ggeffects - Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
Why do we need marginal effects?
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 makes it possible to intuitively get the idea of how predictors and outcome are associated, even for complex models.
Aim of this package
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.
Documentation and Support
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.
ggeffects supports many different models and is easy to use
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, 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 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.
Examples
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 rowsA 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:
plot(mydf)There are some more features, which are explained in more detail in the package-vignette.
Contributing to the package
Please follow this guide if you like to contribute to this package.
Installation
Latest development build
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
Officiale, stable release
To install the latest stable release from CRAN, type following command into the R console:
install.packages("ggeffects")Citation
In case you want / have to cite my package, please use citation('ggeffects') for citation information.