Package: ggeffects 1.8.19

Daniel Lüdecke

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'.

Authors:Daniel Lüdecke [aut, cre], Frederik Aust [ctb], Sam Crawley [ctb], Mattan S. Ben-Shachar [ctb], Sean C. Anderson [ctb]

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ggeffects/json (API)
NEWS

# Install 'ggeffects' in R:
install.packages('ggeffects', repos = c('https://strengejacke.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/strengejacke/ggeffects/issues

Datasets:
  • coffee_data - Sample dataset from a course about analysis of factorial designs
  • efc - Sample dataset from the EUROFAMCARE project
  • efc_test - Sample dataset from the EUROFAMCARE project
  • fish - Sample data set
  • lung2 - Sample data set

On CRAN:

estimated-marginal-meanshacktoberfestmarginal-effectsprediction

15.40 score 559 stars 6 packages 3.1k scripts 37k downloads 41 mentions 32 exports 2 dependencies

Last updated 23 hours agofrom:03d6d4348e. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-winNOTENov 22 2024
R-4.5-linuxNOTENov 22 2024
R-4.4-winNOTENov 22 2024
R-4.4-macNOTENov 22 2024
R-4.3-winNOTENov 22 2024
R-4.3-macNOTENov 22 2024

Exports:collapse_by_groupdata_gridget_complete_dfget_legend_labelsget_legend_titleget_predictionsget_titleget_x_labelsget_x_titleget_y_titleggaverageggeffectggeffects_paletteggemmeansggpredicthypothesis_testinstall_latestjohnson_neymannew_datapool_comparisonspool_predictionspredict_responsepretty_rangeprint_htmlprint_mdrepresentative_valuesresidualize_over_gridshow_palettesspotlight_analysistest_predictionstheme_ggeffectsvalues_at

Dependencies:datawizardinsight

Documentation of the ggeffects package

Rendered fromcontent.Rmdusingknitr::rmarkdownon Nov 22 2024.

Last update: 2024-11-12
Started: 2021-07-29

Readme and manuals

Help Manual

Help pageTopics
Adjusted predictions from regression modelsas.data.frame.ggeffects ggaverage ggeffect ggemmeans ggpredict
Sample dataset from a course about analysis of factorial designscoffee_data
Collapse raw data by random effect groupscollapse_by_group
Sample dataset from the EUROFAMCARE projectefc efc_test
Sample data setfish
Print and format ggeffects-objectsformat.ggcomparisons format.ggeffects print print.ggcomparisons print.ggeffects print_html.ggcomparisons print_html.ggeffects print_md.ggcomparisons print_md.ggeffects
S3-class definition for the ggeffects packageget_predictions get_predictions.default
Get titles and labels from dataget_complete_df get_legend_labels get_legend_title get_title get_x_labels get_x_title get_y_title
Update latest ggeffects-version from R-universe (GitHub) or CRANinstall_latest
Spotlight-analysis: Create Johnson-Neyman confidence intervals and plotsjohnson_neyman plot.ggjohnson_neyman spotlight_analysis
Sample data setlung2
Create a data frame from all combinations of predictor valuesdata_grid new_data
Plot ggeffects-objectsggeffects_palette plot plot.ggeffects show_palettes theme_ggeffects
Pool contrasts and comparisons from 'test_predictions()'pool_comparisons
Pool Predictions or Estimated Marginal Meanspool_predictions
Adjusted predictions and estimated marginal means from regression modelspredict_response
Create a pretty sequence over a range of a vectorpretty_range
Compute partial residuals from a data gridresidualize_over_grid residualize_over_grid.data.frame residualize_over_grid.ggeffects
(Pairwise) comparisons between predictions (marginal effects)hypothesis_test test_predictions test_predictions.default test_predictions.ggeffects
Calculate representative values of a vectorrepresentative_values values_at
Calculate variance-covariance matrix for adjusted predictionsvcov vcov.ggeffects