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

Compute 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 main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.

Version: 0.11.0
Depends: R (≥ 3.2), graphics, stats, utils
Imports: dplyr, insight (≥ 0.4.0), magrittr, MASS, purrr, rlang, scales, sjlabelled (≥ 1.1.0), sjmisc (≥ 2.8.0)
Suggests: AER, aod, betareg, brms, effects (≥ 4.0-0), emmeans, gam, gamm4, gee, geepack, ggplot2, GLMMadaptive, glmmTMB, httr, knitr, lme4, logistf, Matrix, MCMCglmm, mgcv, nlme, ordinal, prediction, pscl, quantreg, rmarkdown, rms, robust, robustbase, rstanarm, rstantools, sandwich, see, sjstats, survey, survival, testthat, VGAM
Published: 2019-07-01
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: ggeffects: Marginal Effects of Regression Models
Introduction: Marginal Effects at Specific Values
Introduction: Customize Plot Appearance
Introduction: Plotting Marginal Effects
Introduction: Marginal Effects for Random Effects Models
Practical example: Logistic Mixed Effects Model with Interaction Term
Technical Details: Difference between ggpredict() and ggemmeans()
Technical Details: Different Output between Stata and ggeffects
Package source: ggeffects_0.11.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: ggeffects_0.11.0.tgz, r-oldrel: ggeffects_0.11.0.tgz
Old sources: ggeffects archive

Reverse dependencies:

Reverse imports: sjPlot


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