How to Interpret Statistical Models Using marginaleffects for R and Python

Datos Bibliográficos

ID23367277
AutoresVincent Arel-Bundock (0000-0003-2042-7063, Université de Montréal), Noah Greifer (0000-0003-3067-7154, Harvard University Press), Andrew Hei (0000-0002-3948-3914, Georgia State University), Andrew Heiss
Año2024
Volumen111
Número9
Fecha de publicación2024-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Statistical Software (JOURNAL)
Identificadores de la revistaISSN: 1548-7660 • E-ISSN: 1548-7660
EditorialFoundation for Open Access Statistic (PUBLISHER)
DOI10.18637/jss.v111.i09
OpenAlexW4404880964
IdiomaEN
Citas recibidas92

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.

Programming language · Python (programming language) · Computational Physics and Python Applications · Computer Science