
Function reference
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as.data.frame(<ggeffects>)
ggeffect()
ggemmeans()
ggpredict()
- Marginal effects, adjusted predictions and estimated marginal means from regression models
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pool_predictions()
- Pool Predictions or Estimated Marginal Means
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johnson_neyman()
spotlight_analysis()
plot(<ggjohnson_neyman>)
- Spotlight-analysis: Create Johnson-Neyman confidence intervals and plots
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hypothesis_test()
- (Pairwise) comparisons between predictions
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plot(<ggeffects>)
theme_ggeffects()
show_pals()
- Plot ggeffects-objects
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collapse_by_group()
- Collapse raw data by random effect groups
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new_data()
data_grid()
- Create a data frame from all combinations of predictor values
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pretty_range()
- Create a pretty sequence over a range of a vector
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residualize_over_grid()
- Compute partial residuals from a data grid
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values_at()
representative_values()
- Calculate representative values of a vector
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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
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install_latest()
- Update latest ggeffects-version from R-universe (GitHub) or CRAN
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vcov(<ggeffects>)
- Calculate variance-covariance matrix for marginal effects