marginaleffects
package for R
Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds ratios, etc.) for over 70 classes of statistical models in R. Conduct linear and non-linear hypothesis tests, as well as equivalence tests using the delta method.
The website includes a “Get started” tutorial and over 25 vignettes, case studies, and technical notes.
Install the latest CRAN release:
install.packages("marginaleffects")
Install the development version:
install.packages(
c("marginaleffects", "insight"),
repos = c("https://vincentarelbundock.r-universe.dev", "https://easystats.r-universe.dev"))
Restart R
completely before moving on.
Parameter estimates are often hard to interpret substantively,
especially when they are generated by complex models with non-linear
components or transformations. Many applied researchers would rather
focus on simple quantities of interest, which have straightforward
scientific interpretations. Unfortunately, these estimands (and their
standard errors) are tedious to compute. Moreover, the different
modeling packages in R
often produce inconsistent objects
that require special treatment.
marginaleffects
offers a single point of entry to easily
interpret the results of over 73 classes of models, using a simple and
consistent user interface.
Benefits of marginaleffects
include:
R
.margins
package, and the memory footprint is much
smaller.Stata
or other R
packages.marginaleffects
follows
“tidy” principles and returns objects that work with standard functions
like plot
, summary()
, tidy()
, and
glance()
. These objects are easy to program with and feed
to other
packages like modelsummary
.The marginaleffects
package allows R
users
to compute and plot three principal quantities of interest: (1)
predictions, (2) comparisons, and (3) slopes. In addition, the package
includes a convenience function to compute a fourth estimand, “marginal
means”, which is a special case of averaged predictions.
marginaleffects
can also average (or “marginalize”)
unit-level (or “conditional”) estimates of all those quantities, and
conduct hypothesis tests on them.
The outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or factor levels. a.k.a. Fitted values, adjusted predictions.
predictions()
,avg_predictions()
,plot_predictions()
.
Compare the predictions made by a model for different regressor values (e.g., college graduates vs. others): contrasts, differences, risk ratios, odds, etc.
comparisons()
,avg_comparisons()
,plot_comparisons()
.
Partial derivative of the regression equation with respect to a regressor of interest. a.k.a. Marginal effects, trends.
slopes()
,avg_slopes()
,plot_slopes()
.
Predictions of a model, averaged across a “reference grid” of categorical predictors.
marginalmeans()
.
Goal |
Function |
---|---|
Predictions |
|
Comparisons |
|
Slopes |
|
Marginal Means |
|
Grids |
|
Hypothesis & Equivalence |
|
Bayes, Bootstrap, Simulation |
|