Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
Collapse raw data by random effect groups
Get titles and labels from data
Adjusted predictions from regression models
Update latest ggeffects-version from R-universe (GitHub) or CRAN
Spotlight-analysis: Create Johnson-Neyman confidence intervals and plo...
Create a data frame from all combinations of predictor values
Plot ggeffects-objects
Pool contrasts and comparisons from test_predictions()
Pool Predictions or Estimated Marginal Means
Adjusted predictions and estimated marginal means from regression mode...
Create a pretty sequence over a range of a vector
Print and format ggeffects-objects
Objects exported from other packages
Compute partial residuals from a data grid
(Pairwise) comparisons between predictions (marginal effects)
Calculate representative values of a vector
Calculate variance-covariance matrix for adjusted predictions
Compute marginal effects and adjusted predictions from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Effects and predictions 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'.
Useful links