The parameters of a statistical model can sometimes be difficult to interpret substantively, especially when that model includes non-linear 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, such as predictions, contrasts, risk differences, ratios, odds, lift, slopes, and so on.
Unfortunately, computing these quantities—along with associated standard
errors—can be a tedious and error-prone task. This problem is compounded
by the fact that modeling packages in R
and Python
produce objects
with varied structures, which hold different information. This means
that end-users often have to write customized code to interpret the
estimates obtained by fitting Linear, GLM, GAM, Bayesian, Mixed Effects,
and other model types. This can lead to wasted effort, confusion, and
mistakes, and it can hinder the implementation of best practices.
This free online book introduces a
conceptual framework to clearly define statistical quantities of
interest, and shows how to estimate those quantities using the
marginaleffects
package for R
and Python
. The techniques
introduced herein can enhance the interpretability of over 100 classes
of statistical and machine learning
models,
including linear, GLM, GAM, mixed-effects, bayesian, categorical
outcomes, XGBoost, and more. With a single unified interface, users can
compute and plot many estimands, including:
The Marginal Effects Zoo book includes
over 30 chapters of tutorials, case studies, and technical notes. It
covers a wide range of topics, including how the marginaleffects
package can facilitate the analysis of:
The marginaleffects
package for R
and Python
offers a single point
of entry to easily interpret the results of over 100 classes of
models,
using a simple and consistent user interface. Its benefits include:
R
.margins
package. Stata
or other R
packages.R
package requires relatively few dependencies.marginaleffects
follows “tidy” principles
and returns simple data frames that work with all standard R
functions. The outputs are easy to program with and feed to other
packages like
ggplot2
or
modelsummary
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