Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests
Comparisons Between Predictions Made With Different Regressor Values
Create a data.frame with all factor or character levels
Data grids
tinytest helper
tinytest helper
tinytest helper
Get a named vector of coefficients from a model object
Download and Read Datasets from marginaleffects or Rdatasets
Extract Posterior Draws or Bootstrap Resamples from marginaleffects ...
Get levels of the outcome variable in grouped or multivariate models
Get a named model matrix
Get predicted values from a model object (internal function)
Take a summary() style vcov argument and convert it to `insight::g...
Get a named variance-covariance matrix from a model object
(Non-)Linear Tests for Null Hypotheses, Joint Hypotheses, Equivalence,...
(EXPERIMENTAL) Bootstrap, Conformal, and Simulation-Based Inference
Print a marginaleffects object in knitr
Plot Conditional or Marginal Comparisons
Plot Conditional or Marginal Predictions
Plot Conditional or Marginal Slopes
alias to get_draws() keep forever for backward compatibility with JS...
Predictions
Print marginaleffects objects
Objects exported from other packages
Method to raise model-specific warnings and errors
Internal function to set coefficients
Slopes (aka Partial derivatives, Marginal Effects, or Trends)
Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.
Useful links