Why 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 allows 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, 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.

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")

#> # 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.

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(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:

sjlabelledsjmiscsjstatsggeffectssjPlot

Officiale, stable release

CRAN_Status_Badge    downloads    total

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.

DOI