How to Interpret Statistical Models Using marginaleffects for R and Python
Dublin Core
Title
How to Interpret Statistical Models Using marginaleffects for R and Python
Subject
marginal effect, marginal mean, slope, prediction, fitted value, contrast, comparison, R, Python.
Description
The parameters of a statistical model can sometimes be difficult to interpret substantively, especially when that model includes nonlinear components, interactions, or
transformations. Analysts who fit such complex models often seek to transform raw parameter estimates into quantities that are easier for domain experts and stakeholders to
understand. This article presents a simple conceptual framework to describe a vast array
of such quantities of interest, which are reported under imprecise and inconsistent terminology across disciplines: predictions, marginal predictions, marginal means, marginal
effects, conditional effects, slopes, contrasts, risk ratios, etc. We introduce marginaleffects,
a package for R and Python which offers a simple and powerful interface to compute all of
those quantities, and to conduct (non-)linear hypothesis and equivalence tests on them.
marginaleffects is lightweight; extensible; it works well in combination with other R and
Python packages; and it supports over 100 classes of models, including linear, generalized
linear, generalized additive, mixed effects, Bayesian, and several machine learning models.
transformations. Analysts who fit such complex models often seek to transform raw parameter estimates into quantities that are easier for domain experts and stakeholders to
understand. This article presents a simple conceptual framework to describe a vast array
of such quantities of interest, which are reported under imprecise and inconsistent terminology across disciplines: predictions, marginal predictions, marginal means, marginal
effects, conditional effects, slopes, contrasts, risk ratios, etc. We introduce marginaleffects,
a package for R and Python which offers a simple and powerful interface to compute all of
those quantities, and to conduct (non-)linear hypothesis and equivalence tests on them.
marginaleffects is lightweight; extensible; it works well in combination with other R and
Python packages; and it supports over 100 classes of models, including linear, generalized
linear, generalized additive, mixed effects, Bayesian, and several machine learning models.
Creator
Vincent Arel-Bundock
Source
https://www.jstatsoft.org/article/view/v111i09
Publisher
Université de Montréal
Date
November 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Vincent Arel-Bundock, “How to Interpret Statistical Models Using marginaleffects for R and Python,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8353.