Lüdecke D (2018). *ggeffects: Tidy Data Frames of Marginal Effects from Regression Models.* Journal of Open Source Software, 3(26), 772. doi: 10.21105/joss.00772

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* or *adjusted predictions* are far easier to understand. In particular, the visualization of such effects or predictions allows to intuitively get the idea of how predictors and outcome are associated, even for complex models.

**ggeffects** is a light-weight package that aims at easily calculating marginal effects and adjusted predictions (or: *estimated marginal means*) at the mean or at representative values of covariates (see definitions here) from statistical models, i.e. **predictions generated by a model when one holds the non-focal variables constant and varies the focal variable(s)**. Furthermore, it is possible to compute contrasts or pairwise comparisons, to test predictions and differences in predictions for statistical significance.

This is achieved by three core ideas that describe the philosophy of the function design:

Functions are type-safe and always return a data frame with the same, consistent structure;

there is a simple, unique approach to calculate marginal effects/adjusted predictions and estimated marginal means for many different models;

the package supports “labelled data” (Lüdecke 2018), which allows human readable annotations for graphical outputs.

This means, users do not need to care about any expensive steps after modeling to visualize the results. The returned as data frame is ready to use with the **ggplot2**-package, however, there is also a `plot()`

-method to easily create publication-ready figures.

Type | Source | Command |
---|---|---|

Release | CRAN | `install.packages("ggeffects")` |

Development | r - universe | `install.packages("ggeffects", repos = "https://strengejacke.r-universe.dev")` |

Development | GitHub | `remotes::install_github("strengejacke/ggeffects")` |

Or you can run `ggeffects::install_latest()`

to install the latest development version from r-universe.

There is no common language across fields regarding a unique meaning of “marginal effects”. Thus, the wording throughout this package may vary. Maybe “adjusted predictions” comes closest to what **ggeffects** actually does. To avoid confusion about what is actually calculated and returned by the package’s functions `ggpredict()`

, `ggemmeans()`

and `ggeffect()`

, it is recommended to read this vignette about the different terminology and its meanings.

Please visit https://strengejacke.github.io/ggeffects/ for documentation and vignettes. For questions about the functionality, you may either contact me via email or also file an issue.

Marginal effects and adjusted predictions can be calculated for many different models. Currently supported model-objects are: ‘averaging’, ‘bamlss’, ‘bayesx’, ‘betabin’, ‘betareg’, ‘bglmer’, ‘bigglm’, ‘biglm’, ‘blmer’, ‘bracl’, ‘brglm’, ‘brmsfit’, ‘brmultinom’, ‘cgam’, ‘cgamm’, ‘clm’, ‘clm2’, ‘clmm’, ‘coxph’, ‘feglm’, ‘fixest’, ‘flac’, ‘flic’, ‘gam’, ‘Gam’, ‘gamlss’, ‘gamm’, ‘gamm4’, ‘gee’, ‘geeglm’, ‘glimML’, ‘glm’, ‘glm.nb’, ‘glmer.nb’, ‘glmerMod’, ‘glmmPQL’, ‘glmmTMB’, ‘glmrob’, ‘glmRob’, ‘glmx’, ‘gls’, ‘hurdle’, ‘ivreg’, ‘lm’, ‘lm_robust’, ‘lme’, ‘lmerMod’, ‘lmrob’, ‘lmRob’, ‘logistf’, ‘logitr’, ‘lrm’, ‘mblogit’, ‘mclogit’, ‘MCMCglmm’, ‘merModLmerTest’, ‘MixMod’, ‘mixor’, ‘mlogit’, ‘multinom’, ‘negbin’, ‘nestedLogit’, ‘nlmerMod’, ‘ols’, ‘orm’, ‘phyloglm’, ‘phylolm’, ‘plm’, ‘polr’, ‘rlm’, ‘rlmerMod’, ‘rq’, ‘rqs’, ‘rqss’, ‘sdmTMB’, ‘speedglm’, ‘speedlm’, ‘stanreg’, ‘survreg’, ‘svyglm’, ‘svyglm.nb’, ‘tidymodels’, ‘tobit’, ‘truncreg’, ‘vgam’, ‘vglm’, ‘wblm’, ‘wbm’, ‘Zelig-relogit’, ‘zeroinfl’ and ‘zerotrunc’.

Support for models varies by function, i.e. although `ggpredict()`

, `ggemmeans()`

and `ggeffect()`

support most models, some models are only supported exclusively by one of the three functions. Other models not listed here might work as well, but are currently not tested.

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**.

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 four terms specified in the `terms`

-argument. Predicted values of the response, along the values of the first term are calculated, optionally grouped by the other terms specified in `terms`

.

```
library(ggeffects)
library(splines)
data(efc)
fit <- lm(barthtot ~ c12hour + bs(neg_c_7) * c161sex + e42dep, data = efc)
ggpredict(fit, terms = "c12hour")
#> # Predicted values of barthtot
#>
#> c12hour | Predicted | 95% CI
#> ----------------------------------
#> 4 | 67.89 | 65.81, 69.96
#> 12 | 67.07 | 65.10, 69.05
#> 22 | 66.06 | 64.18, 67.94
#> 36 | 64.64 | 62.84, 66.45
#> 49 | 63.32 | 61.51, 65.14
#> 70 | 61.20 | 59.22, 63.17
#> 100 | 58.15 | 55.70, 60.60
#> 168 | 51.26 | 47.27, 55.26
#>
#> Adjusted for:
#> * neg_c_7 = 11.83
#> * c161sex = 1.76
#> * e42dep = 2.93
```

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 = 0.1)
```

However, there is also a `plot()`

-method. This method uses convenient defaults, to easily create the most suitable plot for the marginal effects.

With three variables, predictions can be grouped and faceted.

```
ggpredict(fit, terms = c("neg_c_7", "c161sex", "e42dep"))
#> # Predicted values of barthtot
#>
#> c161sex: 1
#> e42dep: 1
#>
#> neg_c_7 | Predicted | 95% CI
#> -----------------------------------
#> 7 | 102.74 | 95.96, 109.52
#> 12 | 102.27 | 97.10, 107.44
#> 17 | 93.79 | 86.95, 100.64
#> 28 | 164.57 | 95.88, 233.27
#>
#> c161sex: 1
#> e42dep: 2
#>
#> neg_c_7 | Predicted | 95% CI
#> -----------------------------------
#> 7 | 83.73 | 77.31, 90.15
#> 12 | 83.26 | 78.94, 87.59
#> 17 | 74.79 | 68.67, 80.90
#> 28 | 145.57 | 76.90, 214.23
#>
#> c161sex: 1
#> e42dep: 3
#>
#> neg_c_7 | Predicted | 95% CI
#> -----------------------------------
#> 7 | 64.72 | 58.27, 71.17
#> 12 | 64.26 | 60.29, 68.22
#> 17 | 55.78 | 50.03, 61.53
#> 28 | 126.56 | 57.88, 195.24
#>
#> c161sex: 1
#> e42dep: 4
#>
#> neg_c_7 | Predicted | 95% CI
#> -----------------------------------
#> 7 | 45.72 | 38.85, 52.58
#> 12 | 45.25 | 41.02, 49.48
#> 17 | 36.77 | 30.96, 42.59
#> 28 | 107.55 | 38.83, 176.28
#>
#> c161sex: 2
#> e42dep: 1
#>
#> neg_c_7 | Predicted | 95% CI
#> ------------------------------------
#> 7 | 109.54 | 105.19, 113.88
#> 12 | 99.81 | 95.94, 103.68
#> 17 | 94.90 | 90.20, 99.60
#> 28 | 90.26 | 71.77, 108.76
#>
#> c161sex: 2
#> e42dep: 2
#>
#> neg_c_7 | Predicted | 95% CI
#> -----------------------------------
#> 7 | 90.53 | 86.71, 94.35
#> 12 | 80.80 | 78.16, 83.44
#> 17 | 75.90 | 72.28, 79.51
#> 28 | 71.26 | 53.04, 89.47
#>
#> c161sex: 2
#> e42dep: 3
#>
#> neg_c_7 | Predicted | 95% CI
#> -----------------------------------
#> 7 | 71.52 | 67.59, 75.46
#> 12 | 61.79 | 59.78, 63.80
#> 17 | 56.89 | 53.86, 59.92
#> 28 | 52.25 | 34.18, 70.32
#>
#> c161sex: 2
#> e42dep: 4
#>
#> neg_c_7 | Predicted | 95% CI
#> -----------------------------------
#> 7 | 52.51 | 47.88, 57.15
#> 12 | 42.79 | 40.29, 45.29
#> 17 | 37.88 | 34.66, 41.11
#> 28 | 33.24 | 15.18, 51.31
#>
#> Adjusted for:
#> * c12hour = 42.10
mydf <- ggpredict(fit, terms = c("neg_c_7", "c161sex", "e42dep"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
geom_line() +
facet_wrap(~facet)
```

`plot()`

works for this case, as well:

Next, an example of an interaction term. We want to know whether the two slopes are significantly different from each other.

```
fit <- lm(neg_c_7 ~ c12hour + barthtot * c161sex + e42dep, data = efc)
result <- ggpredict(fit, c("barthtot", "c161sex"))
plot(result)
```

This can be achieved by `hypothesis_test()`

.

```
hypothesis_test(result)
#> # Linear trend for barthtot
#>
#> c161sex | Contrast | 95% CI | p
#> ----------------------------------------
#> 1-2 | 7.09e-03 | -0.01, 0.03 | 0.464
```

We can conclude that slopes (or “linear trends”) of `barthtot`

for the different groups of `c161sex`

are not statistically significantly different from each other.

More features are explained in detail in the package-vignettes.

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

for citation information:

Lüdecke D (2018). *ggeffects: Tidy Data Frames of Marginal Effects from Regression Models.* Journal of Open Source Software, 3(26), 772. doi: 10.21105/joss.00772

Lüdecke, Daniel. 2018. “Sjlabelled: Labelled Data Utility Functions,” May. https://doi.org/10.5281/zenodo.1249215.