ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs

Compute 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. 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'.

Version: 0.5.0
Depends: R (≥ 3.2), graphics, stats, utils
Imports: dplyr, effects (≥ 4.0-0), ggplot2, magrittr, prediction, purrr, rlang, scales, sjlabelled (≥ 1.0.12), sjmisc (≥ 2.7.3), sjstats (≥ 0.16.0), tibble, tidyr, tidyselect
Suggests: glmmTMB, knitr, lme4, MASS, nlme, rmarkdown, rstantools, survey, testthat
Published: 2018-08-11
Author: Daniel Lüdecke ORCID iD [aut, cre]
Maintainer: Daniel Lüdecke <d.luedecke at>
License: GPL-3
NeedsCompilation: no
Citation: ggeffects citation info
Materials: README NEWS
CRAN checks: ggeffects results


Reference manual: ggeffects.pdf
Vignettes: Marginal Effects at Specific Values
Tidy Data Frames of Marginal Effects
Plotting Marginal Effects
Different Output between Stata and ggeffects
Package source: ggeffects_0.5.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: ggeffects_0.5.0.tgz, r-oldrel: ggeffects_0.5.0.tgz
Old sources: ggeffects archive

Reverse dependencies:

Reverse imports: sjPlot


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