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Adjusted Predictions and Marginal Means for Regression Models

predict_response()
Adjusted predictions and estimated marginal means from regression models
as.data.frame(<ggeffects>) ggaverage() ggeffect() ggemmeans() ggpredict()
Adjusted predictions from regression models
pool_predictions()
Pool Predictions or Estimated Marginal Means

Pairwise Comparisons, Contrasts and Marginal Effects

johnson_neyman() spotlight_analysis() plot(<ggjohnson_neyman>)
Spotlight-analysis: Create Johnson-Neyman confidence intervals and plots
test_predictions() hypothesis_test()
(Pairwise) comparisons between predictions (marginal effects)
pool_comparisons()
Pool contrasts and comparisons from test_predictions()

Plotting Adjusted Predictions

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

Printing and Formatting Output

format(<ggeffects>) print(<ggeffects>) print_md(<ggeffects>) print_html(<ggeffects>)
Print and format ggeffects-objects
reexports print_html print_md
Objects exported from other packages

Plot Annotation from Labelled Data

Utilities

install_latest()
Update latest ggeffects-version from R-universe (GitHub) or CRAN
vcov(<ggeffects>)
Calculate variance-covariance matrix for adjusted predictions

Sample Data Sets

efc efc_test
Sample dataset from the EUROFAMCARE project
fish
Sample data set
lung2
Sample data set