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ggeffects

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

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'.

Functions (13)

collapse_by_group

Collapse raw data by random effect groups

efc

Sample dataset from the EUROFAMCARE project

fish

Sample data set

get_title

Get titles and labels from data

ggpredict

Marginal effects, adjusted predictions and estimated marginal means from regression models

lung2

Sample data set

new_data

Create a data frame from all combinations of predictor values

plot

Plot ggeffects-objects

pool_predictions

Pool Predictions or Estimated Marginal Means

pretty_range

Create a pretty sequence over a range of a vector

residualize_over_grid

Compute partial residuals from a data grid

values_at

Calculate representative values of a vector

vcov

Calculate variance-covariance matrix for marginal effects

ggeffects

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

v1.1.0

GPL-3

Authors

Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>), Frederik Aust [ctb] (<https://orcid.org/0000-0003-4900-788X>), Sam Crawley [ctb] (<https://orcid.org/0000-0002-7847-0411>), Mattan S. Ben-Shachar [ctb] (<https://orcid.org/0000-0002-4287-4801>)

Initial release

ggeffects

Functions (13)

ggeffects

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