. `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")
#> # Predicted values for Total score BARTHEL INDEX
#> # x = average number of hours of care per week
#>
#> x predicted std.error conf.low conf.high
#> 0 75.444 1.116 73.257 77.630
#> 5 74.177 1.061 72.098 76.256
#> 10 72.911 1.010 70.931 74.890
#> 15 71.644 0.965 69.753 73.535
#> 20 70.378 0.925 68.564 72.191
#> 25 69.111 0.893 67.361 70.861
#> 30 67.845 0.868 66.144 69.545
#> 35 66.578 0.851 64.911 68.245
#> 40 65.312 0.842 63.661 66.962
#> 45 64.045 0.843 62.393 65.697
#> ... and 25 more rows.
#>
#> Adjusted for:
#> * neg_c_7 = 11.84
#> * c161sex = 1.76
#> * c172code = 1.97
```

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
#>
#> # low level of education
#> # [1] Male
#> x predicted std.error conf.low conf.high
#> 0 73.954 2.347 69.354 78.554
#> 5 72.688 2.225 68.143 77.233
#> 10 71.421 2.221 66.925 75.917
#> 15 70.155 2.337 65.702 74.607
#> ... and 31 more rows.
#>
#> # low level of education
#> # [2] Female
#> x predicted std.error conf.low conf.high
#> 0 74.996 2.319 71.406 78.585
#> 5 73.729 2.216 70.219 77.239
#> 10 72.463 2.233 69.026 75.899
#> 15 71.196 2.367 67.826 74.566
#> ... and 31 more rows.
#>
#> # intermediate level of education
#> # [1] Male
#> x predicted std.error conf.low conf.high
#> 0 74.673 2.294 71.055 78.290
#> 5 73.406 2.210 69.846 76.966
#> 10 72.139 2.247 68.629 75.650
#> 15 70.873 2.399 67.404 74.342
#> ... and 31 more rows.
#>
#> # intermediate level of education
#> # [2] Female
#> x predicted std.error conf.low conf.high
#> 0 75.714 2.272 73.313 78.115
#> 5 74.447 2.208 72.146 76.748
#> 10 73.181 2.265 70.972 75.390
#> 15 71.914 2.435 69.787 74.041
#> ... and 31 more rows.
#>
#> # high level of education
#> # [1] Male
#> x predicted std.error conf.low conf.high
#> 0 75.391 2.253 71.040 79.741
#> 5 74.124 2.209 69.810 78.439
#> 10 72.858 2.286 68.573 77.143
#> 15 71.591 2.472 67.330 75.853
#> ... and 31 more rows.
#>
#> # high level of education
#> # [2] Female
#> x predicted std.error conf.low conf.high
#> 0 76.432 2.237 72.887 79.977
#> 5 75.166 2.213 71.674 78.657
#> 10 73.899 2.310 70.454 77.345
#> 15 72.633 2.513 69.226 76.040
#> ... and 31 more rows.
#>
#> Adjusted for:
#> * neg_c_7 = 11.84
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:

`plot(mydf)`

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:

```
library(githubinstall)
githubinstall::githubinstall("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

- Download from CRAN at

https://cloud.r-project.org/package=ggeffects - Report a bug at

https://github.com/strengejacke/ggeffects/issues