Function reference • ggeffects

Adjusted Predictions for Regression Models

as.data.frame(<ggeffects>) ggeffect() ggemmeans() ggpredict(): Marginal effects, adjusted predictions and estimated marginal means from regression models

pool_predictions(): Pool Predictions or Estimated Marginal Means

Pairwise Comparisons and Contrasts

johnson_neyman() spotlight_analysis() plot(<ggjohnson_neyman>): Spotlight-analysis: Create Johnson-Neyman confidence intervals and plots

hypothesis_test(): (Pairwise) comparisons between predictions

Plotting Adjusted Predictions

plot(<ggeffects>) theme_ggeffects() show_pals(): Plot ggeffects-objects

Data Grids and Meaningful Values

collapse_by_group(): Collapse raw data by random effect groups

new_data() data_grid(): Create a data frame from all combinations of predictor values

pretty_range(): Create a pretty sequence over a range of a vector

residualize_over_grid(): Compute partial residuals from a data grid

values_at() representative_values(): Calculate representative values of a vector

Plot Annotation from Labelled Data

get_title() get_x_title() get_y_title() get_legend_title() get_legend_labels() get_x_labels() get_complete_df(): Get titles and labels from data

Utilities

install_latest(): Update latest ggeffects-version from R-universe (GitHub) or CRAN

vcov(<ggeffects>): Calculate variance-covariance matrix for marginal effects

Sample Data Sets

efc efc_test: Sample dataset from the EUROFAMCARE project

fish: Sample data set

lung2: Sample data set